---
name: snappy-course
role: Orchestrator for the Skool classroom portfolio at skool.com/snappy. Tracks the content map, enforces cross-link and voice rules, coordinates draft-to-ship pipeline, and owns all browser automation for Skool lesson/course management.
loaded-by: PreToolUse hook (auto-injected when "snappy-course" is mentioned)
---

# snappy-course -- Agent Loader

Orchestrator for the **Skool classroom portfolio** at skool.com/snappy. The classroom is the top-of-funnel for snappy.ai -- it is a growing portfolio of courses mined from the corpus (not a single course). This skill ties together five surfaces: Skool classroom (many courses, each with modules + lessons), course Slack workspace (T0AQPFMQEKB / snappyclaudec-qdk8925.slack.com), blog mirrors at snappy.ai/blog, newsletter at snappy.ai/newsletter, and Skool DM outreach. It does not write lesson copy or post directly -- it tracks the content map, enforces cross-link rules, and coordinates the skills that do the actual work. Write as a teacher, not a performer. Downstream reuse to other surfaces happens separately and must NEVER influence the writing. Robert reviews, never auto-publish.

## API module

```typescript
import { getModules, getCourseModule, getSlackInfo } from "../snappy-course/api.ts";
```

Or CLI:
```bash
npx tsx ~/.claude/skills/snappy-course/api.ts modules
npx tsx ~/.claude/skills/snappy-course/api.ts module <id>
```

## API functions

| Function | Purpose |
|----------|---------|
| `getModules()` | Returns live modules and planned module titles |
| `getCourseModule(id)` | Get a specific module by ID |
| `getSlackInfo()` | Returns course Slack workspace info (team ID, channels) |

## Scripts (the primary operational interface)

| Script | Purpose | Notes |
|--------|---------|-------|
| `scripts/upload-lesson.sh` | Ship a draft to Skool end-to-end | Idempotent (skips if title exists). Handles missing images gracefully. Use `--force` to bypass dedup. **Preferred over manual DOM automation.** |
| `scripts/create-course.sh` | Create a new Skool course | Idempotent (skips if exact title exists). Returns course URL. |
| `scripts/lint-draft.sh` | Lint a draft against D1-D6 rules | Exit 0 = clean, exit 1 = violations. Run before claiming done. |
| `scripts/skool-browser-start.sh` | Start an authenticated Skool browser session | MANDATORY before any Skool browser work. Exits non-zero on failure. |
| `scripts/skool-auth-restore.sh` | Restore auth from backup | Recovery when auth gets corrupted. |
| `scripts/add-cover-image.sh` | Add or refresh a cover image on an existing Skool lesson (via draft frontmatter) | Reads `skool_url` + `image_url` from draft, downloads image, enters edit mode, removes any existing first image-bearing node via ProseMirror transaction, moves cursor to editor top, uploads at position 0, saves via structural-DOM-change (no title-dirty hack), verifies via navigate-away-and-back. Idempotent — works whether lesson has an existing cover or not. |
| `scripts/replace-cover.sh` | Swap an existing cover with a new image file on disk | Takes `<lesson-md-url> <new-image-path>` directly, same ProseMirror transaction pattern as add-cover-image. Use when you already have the image file and don't want the frontmatter round-trip. |
| `scripts/hoist-cover.sh` | Move an already-present buried body image to position 0 (cover banner) | Takes `<lesson-md-url>`. Does NOT change the image — only relocates it. Use when a lesson has the right image but it's wrapped in a `<p>` and not rendering as a cover. |
| `scripts/backfill-ship-state.sh` | Patch `skool_url`/`shipped_at` into draft files | Reads pass-log ship lines. For legacy ships only. |
| `scripts/prep-drafts.mjs` | Convert draft markdown to Skool-ready HTML | Emits `inline_images[{landmark,src}]` for multi-image lessons. |
| `scripts/classroom-check.sh` | Audit the live classroom tile surface against `data/classroom-spec.md` and emit a verification cert | **MANDATORY after any run that touches a course tile, cover, lesson push, or reorder.** Writes `~/.claude/logs/classroom-certs/<session>-<ts>.cert`. Rules C1–C7: every tile has a cover, covers are unique, no placeholder titles, aspect ratio ~1.94:1, count matches `data/classroom-roster.json`. A classroom-touching task is NOT done until this script returns PASS or PASS_WITH_WARNINGS on a fresh run. |

**Classroom enforcement (kernel rule, 2026-04-15):** the spec at `data/classroom-spec.md` and the check at `scripts/classroom-check.sh` are how the classroom surface stays honest. If an agent pushes a lesson, replaces a cover, creates a course, or reorders tiles, the run is not finished until `classroom-check.sh` writes a fresh PASS cert. The roster at `data/classroom-roster.json` is the canonical list of courses that must exist — update it intentionally when adding or removing a course, and the check's C7 rule will catch drift automatically. **This rule exists because every audit sweep prior to 2026-04-15 scoped itself to lesson covers inside a course and missed the missing course-tile cover on Build with Snappy. The check closes that blind spot.**

**Lesson cover scripts — decision tree:**
- Lesson has no cover, you have a draft with `image_url` in frontmatter → `add-cover-image.sh`
- Lesson has no cover (or wrong cover), you have an image file on disk → `replace-cover.sh`
- Lesson already has the right image but it's buried in the body → `hoist-cover.sh`

**Section/folder reorder pattern (Wave 7A 2026-04-13):** moving a lesson into a section uses Skool's "Change folder" modal, scriptable via:
- Lesson row: `[data-rbd-draggable-id="module-*"]` (sections are `set-*`)
- Row `…` menu: `<row> [class*=MenuItemDropdownMenu] button` → menu items are plain `<div>`s with text (`Change folder`, `Edit page`, etc)
- Modal: `[class*=ModalContent]`; folder picker = `[class*=DropdownButton]` inside it
- Folder dropdown items: `[data-testid^="dropdown-item-"]` — `dropdown-item-0` = "None", others = section titles **without** `(Draft)` prefix
- Save: button matched `/save/i` inside `[class*=ModalContent]`
- Timing: modal open ~600ms, dropdown ~500ms, save settle ~1200ms
- **Within-section order**: Skool appends moved lessons to the END of the destination section, so move lessons in the desired final sequence — no separate reorder pass needed.

**Within-section drag reorder (P-fix 2026-04-14):** if you must reorder AFTER the fact, `agent-browser drag` does NOT trigger Skool's `react-beautiful-dnd`. You need real mouse events: `agent-browser mouse move` to the row, `mouse down`, then several `move` steps (first move >5px to start the drag, subsequent ~45px increments), then `mouse up`. Drop zones land just AFTER the hovered row, so reaching idx=0 requires two passes — first drag to idx=1, then a second drag up to the top edge of the current idx=0 row.

**Cover-attach auth precheck (P-fix 2026-04-14):** `add-cover-image.sh` now fails fast with a clear message if the module title is not visible (logged-out / 404 / stale session). Before running the cover loop, start the session with `bash ~/.claude/skills/snappy-course/scripts/skool-browser-start.sh <session-name>`.

### Script usage

```bash
# Ship a draft (preferred path for all lesson shipping)
bash ~/.claude/skills/snappy-course/scripts/upload-lesson.sh \
  [--force] <path/to/draft.md> "<Course Title>" "<Short Title <=50 chars>"

# Create a new course
bash ~/.claude/skills/snappy-course/scripts/create-course.sh \
  --title "Course Title" --description "Optional one-liner"

# Lint before shipping
bash ~/.claude/skills/snappy-course/scripts/lint-draft.sh <path/to/draft.md>
bash ~/.claude/skills/snappy-course/scripts/lint-draft.sh --all
```

## Draft frontmatter contract

Every lesson draft at `data/drafts/<course>/<slug>.md` carries YAML frontmatter. These keys are the canonical contract -- sensor, lint, and ship scripts all read them.

| Key | When set | Purpose |
|-----|----------|---------|
| `title` | draft creation | Display title (Skool page title) |
| `course` | draft creation | Human course name |
| `slug` | draft creation | Filename-matching slug |
| `status` | draft creation | `draft` until shipped |
| `word_count` | after body write | Integer for sensor bucket |
| `source_files` | during mining | List of corpus paths used as grounding |
| `source_quotes` | during mining | Structured `speaker:/date:/quote:` objects -- D2 lint reads these. **Must be structured objects, NOT flat strings.** |
| `image_brief` | image planning | 6-layer Ink Journal brief |
| `image_cdn_url` | after image ship | CDN URL of uploaded lesson cover. **Note**: `add-cover-image.sh` reads `image_url` only — when generating covers, write BOTH `image_url:` and `image_cdn_url:` to the same value, otherwise the cover-attach pod will skip the lesson. |
| `image_generated_at` | after image gen | UTC timestamp |
| `skool_url` | **after Skool ship** | Full Skool lesson URL. **Presence = ground-truth that lesson shipped.** |
| `shipped_at` | **after Skool ship** | UTC timestamp. New ship pods SHOULD write both fields directly. |

## Lint rules (D1-D6)

The linter (`scripts/lint-draft.sh`) enforces these. All must pass before a draft ships.

| Rule | What it checks |
|------|----------------|
| **D1** | No `Mined from:` in body. Provenance lives in frontmatter only. |
| **D2** | Named corpus people in body must appear inside actual `quote:` text in `source_quotes:`. Speaker fields do NOT count as grounding. |
| **D3** | No banned phrases from snappy-positioning (unlocks, leverage, transform, supercharge, 10x, seamless, etc.) in body or title. |
| **D4** | "LinkedIn" is a hard fail in body or title. Classroom tone is teacher, never performer. |
| **D5** | Repetitive sentence openers capped at 2 per draft ("There is a", "There are", "The reason", "Here is", "This is", "The thing is"). |
| **D6** | No em dashes or en dashes. Dead AI tell. |

## Q1-Q8: Lesson Quality Rubric (substance gate, beyond D1-D6 mechanical lint)

D1-D6 is the mechanical floor. A lesson can pass every D rule and still be generic AI slop Robert would not put his name on. Q1-Q8 is the substance gate. Every lesson must score PASS on all eight before shipping. Score WARN = revise. FAIL on any single rule = do not ship.

Auditors: for each rule, paste a specific quote from the lesson that triggers the verdict. If you can't cite a quote, you didn't audit — re-read.

| Rule | Name | One-line definition | Violation signal (what a fail looks like) | How to raise the score |
|------|------|---------------------|--------------------------------------------|------------------------|
| **Q1** | Voice fidelity | Reads like the 3 reference lessons (ralph-long-running-loop, skills-install-from-root, remote-terminus-tmux-mac-mini): first person, direct, builder-to-builder, concrete nouns. | Abstract SaaS tone, second-person cheerleading ("you'll love..."), parallel-structure mic-drops, three-bullet "Here's why" framings, em-dash sandwiches, eyebrow words ("Approach", "What you get"), "welcome to" preambles. | Read a reference lesson aloud, then read the draft aloud. If the volume doesn't match, rewrite the weakest paragraph using the reference's sentence shapes. |
| **Q2** | Depth (named example) | Contains at least ONE worked example with real stack names, real commands, real file paths, or a real number-about-the-world (not author-scoreboard). | Only generic framing: "agents can do things", "skills help you". No concrete artifact a reader could copy. | Replace one abstract sentence with a named tool + real path (`~/.claude/skills/...`), a bash one-liner, or a named workflow from Robert's setup (Mac Mini + tmux + Terminus, Ralph loop, 6-layer image template, etc). |
| **Q3** | Concrete artifact | At least one command, code block, file path, URL, or named config the reader can actually run or inspect on their machine. | Lesson is pure prose. Nothing the reader could paste into a terminal. Nothing they could grep for. | Add a `bash` block, an absolute path, a command invocation, or a canonical URL from the stack. One is enough; two is better. |
| **Q4** | Arc (hook → load-bearing middle → next action) | Opens with a concrete promise or failure mode (NOT "welcome to"), the middle carries a load-bearing idea, the ending gives one specific next action the reader can do today. | Opens with "welcome to / in this lesson". Middle is three parallel bullets with no idea underneath. Ends with "let's dive in" or no action at all. | First sentence must be a claim or a failure mode the reader recognizes. Last paragraph must start with "**Before the next lesson:**" or equivalent and name one thing to do. |
| **Q5** | Opinion (hot take) | Contains at least one sentence that is Robert's named opinion — a take a generic AI would not write because it requires judgment or conviction. | Every sentence is neutral information. No "I think / I've found / the mistake most people make / this matters more than X". Everything is defensible and nothing is interesting. | Name a trade-off, a failure mode you've seen, or a contrarian position. Prefer "the mistake most people make is X" or "this is worth more than it sounds" to bland restatements. |
| **Q6** | Citation honesty | Every named person / quote / stat is either grounded in `source_quotes:` frontmatter OR is generic pattern language ("the pattern I see in client work"). No fabricated quotes. No author-scoreboard numbers. | Body names Jordan/Daniel/Andy/etc without a matching structured `quote:` payload. Uses counts-of-my-own-output ("70 skills shipped", "620 endpoints"). | Strip names you cannot ground. Strip counts of Robert's own output (decoy signal per feedback_numbers_decoy_rule). Numbers-about-the-world and generic patterns are fine. |
| **Q7** | Not-yours smell test | Could a generic AI have produced this lesson from the title alone? If yes, FAIL. | The lesson contains no detail that reveals Robert actually built/ran this. No idiosyncratic setup, no specific tool versions, no "I tried X and it broke, so I do Y" moment. Reads like a Wikipedia summary of the topic. | Add something that only someone who ran this would know: a gotcha, a version caveat, a config flag that matters, the order of operations that actually works, the default that bit you. |
| **Q8** | Shape over credential | Teaches a piece of the SHAPE of the tech — names components, shows how they connect, gives the reader a mental model they can hold and manipulate. Never frames value as a certificate, badge, or completion credential. | Lesson promises a credential, certificate, badge, or "claim your X". Lesson tells the reader what to do without revealing how the parts fit together. Generic completion gamification. Dumbed-down "prompt better" tips. | Replace credential framing with a recap of the architecture. Name the parts, draw the connections, give the reader something they can mentally manipulate. Practical at our level (Mac Mini + tmux + Terminus, Ralph loop, skill auto-injection, 6-layer image template) — the stuff Robert actually does. |

**Auditor output format (required for every pilot and sweep):**

```
Q1 voice: PASS | WARN | FAIL — "<verbatim quote from lesson>"
Q2 depth: PASS | WARN | FAIL — "<verbatim quote>"
...
Q7 smell: PASS | WARN | FAIL — "<verbatim quote>"
Q8 shape: PASS | WARN | FAIL — "<verbatim quote>"

VERDICT: SHIP | REVISE | RESTART
```

Robert's standard: a lesson he would put his name on scores PASS on all 8. One FAIL = REVISE. Two or more FAIL = RESTART from a different angle.

## Grounding rules (G1-G3) -- MANDATORY for every lesson generation

### G1 -- provenance lives in frontmatter, NEVER in the body

Source file paths go in `source_files:`. Verbatim corpus excerpts go in `source_quotes:` (structured `speaker:/date:/quote:` objects). The rendered body must not contain any `Mined from:` line, source-file path, meeting-date citation, or "based on the transcript from..." phrasing.

### G2 -- named-person anecdotes must be grounded

Any sentence naming a real corpus contributor (Daniel, Dimitrius, Benoit, Andy, Jordan, Ray, etc.) must trace back to a verbatim quote passed as corpus context. That quote must appear in `source_quotes:`. If you cannot ground it, rewrite using generic framing: "the pattern I keep seeing is...", "one thing that comes up in client work...".

**Do not mine `data/krisp-teaching-by-person.json`** -- see warning header in that file. Its `verbatim_quote` fields contain host (Robert) first-person speech mis-attributed to each guest. Root cause of the Jordan fabrication pattern in pass-log 2026-04-13. Use `data/quote-bank.json` for speaker-attributed quotes instead.

### G3 -- run the lint before claiming done

```bash
bash ~/.claude/skills/snappy-course/scripts/lint-draft.sh <path/to/draft.md>
```

Exit 0 = clean. Exit 1 = violations listed. Fix the draft, do not override the lint.

## Operational rules -- MANDATORY for every lesson generation pod

### Image rules

1. **Title rendered INTO the image.** The Ink Journal base ref has an empty lower text slot -- fill it with the lesson title in clean black serif. Blank = incomplete.
2. **Lorem-ipsum gate:** mentally render the ref with lorem ipsum in the text slot. If it looks right, the ref is good. If empty/awkward, fix the ref.
3. **Cover image goes at the TOP** of the lesson body, above the first heading/paragraph. `upload-lesson.sh` handles this. Bottom-placed images are a recurring bug.
4. **Lessons > 500 words need 2-4 inline illustrations**, one per major section break. Each must visualize a RELATIONSHIP (cause/effect, hierarchy, loop, tradeoff), not just a noun.
5. **Canva sync:** every lesson image auto-imports to Canva (Course Images folder `FAHGsRxfKoU`).

### Image generation pipeline

```bash
# 1. Download ref image
curl -o /tmp/ref-1x1.png "https://robert-storage.tor1.digitaloceanspaces.com/images/generated/img-1773735051000.png"

# 2. Generate with ref (6-layer prompt)
npx tsx ~/.claude/skills/snappy-gemini/api.ts image "FULL 6-LAYER PROMPT" --ref /tmp/ref-1x1.png --out /tmp/lesson-img.png

# 3. Resize (HEIGHT then WIDTH)
sips -z 1080 1080 /tmp/lesson-img.png

# 4. Upload to CDN
bash ~/.claude/skills/snappy-image/scripts/cdn-upload.sh --file /tmp/lesson-img.png --context skool --slug lesson-name
```

Course cover spec: 1460 x 752 px (`sips -z 752 1460`), use 16:9 ref (`img-1773735074000.png`).

**Known quirk:** First generation sometimes produces inverted colors (black background). Regenerate with explicit "background MUST be warm cream/egg-white paper, NOT dark or black."

### Writing voice

- **Classroom tone is primary.** Never mention LinkedIn, carousels, social media, or "multi-surface reuse" in any prompt that writes lesson content. The word "LinkedIn" is cursed -- it produces performative writing.
- **Write as a teacher, not a performer.** The tone test: does this read like explaining to a student, or performing for an audience? If the latter, rewrite.
- **Sentence variety.** Mix short punchy lines with longer flowing ones. If three consecutive sentences have the same length/shape, rework. Read aloud -- if it sounds like a list of declarations, break the pattern.
- **Accuracy before polish.** Consult `snappy-mine`, `snappy-corpus`, `snappy-knowledge` BEFORE refining workflow descriptions. A slightly rougher true sentence beats a beautiful made-up one.
- **Multi-pass strategy.** Courses need many waves (write, critique, image, resources, cross-link). Polish what exists before shipping more. One world-class lesson beats five mediocre ones.

## Skool browser automation

### Auth and session setup

**MANDATORY: start every Skool session via the guard script.**

```bash
bash ~/.claude/skills/snappy-course/scripts/skool-browser-start.sh <session-name>
```

The guard script kills stale daemons, backs up auth, verifies `auth_token` cookie, opens the classroom, and verifies logged-in state. Exits non-zero on failure -- STOP and ask Robert.

**Hard rules:**
- Skool WAF blocks headless browsers. Always use `--headed`.
- NEVER call `agent-browser state save` for Skool. Prior incident (2026-04-09): agents saved state while logged-out, clobbered working auth, blocked the swarm for hours.
- Session isolation by `--session <name>` only. Never `pkill agent-browser`.
- Auth file: `~/.openclaw/workspace/skool-auth.json`. Restore: `bash ~/.claude/skills/snappy-course/scripts/skool-auth-restore.sh`.
- Concurrent sessions work if each uses a unique `--session` name.
- Headed session may need close+reopen to get past blank page on first launch.

### React-form rule (the trap)

Programmatic `.value=` + `dispatchEvent` does NOT enable Save buttons on Skool inputs. Real keystrokes are required:

```bash
# Tag the input first
agent-browser --session "$S" eval "document.querySelector('SELECTOR').id = 'my-input'"
# Real keystrokes via Playwright
agent-browser --session "$S" fill "#my-input" "New value"
```

The `_valueTracker` hack, `Object.getOwnPropertyDescriptor(...).set.call()`, and toggling other fields all fail. Only `agent-browser fill` (Playwright real keystrokes) works.

### Page editor (Tiptap)

The Skool lesson editor is Tiptap/ProseMirror. Key facts:

- **Enter edit mode:** click the pencil button (`.iuLEXd`) via `dispatchEvent(new MouseEvent('click',{bubbles:true}))`.
- **Title input:** Find by placeholder: `Array.from(document.querySelectorAll('.styled__SingleLineInput-sc-1saiqqb-1')).find(i=>i.placeholder==='Title')`. There are 3 visible SingleLineInputs (2 Search + 1 Title) -- never use "non-empty value" heuristic.
- **Body editor:** `.tiptap.ProseMirror.skool-editor2` (contenteditable).
- **Set content:** `editor.commands.setContent(html)` (Tiptap API). `innerHTML` also works with `dispatchEvent(new Event('input',{bubbles:true}))`.
- **Image upload:** `agent-browser upload "#upload-image-input" /path/to.png` (auto-inserts at cursor). This is the ONLY path that triggers Skool's CDN handler and persists `assets.skool.com/f/...` URLs. **Data URLs (`data:image/...;base64,...`) injected via `setContent()` render in-editor but get stripped to `null` on save** — the rendered page shows a broken `classroom/null` image with naturalWidth=0. Confirmed recovery 2026-04-14 on lesson 1.1: prior pod used data-URL injection, save silently nulled src; running `replace-cover.sh` with the native `#upload-image-input` path produced a clean `assets.skool.com/f/f40396.../946f54....png` URL that survived independent reload. Never inject images via `setContent(html)` — always go through the hidden file input. To set alt text on a persisted image, use `editor.view.dispatch(state.tr.setNodeMarkup(imgPos, null, {...node.attrs, alt: '...', title: '...'}))` after upload, then dirty the form via `editor.chain().focus('end').insertContent(' ').run()` + a subsequent `deleteRange` to re-enable SAVE.
- **SAVE requires title dirty.** `setContent()`, image uploads, ProseMirror transactions do NOT enable SAVE. Fill title LAST via `agent-browser fill` -- this is the only step that enables SAVE.
- **Title limit:** 50 characters. Counter goes red when exceeded.
- **Published/Draft toggle:** New pages default to Published. You MUST toggle before SAVE or the lesson ships live. `upload-lesson.sh` handles this automatically.
- **Read-mode body selector for audits:** `.styled__ModuleBody-sc-cgnv0g-3 .tiptap` (NOT `.styled__ModuleBody-sc-cgnv0g-3` which always has exactly 1 child wrapper).
- **Screenshot:** `agent-browser --session "$S" screenshot /tmp/foo.png` (path is positional, NOT `--output`). Full-page: `agent-browser --session "$S" screenshot --full /tmp/foo.png`.
- **`agent-browser press`:** Takes ONLY a key name, not a selector. Focus first, then press.

**Save-enable trap recovery:**
```bash
agent-browser --session "$S" eval "document.getElementById('t-title').focus()"
agent-browser --session "$S" type "#t-title" " "      # append space -- SAVE enables
agent-browser --session "$S" eval "document.getElementById('t-title').focus()"
agent-browser --session "$S" press Backspace           # remove space, SAVE stays enabled
```

**Save-enable trap — when title trick fails (Wave 9, 2026-04-14):** Skool normalizes trailing whitespace on the title field, so the space+backspace trick can leave SAVE disabled even though the title input shows 35 chars. `editor.commands.setContent()` also does NOT dirty the form. The reliable path when you need to replace body content is to dirty through the Tiptap editor API directly:
```bash
# After setContent or any programmatic body change:
agent-browser --session "$S" eval "const e=document.querySelector('.tiptap.ProseMirror.skool-editor2').editor; e.chain().focus('end').insertContent(' ').run()"
# SAVE now enabled. Optionally delete the inserted space via a ProseMirror transaction —
# the form stays dirty because Skool tracks dirty-flag, not deep equality.
agent-browser --session "$S" eval "const e=document.querySelector('.tiptap.ProseMirror.skool-editor2').editor; e.chain().focus('end').deleteRange({from: e.state.selection.from - 1, to: e.state.selection.from}).run()"
```
Also: if you use `setContent(coverHtml + bodyHtml)` to preserve an existing cover image, the image can get dropped when saving — safer to re-insert the cover image via a ProseMirror transaction (`tr.insert(0, schema.nodes.image.create({src}))`) AFTER the body replace, before saving.

**Tiptap capabilities:** H1-H4, bold, italic, strike, code, inline code, ordered/unordered lists, blockquotes, code blocks with language, horizontal rules, links, images, video embeds (YouTube/Loom/Vimeo/Wistia). Does NOT support: tables, callouts, toggles, task lists, text color, alignment, underline, emoji, slash commands.

**Tiptap does NOT render `<table>` via innerHTML.** HTML table elements are flattened to inline text. Use bold paragraph lists instead.

**ProseMirror image deletion (DOM removal does not work):**
```javascript
const editor = document.querySelector('.tiptap.ProseMirror.skool-editor2').editor;
const view = editor.view;
const {state} = view;
let tr = state.tr;
const toDelete = [];
state.doc.descendants((node, pos) => {
  if (node.type.name === 'image') toDelete.push({pos, size: node.nodeSize});
});
toDelete.reverse().forEach(({pos, size}) => { tr = tr.delete(pos, pos + size); });
view.dispatch(tr);
```

**ProseMirror inline image insertion at specific position:**
```javascript
// Map positions first
const positions = [];
editor.state.doc.forEach((n,o) => positions.push({offset:o, type:n.type.name, text:n.textContent.slice(0,60)}));
// Set cursor then upload
editor.chain().focus().setTextSelection(targetOffset).run();
// Then: agent-browser upload "#upload-image-input" <path>
```

**`insertAdjacentHTML` silently drops `<img>` tags** (not ProseMirror schema nodes). Use `editor.schema.nodes.image.create` + `tr.insert` instead.

### Sidebar navigation and dropdown menus

| What | Selector | Notes |
|------|----------|-------|
| Title text | `.styled__MenuItemTitle-sc-1wvgzj7-8` | Shared by sections and pages |
| Row wrapper | `.styled__MenuItemTitleWrapper-sc-1wvgzj7-6` | Section: `.kOFVaB`. Page: `.jKMFqx`/`.kqLDxL` |
| Kebab/dropdown trigger | `.styled__DropdownButton-sc-1c1jt59-9` | Parent has `display:none` (CSS hover). Must force-show: `btn.parentElement.style.display='flex'`, tag with id, then `dispatchEvent(new MouseEvent('click',{bubbles:true}))` |
| Course tile wrapper | `.styled__CourseLinkWrapper-sc-ugqan8-6` | Navigate into course: tag wrapper, then `agent-browser click "#id"` (dispatchEvent does NOT navigate) |
| Course tile dropdown | `.styled__DropdownButton-sc-1c1jt59-9` inside `.styled__CourseDropdownMenu-sc-ugqan8-8` | dispatchEvent click |

**Click-pattern rule:** Sidebar dropdown buttons require `dispatchEvent(new MouseEvent('click',{bubbles:true}))`. Plain `.click()` does nothing. `agent-browser click` may navigate to the page instead of opening the menu.

**Always tag-then-click:** Find row by text, query its dropdown child, set `.id`, then dispatchEvent.

### Update course tile description (Edit course modal)

Classroom index → tile kebab → Edit course → description textarea → Save. The tile kebab parent is hidden (hover-only); force-show before clicking. Full recipe:

```bash
S="skool-tile-desc-pod"
# 1. Open dropdown on tile (force-show + dispatchEvent, NOT agent-browser click)
agent-browser --session "$S" eval "
  const w=[...document.querySelectorAll('.styled__CourseLinkWrapper-sc-ugqan8-6')].find(x=>x.innerText.startsWith('DSPy'));
  const dd=w.querySelector('.styled__CourseDropdownMenu-sc-ugqan8-8');
  dd.style.display='flex';
  dd.querySelector('.styled__DropdownButton-sc-1c1jt59-9').dispatchEvent(new MouseEvent('click',{bubbles:true}));
"
# 2. Click Edit course (first dropdown option)
agent-browser --session "$S" eval "document.querySelector('.skool-ui-dropdown-option-0').dispatchEvent(new MouseEvent('click',{bubbles:true}))"
# 3. Tag textarea and fill via agent-browser fill (NOT .value=)
agent-browser --session "$S" eval "document.querySelector('.skool-ui-base-modal .styled__MultiLineInput-sc-1saiqqb-2').id='course-desc-input'"
agent-browser --session "$S" fill "#course-desc-input" "New description here"
# 4. Click Save in modal
agent-browser --session "$S" eval "
  const modal=document.querySelector('.skool-ui-base-modal');
  [...modal.querySelectorAll('button')].find(b=>b.textContent.trim()==='Save').dispatchEvent(new MouseEvent('click',{bubbles:true}))
"
```

Course tile dropdown options (order): `-0` Edit course, `-1` Move ←, `-2` Move →, `-3` Duplicate course, `-4` View as member, `-5` Share course key. Delete course is `.styled__DeleteDropdownItem-sc-ugqan8-18` (separate).

### Folder dropdown menu items

| Item | Selector |
|------|----------|
| Edit folder | `.skool-ui-dropdown-option-0` |
| Add page in folder | `.skool-ui-dropdown-option-1` |
| Duplicate folder | `.skool-ui-dropdown-option-2` |
| Delete folder | `.styled__DeleteDropdownItem-sc-1wvgzj7-3` (NOT a dropdown-option) |

### Page dropdown menu items (force-show kebab parent first)

Edit page, Revert to draft, Change folder, Duplicate, Drip status, **Delete** (red, shows confirmation modal with CANCEL and DELETE buttons).

### Edit modal (folder/course)

| What | Selector |
|------|----------|
| Modal root | `.skool-ui-base-modal` |
| Name input | `.styled__SingleLineInput-sc-1saiqqb-1` |
| Description textarea | `.styled__MultiLineInput-sc-1saiqqb-2` |
| Save button | `Array.from(modal.querySelectorAll('button')).find(b=>b.textContent.trim()==='Save')` |

**Folder name limit: 50 chars.** Effective sidebar clip is ~44 chars.
**Course description limit: 500 chars.** Counter goes red, Save disabled when exceeded.

### Course cover upload (bridge input pattern)

React-dropzone intercepts `setInputFiles` on its hidden input. The fix:

```bash
S="session-name"

# 1. Inject bridge input
agent-browser --session "$S" eval "
  const tmp = document.createElement('input');
  tmp.type = 'file'; tmp.accept = 'image/*'; tmp.id = 'cover-bridge';
  tmp.style.cssText = 'position:fixed;top:0;left:0;z-index:99999';
  document.body.appendChild(tmp);
  tmp.addEventListener('change', function() {
    if (!tmp.files.length) return;
    const dzInput = document.querySelector('[class*=DropzoneRoot] input[type=file]');
    const rp = Object.keys(dzInput).find(k => k.startsWith('__reactProps'));
    dzInput[rp].onChange({ target: { files: [tmp.files[0]] } });
  });
  'bridge ready';
"

# 2. Upload via bridge
agent-browser --session "$S" upload "#cover-bridge" /tmp/cover.png

# 3. Click Save on crop modal (last Save on page)
agent-browser --session "$S" eval "
  const saves = Array.from(document.querySelectorAll('button')).filter(b => b.textContent.trim() === 'Save');
  saves[saves.length - 1].dispatchEvent(new MouseEvent('click', {bubbles: true}));
"

# 4. Click Save on edit course modal
agent-browser --session "$S" eval "
  const save = Array.from(document.querySelectorAll('button')).find(b => b.textContent.trim() === 'Save');
  save.dispatchEvent(new MouseEvent('click', {bubbles: true}));
"

# 5. Clean up
agent-browser --session "$S" eval "document.getElementById('cover-bridge')?.remove()"
```

Cover image spec: 1460 x 752 px.

### Eval-grep gotcha

`agent-browser eval` returns JSON-stringified results wrapped in another layer of quoting: `"{\"hit\":true}"`. Use `grep -qE '(\"hit\":true|\\"hit\\":true)'` or `grep -qF "$TITLE"` for substring matches.

### Additional Skool features (resources, transcripts, pinned posts)

Full browser automation recipes for resource links, resource files, transcripts, pinned community posts, and resource management (edit/reorder/delete) are documented in:

**`~/.claude/skills/snappy-course/data/skool-feature-recipes.md`**

Key facts from that reference:
- Resources and transcripts save immediately when added via ADD dropdown. They persist independently of the page SAVE button.
- CANCEL does NOT revert resource/transcript additions.
- Adding a resource link enables the page SAVE button as a side effect (useful for force-enabling SAVE).
- Resource label limit: 34 characters.
- Pinning community posts is done FROM the community feed, not the lesson editor.

### Rich formatting for lessons (available in Tiptap)

Use these to make lessons more than flat paragraphs:

```javascript
editor.commands.setContent(`
  <h2>Section Title</h2>
  <p>Intro with <strong>bold</strong> and <code>inline code</code>.</p>
  <blockquote><p>Key takeaway here.</p></blockquote>
  <ol><li><strong>Step 1:</strong> Install the tool</li></ol>
  <pre><code class="language-bash">npm install @anthropic-ai/sdk</code></pre>
  <hr>
  <h3>Subsection</h3>
`);
```

### Updating existing lesson content

`upload-lesson.sh` is create-only (idempotency check skips existing). To update: navigate to lesson, enter edit mode, extract cover image HTML first (`child.querySelector('img')`), replace innerHTML with `coverHtml + newBody`, dirty title via fill/type+backspace, save.

## Push wave discipline -- MANDATORY sequencing

**Audits run BEFORE push pods, never in parallel.** Skool writes are irreversible from the orchestrator's POV — by the time a lint/dashboard pod's report returns, a parallel push pod has already landed defective bytes into a live lesson.

**Why:** On 2026-04-14 during the Claude Code Mastery M1 push, a dashboard pod and a push pod were dispatched in the same wave. The dashboard flagged a §4a banned word (`unlocks`) in the M1.2 draft. By the time its report arrived, the push pod had already created the Skool lesson carrying that defect live. Required a separate patch-pod round-trip.

**How to apply:**
- **Local work** (file inspections, drafts, image generation, dry-run greps): parallelize freely.
- **External-surface writes** (Skool save, API `PUT`, post publish, DM send): sequence in two waves.
  - Wave 1: audit/dashboard/lint pods. Await results. Inspect output.
  - Wave 2: push pods, only after Wave 1 returned clean.
- Push pods ALSO re-grep their own payload before saving (see `snappy-positioning` AGENTS.md → "Push pods: re-grep payload in-line"). Defense in depth.
- A push pod's verification certificate must show the pre-save grep came back empty for all §4a phrases.

## Ship verification protocol -- MANDATORY

**A toast is not evidence. An action is not done until verified.**

After EVERY action (edit, upload, rename, delete):
1. Click SAVE and wait for toast.
2. Navigate AWAY from the page.
3. Navigate BACK to the edited page.
4. Screenshot the rendered page in read mode.
5. Verify each change in the screenshot.
6. If images expected but not visible, enter edit mode and check `<img>` existence. Missing = upload silently failed.

**Cover-at-top pass state:** `imgIdx` in `{0, 1}` with `contentBefore === 0`. Either is valid. `imgIdx >= 2` or `contentBefore > 0` = cover is buried, must be hoisted.

**Image count verification:**
```javascript
document.querySelectorAll('.ProseMirror img, [class*=Module] img, [class*=Content] img').length
// 0 = images did not persist
```

**Certificate format:**
```
certificate:
  premises: [what was supposed to change]
  action: [what the agent did]
  trace: [session name, timestamps]
  evidence: post-navigation screenshot showing [title/body/images] rendered correctly
  image_count: [expected] actual [observed]
  conclusion: VERIFIED | FAILED -- [detail]
```

Hard rule: if `image_count` expected > 0 and actual = 0, the action FAILED. Re-upload.

## Classroom state (as of 2026-04-12)

### Active courses (all Draft)

| Course | Lessons | Notes |
|--------|---------|-------|
| Agentic Development Playbook | 12 | All have images |
| Skills as the OS | 6 | All have images |
| Xano + Agents | 5 | All have images |
| Ray's Mental Models | 5 | All have images |
| MCP Deep Dive | 8 | All have images |
| Tech Content | 7 | All have images |
| MCP Wednesday Vault | 1 | Intro page only, no session archives |

### Other modules (not course portfolio)

Getting Started with Snappy, Business Resources, Core Implementation Library, AG Grid/WeWeb/Webflow boilerplates, Meet Your Mentor: Daniel Migizi.

## Cross-link rules

1. Every Skool lesson links to its blog mirror at the bottom.
2. Every blog mirror links to the Skool classroom and the Slack invite.
3. Every blog mirror ends with one CTA: newsletter signup OR book a call. Pick one.
4. Every Slack `#lessons` thread is anchored to the blog URL.
5. UTMs on every outbound link: `?utm_source=<surface>&utm_medium=<format>&utm_campaign=course-launch`.
6. Skool posts referencing external resources link to canonical snappy.ai page, never third party.

## Rules

1. No LinkedIn for course. Robert said no.
2. Slack output is drafts only. Review before send.
3. Skool classroom is source of truth. Blog mirrors track Skool, not the reverse.
4. One CTA per surface. Never two equal CTAs.
5. Never invite cold. Every invite traces to a prior touchpoint in `snappy-knowledge`.
6. Voice is non-negotiable. Load `snappy-positioning` and read section 4a before drafting any copy.

## Invite flow

Priority order: active Skool members (14 days) > joined-but-never-opened > newsletter subscribers > past call no-shows > recent commenters. Channel: Skool DM primary, email secondary. Max 1 course-specific DM per member per week.

## Slack workspace

Team ID: `T0AQPFMQEKB`. Subdomain: `snappyclaudec-qdk8925.slack.com`. Channels: #welcome, #lessons, #wins, #help, #live, #general. All output goes through `snappy-slack` as drafts.

## Skool API endpoints

| Method | Endpoint | Notes |
|--------|----------|-------|
| POST | `/groups/{groupId}/update-landing-page-description` | Body: `{lpDescription}`. 1000 char limit. |
| GET/POST | `/groups/{groupName}/links` | Sidebar links. 3 maximum. |
| POST | `/groups/{groupName}/move-link` | `old` is 0-indexed, `new` is 1-indexed. |

## Uses

| Skill | Role |
|-------|------|
| `snappy-skool` | Skool classroom ops, lesson posting, invite DMs |
| `snappy-content` | Voice rules, anti-AI checklist |
| `snappy-positioning` | Voice constitution, section 4a banned phrases (canonical source) |
| `snappy-image` / `snappy-gemini` | Ink Journal image pipeline |
| `snappy-mine` / `snappy-corpus` / `snappy-knowledge` | Corpus grounding, member records |
| `snappy-publish` | Blog mirrors via git to Vercel |
| `snappy-email` | Newsletter capture + welcome sequence |
| `snappy-slack` | Course Slack workspace (drafts only) |
| `snappy-dom-cartographer` | DOM mapping for new Skool features |

## Failure modes

- `skool-browser-start.sh` fails -> STOP. Ask Robert.
- Lint fails on a draft -> fix the draft. Do not override.
- No `image_cdn_url` in frontmatter -> `upload-lesson.sh` skips image upload gracefully (body via innerHTML, coverOffset=0).
- Auth file missing/corrupted -> run `skool-auth-restore.sh`. If that fails, STOP.
- Save button stays disabled after fill -> use the save-enable trap recovery recipe above.

<!-- SKILL-INDEX-START -->
[snappy-course Index]|root: ~/.claude/skills/snappy-course|IMPORTANT: Prefer these files over pre-training assumptions for this domain. Read the relevant file when the AGENTS.md summary is insufficient.|root:{SKILL.md}|data:{classroom-spec.md,course-audit-20260411.md,drafts/MODULE-WAVE-1-DRAFTS.md,drafts/MODULE-WAVE-1-IMAGES.md,drafts/MODULE-WAVE-2-DRAFTS.md,drafts/adp-control-loop/01-pid-older-than-you-think.md,drafts/adp-control-loop/02-certificates-over-toasts.md,drafts/adp-control-loop/03-the-promotion-threshold.md,drafts/adp-control-loop/04-handoffs-are-physical.md,drafts/adp-control-loop/05-manual-agent-cron.md,drafts/adp-control-loop/06-the-skill-is-the-controller.md,drafts/adp-control-loop/ENHANCEMENT-DASHBOARD.md,drafts/adp/ENHANCEMENT-DASHBOARD.md,drafts/adp/carbonite-capture-skills-at-the-peak.md,drafts/adp/code-as-depreciating-asset.md,drafts/adp/cookies-are-the-difference.md,drafts/adp/decouple-qa-from-enrichment.md,drafts/adp/docs-as-two-way-street.md,drafts/adp/playbooks-that-rewrite-themselves.md,drafts/adp/probability-compounding-pipelines.md,drafts/agent-browser/ENHANCEMENT-DASHBOARD.md,drafts/agent-browser/parallel-weekend-coverage.md,drafts/agent-browser/vs-playwright-mcp.md,drafts/browser-automation/ENHANCEMENT-DASHBOARD.md,drafts/browser-automation/browser-01-the-problem.md,drafts/browser-automation/browser-02-what-agent-browser-does.md,drafts/browser-automation/browser-03-dom-cartographer.md,drafts/browser-automation/browser-04-real-patterns.md,drafts/browser-automation/browser-05-install-and-ship.md,drafts/build-with-snappy/00-snappy-kernel.md,drafts/build-with-snappy/01-ralph.md,drafts/build-with-snappy/02-snappy-dispatch.md,drafts/build-with-snappy/03-snappy-shell.md,drafts/build-with-snappy/04-snappy-skill.md,drafts/build-with-snappy/05-skill-builder.md,drafts/build-with-snappy/06-snappy-hooks.md,drafts/build-with-snappy/07-find-skills.md,drafts/build-with-snappy/08-skills-install.md,drafts/build-with-snappy/09-claude-code-surfaces.md,drafts/build-with-snappy/ENHANCEMENT-DASHBOARD.md,drafts/build-with-snappy/REVIVAL-DASHBOARD.md,drafts/build-with-snappy/_OUTLINE.md,drafts/build-with-snappy/_TEMPLATE.md,drafts/build-with-snappy/_live-snapshot-M2L2.md,drafts/build-with-snappy/course-cover-spec.md,drafts/build-with-snappy/module-02/01-snappy-openrouter.md,drafts/build-with-snappy/module-02/02-snappy-ai-models.md,drafts/build-with-snappy/module-02/03-snappy-gemini.md,drafts/build-with-snappy/module-02/04-research.md,drafts/build-with-snappy/module-02/05-dspy-brain.md,drafts/build-with-snappy/module-03/01-agent-browser.md,drafts/build-with-snappy/module-03/02-snappy-browse.md,drafts/build-with-snappy/module-03/03-snappy-dom-cartographer.md,drafts/build-with-snappy/module-03/04-snappy-api-sniffer.md,drafts/build-with-snappy/module-03/05-snappy-desktop.md,drafts/build-with-snappy/module-03/06-mac-mini-server.md,drafts/build-with-snappy/module-04/01-snappy-channel-contract.md,drafts/build-with-snappy/module-04/02-snappy-telegram.md,drafts/build-with-snappy/module-04/03-snappy-whatsapp.md,drafts/build-with-snappy/module-04/04-snappy-imessage.md,drafts/build-with-snappy/module-04/05-snappy-sync.md,drafts/build-with-snappy/module-04/06-snappy-walkthrough.md,drafts/build-with-snappy/module-05/01-snappy-ffmpeg.md,drafts/build-with-snappy/module-05/02-snappy-video.md,drafts/build-with-snappy/module-05/03-video-pipeline.md,drafts/build-with-snappy/module-05/04-remotion-best-practices.md,drafts/build-with-snappy/module-05/05-snappy-sensors.md,drafts/build-with-snappy/module-06/01-universal-backend.md,drafts/build-with-snappy/module-06/02-xanoscript-builder.md,drafts/build-with-snappy/module-06/03-snappy-xano-mcp.md,drafts/build-with-snappy/module-06/04-mcp-server-builder.md,drafts/build-with-snappy/module-06/05-xano-mcp-tool-developer.md,drafts/build-with-snappy/module-06/06-chatgpt-mcp-apps.md,drafts/build-with-snappy/module-06/07-snappy-box.md,drafts/build-with-snappy/module-06/08-notion-api.md,drafts/build-with-snappy/module-07/01-vercel-agents.md,drafts/build-with-snappy/module-07/02-vercel-react-best-practices.md,drafts/build-with-snappy/module-07/03-vercel-composition-patterns.md,drafts/build-with-snappy/module-07/04-vercel-react-native-skills.md,drafts/build-with-snappy/module-07/05-seymour-papert.md,drafts/build-with-snappy/module-07/06-web-design-guidelines.md,drafts/build-with-snappy/module-07/07-crayonchat.md,drafts/build-with-snappy/module-07/08-dev-terminal.md,drafts/claude-code-bridges/completion-claim-your-certificate.md,drafts/claude-code-bridges/foundation-agent-skills.md,drafts/claude-code-bridges/foundation-building-with-claude-api.md,drafts/claude-code-bridges/foundation-claude-code-in-action.md,drafts/claude-code-bridges/foundation-intro-to-mcp.md,drafts/claude-code-mastery/DASHBOARD.md,drafts/claude-code-mastery/ENHANCEMENT-DASHBOARD.md,drafts/claude-code-mastery/m1/1.1-body-revised.md,drafts/claude-code-mastery/m1/1.1-hero-spec.md,drafts/claude-code-mastery/m1/1.2-body-new.md,drafts/claude-code-mastery/m1/alt-text-1.1.md,drafts/claude-code-mastery/m1/completion-stakes.md,drafts/claude-code-mastery/m1/cta-block.md,drafts/claude-code-mastery/m1/loom-script-1.1.md,drafts/claude-code-mastery/m1/module-description.md,drafts/claude-code-mastery/m1/resources-1.1.md,drafts/claude-code-mastery/m1/resources-1.2.md,drafts/claude-code-mastery/m2/2.1-hero-spec.md,drafts/claude-code-mastery/m2/2.2-body-revised.md,drafts/claude-code-mastery/m2/2.2-inline-spec.md,drafts/claude-code-mastery/m2/inspection-report.md,drafts/claude-code-mastery/m3/3.1-hero-spec.md,drafts/claude-code-mastery/m3/3.2-body-revised.md,drafts/claude-code-mastery/m3/3.2-hero-spec.md,drafts/claude-code-mastery/m3/inspection-report.md,drafts/claude-code-mastery/m4/4.1-hero-spec.md,drafts/claude-code-mastery/m4/4.2-body-revised.md,drafts/claude-code-mastery/m4/4.2-hero-spec.md,drafts/claude-code-mastery/m4/inspection-report.md,drafts/claude-code-mastery/m5/5.1-hero-spec.md,drafts/claude-code-mastery/m5/5.2-body-revised.md,drafts/claude-code-mastery/m5/inspection-report.md,drafts/claude-code-mastery/m6/6.1-body-revised.md,drafts/claude-code-mastery/m6/6.1-hero-spec.md,drafts/claude-code-mastery/m6/inspection-report.md,drafts/claude-code/ENHANCEMENT-DASHBOARD.md,drafts/claude-code/bug-report-widget.md,drafts/claude-code/ralph-02-the-loop-pattern.md,drafts/claude-code/ralph-03-exit-criteria.md,drafts/claude-code/ralph-05-install-and-run.md,drafts/claude-code/ralph-long-running-loop.md,drafts/claude-code/remote-terminus-tmux-mac-mini.md,drafts/claude-code/skills-install-from-root.md,drafts/claude-code/terminal-vs-vscode-extension.md,drafts/claude-code/welcome-what-youll-build.md,drafts/crayonchat/ENHANCEMENT-DASHBOARD.md,drafts/crayonchat/crayonchat-01-what-it-is.md,drafts/crayonchat/crayonchat-02-wiring-backend.md,drafts/crayonchat/crayonchat-03-customization.md,drafts/crayonchat/crayonchat-04-install-and-ship.md,drafts/dspy/00-welcome-dspy.md,drafts/dspy/ENHANCEMENT-DASHBOARD.md,drafts/dspy/measuring-agent-output-in-background.md,drafts/dspy/ship-dspy-single-session.md,drafts/fly/00-welcome-fly.md,drafts/fly/ENHANCEMENT-DASHBOARD.md,drafts/fly/mcp-hot-deploy-wrap.md,drafts/fly/never-split-logic-supabase-edge.md,drafts/fly/suspended-machine-mental-model.md,drafts/fly/two-instances-dev-prod.md,drafts/gemini/00-welcome-gemini.md,drafts/gemini/ENHANCEMENT-DASHBOARD.md,drafts/gemini/claude-code-fallback.md,drafts/gemini/direct-api-for-nano-banana.md,drafts/hookdeck/00-welcome-hookdeck.md,drafts/hookdeck/ENHANCEMENT-DASHBOARD.md,drafts/hookdeck/every-webhook-day-one.md,drafts/hookdeck/migration-insurance.md,drafts/knowledge-graphs/ENHANCEMENT-DASHBOARD.md,drafts/knowledge-graphs/falkordb-01-what-is-a-knowledge-graph.md,drafts/knowledge-graphs/falkordb-02-why-falkordb.md,drafts/knowledge-graphs/falkordb-03-entity-vs-edge.md,drafts/knowledge-graphs/falkordb-04-ingestion.md,drafts/knowledge-graphs/falkordb-05-querying.md,drafts/krisp/00-welcome-krisp.md,drafts/krisp/ENHANCEMENT-DASHBOARD.md,drafts/krisp/mine-pipeline-transcript-to-lesson.md,drafts/krisp/queryable-meeting-retrospection.md,drafts/mac-mini-stack/ENHANCEMENT-DASHBOARD.md,drafts/mac-mini-stack/mac-mini-01-why-a-mac-mini.md,drafts/mac-mini-stack/mac-mini-02-hardware-and-base-install.md,drafts/mac-mini-stack/mac-mini-03-terminal-tmux-remote-access.md,drafts/mac-mini-stack/mac-mini-04-claude-code-remote.md,drafts/mac-mini-stack/mac-mini-05-one-pass-install.md,drafts/mcp-cloudflare/ENHANCEMENT-DASHBOARD.md,drafts/mcp-cloudflare/mcp-cloudflare-01-what-mcp-is-practically.md,drafts/mcp-cloudflare/mcp-cloudflare-02-why-meta-tools-beat-one-per-endpoint.md,drafts/mcp-cloudflare/mcp-cloudflare-03-cloudflare-workers-as-your-mcp-host.md,drafts/mcp-cloudflare/mcp-cloudflare-04-snappy-xano-mcp-case-study.md,drafts/mcp-cloudflare/mcp-cloudflare-05-build-your-own.md,drafts/mcp-cloudflare/mcp-cloudflare-06-when-not-to-use-mcp.md,drafts/mcp/ENHANCEMENT-DASHBOARD.md,drafts/mcp/authentication-with-oauth.md,drafts/mcp/building-mcp-from-existing-api.md,drafts/mcp/cli-vs-mcp-tradeoff.md,drafts/mcp/deploying-on-cloudflare-workers.md,drafts/mcp/designing-your-tools.md,drafts/mcp/interactive-ui-in-chat.md,drafts/mcp/mcp-as-feedback-channel-not-wrapper.md,drafts/mcp/mcp-vs-skills-vs-cli.md,drafts/mcp/nine-meta-tools-cover-four-hundred-endpoints.md,drafts/mcp/plug-your-mcp-into-your-dev-loop.md,drafts/mcp/sharing-installing-controlling-mcp.md,drafts/mcp/tool-descriptions-context-window.md,drafts/mcp/two-tier-judgment-tools.md,drafts/mcp/what-an-mcp-actually-is.md,drafts/mcp/xano-mcp-as-cli-mistake-insurance.md,drafts/ray/ENHANCEMENT-DASHBOARD.md,drafts/ray/ar-aging-the-business-line.md,drafts/ray/four-drivers-of-rate.md,drafts/ray/hours-and-minutes.md,drafts/ray/multiplication-vs-addition.md,drafts/ray/the-harness-has-opinions.md,drafts/ray/the-kitchen-sink-demo.md,drafts/ray/the-lackey-trap.md,drafts/ray/the-rewrite-trap.md,drafts/ray/the-transcript-data-asset.md,drafts/ray/you-create-the-data.md,drafts/skills-os/ENHANCEMENT-DASHBOARD.md,drafts/skills-os/cli-first-everything-else-second.md,drafts/skills-os/composition-over-orchestration.md,drafts/skills-os/half-the-code-is-explanation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<!-- SKILL-INDEX-END -->

## Used by

- `snappy-image`
- `snappy-remotion`

<!-- SNAPPY-CONTRACT-VERBS-START -->
## Contract verbs

Generated from `api.ts` `HAND_CONTRACT`. Do not hand-edit this block.

| Verb | Contract arguments | Effect | First call |
|---|---|---|---|
| `module` | `module-id` | `read` | `npx tsx ~/.claude/skills/snappy-course/api.ts module <module-id>` |
| `modules` | — | `read` | `npx tsx ~/.claude/skills/snappy-course/api.ts modules` |

## Show the result

When an answer carries `face_hint`, show it with one `snappy_present(<answer>)` call.
See `/snappy-faces` for face selection. Human-facing images must crop to the
element, render at 2x on Retina, and fill the destination channel instead of
placing a small card in a full-page screenshot.
<!-- SNAPPY-CONTRACT-VERBS-END -->

---
name: snappy-course
reports_to: growth
head: false
category: Education
description: >
  Orchestrator for the free agentic-building course. Owns the end-to-end flow:
  Skool classroom (skool.com/snappy) modules and lessons, the Slack workspace where
  students hang out between lessons, blog mirrors at snappy.ai/blog, newsletter
  capture at snappy.ai/newsletter, and the invite + outreach pipeline (Skool DMs,
  not LinkedIn). Knows the content map, the channel structure, the cross-link
  rules, and which downstream skills do the actual work. Triggers on: course,
  free course, agentic course, agentic-building course, snappy course, classroom
  module, lesson, invite to course, course outreach, course slack, course blog
  mirror, newsletter signup, course content map, cohort, course onboarding.
---

# Snappy Course -- Free Agentic-Building Course Orchestrator

## Tagline (canonical)

> Faster than vibe coding. Private AI tools to ship real systems while others prompt. We build businesses, not demos.

Short-form for Skool sidebar banner and community references: **"We build businesses, not demos."**

Source: `/Users/robertboulos/.openclaw/workspace/skool_audit_full.md`.

## Purpose

I run a free course that teaches developers and technical founders how to build
and control agents that ship real systems. The course is not a product. It is
the top of the funnel for everything else at snappy.ai. This skill ties the
pieces together so I do not have to think about which surface owns what.

The pieces:

- Skool classroom at https://www.skool.com/snappy -- modules and lessons live here
- Slack workspace (built and run on the Mac Mini) -- between-lesson conversation
- Blog mirrors at https://snappy.ai/blog -- public, indexable, SEO surface
- Newsletter at https://snappy.ai/newsletter -- email capture and nurture
- Skool DM outreach to existing active members -- invite flow (NOT LinkedIn)

## When to Use This Skill

- Setting up or updating the course content map
- Mirroring a Skool lesson to the blog
- Drafting an invite DM or onboarding sequence
- Posting a course-related update into Slack (drafts only -- never auto-post)
- Wiring a new lesson into the cross-link graph
- Anything that touches more than one surface (Skool + Slack + blog + newsletter)
  in a single move

Do not use for:

- Writing the lesson copy itself → `snappy-content` + `snappy-blog`
- Posting to Skool feed or DMs at the keystroke level → `snappy-skool`
- Shipping the blog mirror → `snappy-publish`
- Editing snappy.ai pages → `snappy-website`

---

## Draft frontmatter contract

Every lesson draft at `data/drafts/<course>/<slug>.md` carries a YAML frontmatter
block. These keys are the canonical contract — the sensor, lint, and ship scripts
all read them.

| Key | When set | Purpose |
|---|---|---|
| `title` | draft creation | Display title (used for Skool page title) |
| `course` | draft creation | Human course name ("Ray's Mental Models") |
| `slug` | draft creation | Filename-matching slug |
| `status` | draft creation | `draft` until shipped; not used for ship detection |
| `word_count` | after body write | Integer used by `[words]` sensor bucket |
| `source_files` | during mining | List of corpus paths used as grounding |
| `source_quotes` | during mining | Structured `speaker/date/quote` objects — D2 lint reads these |
| `image_brief` | image planning | 6-layer Ink Journal brief |
| `image_cdn_url` | after image ship | CDN URL of uploaded lesson cover |
| `image_generated_at` | after image gen | UTC timestamp of generation |
| `skool_url` | **after Skool ship** | Full Skool lesson URL: `https://www.skool.com/snappy/classroom/<cid>?md=<mid>`. **Presence of this key is the ground-truth sensor signal that a lesson has shipped.** |
| `shipped_at` | **after Skool ship** | UTC timestamp `YYYY-MM-DDTHH:MM:SSZ` of the ship. Used to detect stale ship state. |

Key order convention: `skool_url` and `shipped_at` live immediately after
`image_cdn_url` (if present) so image+ship metadata cluster together. The
`snappy-sensors` `[ship-state]` section counts these keys per course; the
`snappy-course/scripts/backfill-ship-state.sh` script parses pass-log
`- Shipped: <course> / <slug> / <url>` lines and patches matching draft files
idempotently. Both fields are optional — a missing `skool_url` means the
lesson has not shipped yet, and the sensor will report it in the `drafted=`
bucket until it does.

New ship pods SHOULD write these two keys directly into the draft file
immediately after a successful upload (in addition to logging the ship line
to pass-log). The backfill script exists to close the gap for legacy ships;
do not rely on it for new pods.

---

## Workflow

**Inputs (skills that feed this one):**

- `snappy-content` -- voice rules, anti-AI checklist, 50% specificity rule
- `snappy-blog` -- long-form lesson mirrors
- `snappy-skool` -- Skool classroom + DM primitives
- `snappy-knowledge` -- member records, who is active, who has not been invited
- `snappy-offer` -- what the course leads into downstream
- `snappy-website` -- blog and newsletter surfaces
- `snappy-positioning` -- voice rules and §4a trip-wires (canonical source) <!-- learning from 2026-04-07 session -->

**Outputs (skills that consume this one):**

- `snappy-skool` -- receives lesson posts, DM drafts, classroom updates
- `snappy-publish` -- receives blog mirrors of lessons
- `snappy-email` -- receives newsletter signups and welcome sequence drafts
- `snappy-slack` -- receives drafted Slack messages (drafts only -- review before send)
- `snappy-knowledge` -- receives invite history, course progress, drop-off events

**Channels (where output is delivered):**

- Skool classroom (modules + lessons)
- Skool feed + DMs
- Slack workspace (Mac Mini-hosted desktop client)
- snappy.ai/blog
- snappy.ai/newsletter

**Orchestrator:**

- `snappy-ops` triggers this during the morning briefing when the calendar shows
  a course slot, and during the Friday weekly review for cohort retention.

---

## Course Content Map

> **LIVE STATE -- update on every Skool classroom change.** This section mirrors what is actually published in Skool. Speculative roadmap goes under "Planned future modules" only. <!-- learning from 2026-04-07 session -->

The classroom in Skool is the source of truth. Every lesson has a blog mirror at
snappy.ai/blog and a Slack thread in `#lessons`. The map is the contract -- if a
module exists in Skool, it must exist in the blog and have a Slack thread.

The live classroom is the **Agentic Development Playbook** (Draft status, 11 pages
as of 2026-04-09). The Skool group also has legacy modules (Getting Started with
Snappy, Business Resources, Core Implementation Library, boilerplate courses, MCP
Wednesday Vault) that are NOT part of the free course content map.

### Section 1 -- Build and control your first agent
1. Start Here, Playbook Overview -- has content, voice-cleaned
2. What agentic building actually is -- has content, voice-cleaned, has image (needs Ink Journal regen)
3. Your stack: Claude Code + skills + MCPs -- has content, voice-cleaned, has image (needs Ink Journal regen)
4. Ship something today: a 30-minute agent -- has content, voice-cleaned, has image (needs Ink Journal regen)

### Section 2 -- Skills, MCPs, and the agentic stack
5. What a skill is and why it changes everything -- has content
6. MCPs in plain English: tools your agent can call -- has content
7. Wiring Claude Code to your codebase -- has content
8. MCP Setup Guide -- rewritten 2026-04-09 (was AI filler with factual errors), has image (needs Ink Journal regen)
9. Tool Comparison Guide -- rewritten 2026-04-09 (was AI filler with factual errors), has image (needs Ink Journal regen)

### Section 3 -- Control: keep agents from going off the rails
- Plans, tasks, and verification -- the control loop -- has content, has Ink Journal image (2026-04-09)
- When to let the agent run and when to stop it -- has content (placeholder, needs voice-clean)
- Reading agent output like a code review -- has content (placeholder, needs voice-clean)

### Housekeeping needed
- 2 empty "New page" placeholders need deleting
- 1 empty "Folder Name" section needs deleting
- Lesson titles with em dashes need fixing (Skool titles edit separately from body)
- All existing images were generated WITHOUT the Ink Journal pipeline and need regeneration

### Planned future modules (not yet built)
- Xano + agents -- building a backend an agent can actually drive
- Skills as the OS -- writing, scoping, and chaining your own skills
- Shipping -- putting one real system into production with agents doing the work

### Other modules in the Skool group (not free course)
- Getting Started with Snappy (free, unlocked) -- orientation
- Core Implementation Library (Level 2 gated) -- implementation guides
- AG Grid for WeWeb/Xano (Private, "No longer available!")
- WeWeb/Xano Application Boilerplate ($149)
- Webflow/Wized/Xano Boilerplate ($149)
- Meet Your Mentor: Daniel Migizi -- single intro page
- MCP Wednesday Vault -- intro page only, no session archives yet

Agents are iteratively building and improving module/lesson content inside Skool.
This skill tracks the map and enforces the mirror.

Rules:

1. A lesson is not "done" until Skool, the blog, and the Slack thread all exist
   and link to each other.
2. Blog mirror is the canonical public URL. Skool URL is gated. Slack thread is
   for discussion only.
3. Every lesson ends with one CTA: join the newsletter, or book a call. Never
   both at equal weight.

---

## Slack Workspace

The canonical course Slack workspace is confirmed:

- **Display name**: Claude Code Training (Robert calls it "Snappy Claude Code Group")
- **Team ID**: `T0AQPFMQEKB`
- **Subdomain**: `snappyclaudec-qdk8925.slack.com`
- **User ID**: `U0AQAJKQ82W`

All course Slack output still goes through `snappy-slack` as drafts for review
before send -- that is a workflow rule, not a discovery gap.

### Channel structure (target -- create if missing)

| Channel | Purpose |
|---|---|
| `#welcome` | New-member intro thread, pinned start-here post |
| `#lessons` | One thread per published lesson, anchored to its blog URL |
| `#wins` | Members posting things they shipped |
| `#help` | Stuck moments -- answered within 24h |
| `#live` | Announcements for live sessions and replays |
| `#general` | Default fallback, low-traffic |

### What was found in `#general` / equivalent

Not inspected. Discovery is pending Mac Mini access. Do not assume content.

---

## Invite Flow

The course is free. Invites go to people who have already shown up somewhere in
the Snappy surface -- Skool members, newsletter subscribers, blog readers who
opted in, sales-call no-shows worth re-engaging. **Not LinkedIn.** Robert has
explicitly said no LinkedIn posting for the course launch.

### Who gets invited (priority order)

1. Active Skool members (last 14 days) who have not yet joined the classroom
2. Skool members who joined but never opened a lesson
3. Newsletter subscribers tagged `interest:agentic` in Xano contacts (ActiveCampaign is NOT in use)
4. Past discovery-call attendees who did not close
5. People who replied to a blog post or commented in Skool feed in the last 30 days

Pull lists via `snappy-knowledge` and `snappy-skool`. Never invite cold.

### Channel for the invite

Skool DM is the primary channel. Email (via `snappy-email`) is the secondary
channel for people not in Skool. Slack invites only go out **after** someone
accepts the course -- Slack is the inside-the-tent surface, not the door.

### Invite DM template (draft -- voice per POSITIONING.md §4a)

Read aloud test: would I say this to a senior engineer at a bar?

```
Hey {first_name} -- I'm running a free course on building and controlling
agents on real codebases. It's all in Skool, with the long versions mirrored
to snappy.ai/blog.

It's the stuff I actually do in client sessions -- Claude Code, MCPs, Xano,
skills, shipping a real system at the end. No pre-recorded fluff.

If you want in, here's the classroom: https://www.skool.com/snappy/classroom

I'll be in the Slack we run alongside it if you get stuck.
```

Trip-wire check:

- No tagline structure, no "the X for Y"
- No banned phrases (no leverage / unlock / supercharge / 10x / seamless)
- First person, not "we"
- No promises of feeling, no countdowns, no "limited spots"
- No alliteration, no closing flourish

If a draft fails any of those, rewrite it. Run it past `snappy-content` before
shipping.

---

## Outreach

Outreach happens inside Skool. Skool DMs to active members. **No LinkedIn
posts, no LinkedIn DMs, for the course launch.**

| Audience | Channel | Skill that does the send |
|---|---|---|
| Active Skool members not in classroom | Skool DM | `snappy-skool` |
| Skool members who lurked then dropped | Skool DM (re-engagement) | `snappy-skool` |
| Newsletter subscribers | Email | `snappy-email` |
| Past call no-shows | Email | `snappy-email` |
| Anyone already inside the course | Slack DM | `snappy-slack` (drafts only) |

Cadence: at most one course-specific DM per member per week. Re-engagement DM
follows the 14-day inactivity trigger from `snappy-skool/SKILL.md`.

---

## Cross-Link Rules

The course only works if the surfaces point at each other. No dead ends.

1. Every Skool lesson links to its blog mirror at the bottom.
2. Every blog mirror links to the Skool classroom and the Slack invite.
3. Every blog mirror ends with one CTA: newsletter signup OR book a call. Pick one.
4. Every Slack `#lessons` thread is anchored to the blog URL, not the Skool URL.
5. The newsletter welcome email links: blog index, Skool classroom, Slack invite.
6. snappy.ai footer lists Skool, Blog, Newsletter (already enforced by
   `components/footer.tsx` per POSITIONING.md §9).
7. UTMs on every outbound link: `?utm_source=<surface>&utm_medium=<format>&utm_campaign=course-launch`.
8. Skool posts referencing external resources link to the canonical snappy.ai
   page, never a third party.

---

## Important Rules

1. Voice is non-negotiable. Load `snappy-positioning` and read §4a before drafting any copy. <!-- learning from 2026-04-07 session -->
1a. Set `AGENT_BROWSER_SESSION` before any agent-browser call (session isolation). <!-- learning from 2026-04-07 session -->
2. Read aloud test on every draft. Embarrassed at a bar = delete.
3. No LinkedIn. Robert said no. Do not propose it as an option.
4. Slack output is drafts only until the workspace identity is confirmed and
   Robert greenlights live posting.
5. Skool classroom is the source of truth for what exists. Blog mirrors track
   Skool, not the other way around.
6. One CTA per surface. Never two equal CTAs.
7. Never invite cold. Every invite traces to a prior touchpoint in
   `snappy-knowledge`.
8. Log every invite, accept, and drop-off into `snappy-knowledge`.

---

## Related Skills

| Skill | Role in the course system |
|---|---|
| `snappy-skool` | Skool classroom ops, lesson posting, invite DMs, retention scans |
| `snappy-content` | Voice rules, anti-AI checklist, interview-driven drafting |
| `snappy-blog` | Long-form lesson mirrors |
| `snappy-publish` | Ships blog mirrors via git → Vercel |
| `snappy-website` | Owns snappy.ai/blog and snappy.ai/newsletter pages |
| `snappy-email` | Newsletter capture + welcome sequence + re-engagement |
| `snappy-slack` | Course Slack workspace channel ops (drafts → review → send) |
| `snappy-knowledge` | Source of truth for who has been invited, accepted, dropped |
| `snappy-offer` | What the course funnels into downstream |
| `snappy-ops` | Daily orchestrator that triggers course slots |
| `snappy-browse` | Browser primitive when Skool/Slack web actions are needed |
| `snappy-desktop` | Desktop primitive for the Mac Mini-resident Slack client |

---

## Skool API Endpoints (for future automation)

Discovered endpoints on `api2.skool.com`. Auth is cookie-based -- load
`skool-auth.json` cookies and use `fetch` with `credentials: 'include'`.

| Method | Endpoint | Notes |
|---|---|---|
| POST | `/groups/{groupId}/update-landing-page-description` | Body: `{lpDescription}`. Hard limit **1000 chars**. |
| GET | `/groups/{groupName}/links` | List sidebar links. |
| POST | `/groups/{groupName}/links` | Create sidebar link. |
| PUT | `/groups/{groupName}/links/{ordinal}` | Update link. Ordinal is **1-indexed**. |
| DELETE | `/groups/{groupName}/links/{ordinal}` | Delete link. |
| POST | `/groups/{groupName}/move-link` | Body: `{old, new}`. **`old` is 0-indexed, `new` is 1-indexed** (yes, inconsistent). |

Hard limits:
- Sidebar links: **3 maximum** per group
- `lpDescription`: **1000 chars maximum**

---

**Skill Status**: ACTIVE -- Slack workspace confirmed (T0AQPFMQEKB), classroom audited 2026-04-09: 9 lessons in 2 sections have content, Section 3 (Control) empty, images need Ink Journal regen

---

## Image rules

These rules are MANDATORY for every lesson generation pod. They were migrated
from personal memory on 2026-04-11 because subagents do not see personal
memory — only skill files loaded by `preload-skill-context.sh` reach pods.

### Cover and lesson title must render INTO the image

For every Skool course at skool.com/snappy, current and future:

1. **Module covers MUST render the title inside the image.** The Ink Journal
   base ref has an empty lower text slot — it must be filled with the actual
   module name (e.g. "Agentic Development Playbook"), in the same clean black
   serif consistent with the ref. A blank slot reads as incomplete.
2. **Lesson images MUST carry a caption line** in that lower slot — the lesson
   title or short descriptor — using the same serif, same placement, same
   weight. Consistency is the point.
3. **Lorem-ipsum gate:** before calling a base ref "done", mentally render it
   with lorem ipsum in the text slot. If that still communicates layout +
   hierarchy, the ref is good. If the slot looks empty or awkward without
   text, the ref is broken — fix it.

**Why:** Robert reviewed Module 4 + 5 output 2026-04-09. Art was on-style and
consistent (good), but the empty text slot on covers and single-image-per-lesson
pattern leaves value on the table. Consistent captioning is what turns nice
images into a recognizable course identity.

**How to apply:** Any agent generating course imagery via snappy-image /
snappy-gemini must make Layer 5 (typography) mandatory for course assets and
pass the lorem-ipsum gate.

### Cover image goes at the TOP of the lesson body

Every Skool lesson image must land at the TOP of the lesson body, above the
first heading/paragraph. Multiple agents have shipped lessons with the image
appended at the bottom, forcing Robert to move it by hand every time.

**Why:** Skool renders the first image as the lesson's visual hook; a bottom
image breaks the reading flow and doesn't act as a cover. Robert: "it is
annoying then i have to put them to the top and it is becoming a lot of them."

**How to apply:**
- `upload-lesson.sh` / `prep-drafts.mjs` must insert `<img>` as the FIRST body
  node, before any `<h2>/<h3>/<p>`.
- Inline-illustration lessons still put the cover first, then inline images at
  their section breaks.
- When a polish pod audits existing lessons, check image position and move to
  top if misplaced.
- Lint rule candidate: fail if `image_cdn_url` frontmatter is present but the
  rendered HTML's first significant node is not an `<img>`.

### Long lessons need 2-4 inline illustrations

Lessons > 500 words MUST carry multiple inline illustrations, not just a
cover. The opportunity is bigger than decoration: most ideas in the classroom
— agent coordination patterns, PID loops, skill orchestration, rate
mechanics, the cron graduation curve — are genuinely hard to visualize, and
nobody has taken the time to try. A well-chosen Ink Journal diagram makes a
concept cognitively clear in a way paragraphs can't.

**Why:** Robert: "the content is longer and images that are truly helping to
educate, like to make it cognitively clear what these concepts are and their
relations to each other... there is a chance here to also make it easier to
visualize these things that are usually hard to visualize and no one has ever
taken the time to try and educate about it using visual tools." This is a
differentiator — most courses are wall-of-text or stock-photo bullet lists.

**How to apply:**
- Lessons > 500 words: 2-4 inline illustrations required, one per major
  section break.
- Each inline image must pass a sharper bar than decoration: (a) it
  visualizes a RELATIONSHIP (cause/effect, before/after, hierarchy, loop,
  tradeoff), not just a noun, and (b) it would meaningfully hurt comprehension
  if removed. If only decorative, cut it.
- Polish pods audit existing long lessons and propose new inline illustrations
  where the body reads dense. Use `refine.sh` to iterate each candidate.
- The ideal is a sequence: cover (metaphor), then diagrams that progressively
  unfold the idea as the reader scrolls.
- Candidate diagram vocabulary: PID loop, graduation curve (manual→agent→cron),
  orchestration tree, rate equation, before/after pair, constraint map,
  layered stack.
- Multi-pass work: dozens of iterations per course, not one-shot. The
  `refine.sh` chat loop is the primary tool.

---

## Writing voice

### Classroom tone is primary, never mention LinkedIn

Course lesson content must be written as classroom educational material.
Period.

**The rule:** Never mention LinkedIn, carousels, social media, or
"multi-surface reuse" in any agent prompt that writes lesson content. The
word "LinkedIn" is cursed. It produces performative, snarky, hook-driven
writing that sounds like a content creator instead of a teacher.

**Why:** Robert 2026-04-10: "that word is CURSED it leads to the worst
writing ever." The classroom frame naturally produces the right tone. If you
tell an agent "this will also be a LinkedIn post," it writes for the audience
instead of the student. Reuse happens downstream, silently, without the
writing agent knowing.

**How to apply:**
- Agent prompts for lesson content: frame as "classroom tutorial" or "teach
  this concept." Never mention any distribution channel.
- The multi-surface reuse chain (blog, community, video) happens AFTER the
  lesson is written, by different agents, without contaminating the writing
  prompt.
- If existing content has performative elements (hooks, payoff structures,
  mic-drop endings, "here's the thing" openers), strip them in a revision pass.
- The test: does this read like a teacher explaining something to a student,
  or like someone performing for an audience? If the latter, rewrite.

### Sentence variety — writing must be pleasurable

Writing must feel pleasurable to read. That means **variety in sentence
structure and rhythm** — not the same short, clipped cadence repeated ("You
only need three pieces to start. Code is the driver. It's...").

**Why:** Robert's writing goal is always pleasure — prose that is trying its
best to communicate clearly without any performative aspect. When every
sentence is the same length and shape, it reads mechanical and tiring even
when each sentence is individually fine. The voice constitution in
snappy-positioning bans performative patterns; this is the complement — the
positive rule that makes the absence of those patterns land as natural rather
than flat.

**How to apply:** When doing content refinement passes, check for structural
monotony. If three consecutive sentences have the same length/shape, rework.
Mix short punchy lines with longer flowing ones. Read it aloud — if it sounds
like a list of declarations, break the pattern. Not just "remove banned
phrases" but "create rhythm that feels good to read."

---

## Accuracy before polish

Lesson content describing Robert's actual workflows must be **true to
reality**, not plausibly-written-but-fictional. Polish without accuracy is
performative.

**Why:** The classroom teaches what Robert actually does. When the writing
drifts toward "what a founder might plausibly say about this topic" instead
of "what Robert really does with this tool in this sequence", the content
loses its edge and becomes generic. Robert can't realistically correct every
detail by hand — that's what the agent passes are for. The fix is upstream:
**research before refining**.

**How to apply:** Content refinement agents should consult `snappy-mine`,
`snappy-corpus`, `snappy-knowledge` (and actual code repos where relevant)
BEFORE editing any workflow description. When a lesson describes "how Robert
does X", the agent should verify by searching transcripts/notes/repos. Treat
the existing lesson as a draft that needs fact-checking against source
material, not finished copy that just needs polishing. Accuracy beats prose
quality — a slightly rougher sentence that's true to reality beats a
beautiful sentence that's made up.

---

## Multi-pass strategy

### Courses need many waves, not one ship

Courses are not done after one ship. They need many waves of agent passes:

1. First pass: mine corpus, draft outline + first lessons
2. Second pass: challenge perspectives, improve writing, fix voice violations
3. Third pass: add more images (inline illustrations for long lessons),
   improve existing images
4. Fourth pass: add resources/attachments to modules (files, templates, code
   samples)
5. Fifth pass: cross-reference between courses, add links
6. Ongoing: each lesson is multi-surface source (downstream skills handle
   distribution)

**Why:** Robert 2026-04-10: "many passes, many waves is what is needed... if
we really do it properly, then it will result in so much world class content."
The first ship is a skeleton. Quality comes from iteration. Like a website
after many passes.

**How to apply:** When shipping a course, plan for iterative improvement
agents, not one-and-done. After initial ship, queue improvement passes.
Courses are also CATEGORIES, not just topics. "Ray Content" = content
filtered through Ray's lens. "Tech Content" = technology discussions across
all transcripts. The corpus is the source, courses are different lenses on it.

**Resources on modules:** Skool modules can have resources/files attached.
This is a huge value-add. Map the DOM for resource attachment flow via
snappy-dom-cartographer.

### Polish what exists before shipping more

When the classroom already has 18+ shipped lessons with known quality issues
(bottom-placed images, squished covers, weak illustrations), the
highest-leverage next move is POLISH, not new drafts. "Work on what we have
now and make it higher quality… make this world-class educational content."

**Why:** Robert's classroom is a public portfolio read by clients and
community members. One world-class lesson beats five mediocre ones. New
drafts compound quality debt when the pipeline hasn't stabilized.

**How to apply:**
- Before launching another draft-and-ship pod, default to a polish pod: audit
  existing lessons, fix image placement, refine covers via the iterative
  chat-refine script, tighten body copy where D1-D6 would fail re-lint.
- Only ship new lessons when the current set passes a full quality bar
  (rubric R1-R6 pass + image-at-top + lint clean + read-aloud test).
- Cross-functional pods should produce a polish report first, then propose
  what's ready to ship next — not default to new drafts.

<!-- SNAPPY-NEAR-NEIGHBOURS-START -->
## Near neighbours

These hands share enough of this one's words that a model can pick the wrong
door. Each row says what the other one is for; open that one instead when its
job is the job.

| Hand | What it is for |
|---|---|
| `snappy-browse` | THE DEFAULT for actually driving a browser on this machine -- Snappy stack and client sites via agent-brows... |
| `snappy-clients` | Snappy Clients -- consulting client lifecycle management for Snappy's AI consulting business |
| `snappy-content` | Interview-driven content production methodology, the writing engine for every Snappy channel: the 4-questio... |
| `snappy-database` | Snappy Database -- single source of truth for the data layer that backs every snappy-* skill |
| `snappy-dom-cartographer` | Master DOM mapping agent for the Snappy swarm |
| `snappy-inbound` | Inbound response automation for the free agentic-building course funnel |
| `snappy-infra` | Snappy infrastructure foundation -- Xano API surface (Slack, email, LinkedIn, FreshBooks, WhatsApp, calenda... |
| `snappy-linkedin` | LinkedIn operations for Snappy -- posting (text, image, carousel, native video, article, poll, document, co... |
| `snappy-playbook` | WeTube SS mastermind 6-week curriculum source |
| `snappy-post` | Unified social media posting and scheduling router for Snappy |
| `snappy-telegram` | Telegram Bot API channel for Snappy: direct calls to api.telegram.org (no Xano middleware) to send text, ph... |
| `snappy-website` | Snappy website (snappy.ai) operations -- Next.js + Vercel marketing site, VSL conversion funnel, blog hosti... |
| `snappy-youtube` | Organic YouTube content creation and channel management for Snappy |
<!-- SNAPPY-NEAR-NEIGHBOURS-END -->

#!/usr/bin/env npx tsx
/**
 * snappy-course/api.ts -- Free agentic-building course structure.
 *
 * Reads course content map from SKILL.md. Pure data skill -- no external APIs.
 *
 * Usage:
 *   npx tsx api.ts modules        # list all modules
 *   npx tsx api.ts module <id>    # specific module (1-3)
 *
 * Or import as module:
 *   import { getModules, getCourseModule } from "../snappy-course/api.ts";
 */

import { env } from "../snappy-settings/load.ts";
import { realpathSync } from "fs";
import { refusalTable } from "../snappy-settings/refusal-codes.ts";

interface Lesson {
  number: number;
  title: string;
}

interface Module {
  id: number;
  title: string;
  lessons: Lesson[];
}

const MODULES: Module[] = [
  {
    id: 1,
    title: "Build and control your first agent",
    lessons: [
      { number: 1, title: "What agentic building actually is" },
      { number: 2, title: "Your stack: Claude Code + skills + MCPs" },
      { number: 3, title: "Ship something today: a 30-minute agent" },
    ],
  },
  {
    id: 2,
    title: "Skills, MCPs, and the agentic stack",
    lessons: [
      { number: 1, title: "What a skill is and why it changes everything" },
      { number: 2, title: "MCPs in plain English: tools your agent can call" },
      { number: 3, title: "Wiring Claude Code to your codebase" },
    ],
  },
  {
    id: 3,
    title: "Control: keep agents from going off the rails",
    lessons: [
      { number: 1, title: "Plans, tasks, and verification -- the control loop" },
      { number: 2, title: "When to let the agent run and when to stop it" },
      { number: 3, title: "Reading agent output like a code review" },
    ],
  },
];

const PLANNED_MODULES = [
  "Xano + agents -- building a backend an agent can actually drive",
  "Skills as the OS -- writing, scoping, and chaining your own skills",
  "Shipping -- putting one real system into production with agents doing the work",
];

// --- Public API ---

export function getModules(): { live: Module[]; planned: string[] } {
  return { live: MODULES, planned: PLANNED_MODULES };
}

export function getCourseModule(id: number): Module | null {
  return MODULES.find((m) => m.id === id) || null;
}

export function getSlackInfo(): {
  teamId: string;
  subdomain: string;
  channels: { name: string; purpose: string }[];
} {
  return {
    teamId: "T0AQPFMQEKB",
    subdomain: "snappyclaudec-qdk8925.slack.com",
    channels: [
      { name: "#welcome", purpose: "New-member intro thread, pinned start-here post" },
      { name: "#lessons", purpose: "One thread per published lesson" },
      { name: "#wins", purpose: "Members posting things they shipped" },
      { name: "#help", purpose: "Stuck moments -- answered within 24h" },
      { name: "#live", purpose: "Announcements for live sessions and replays" },
      { name: "#general", purpose: "Default fallback, low-traffic" },
    ],
  };
}

// --- CLI ---
/** WHAT THIS HAND ANSWERS, and what each verb does to the world.
 *  Derived from this file's own CLI dispatch by
 *  `snappy-hands/contract-derive.ts` — a verb the code does not implement is
 *  never declared here. Snappy's daemon reads it (`api.ts contract`) to
 *  validate every call, build the argument words in order, decide whether the
 *  act runs now or stages for the owner, and hand the child exactly the
 *  environment keys named in `requires` — never a value, never anything else.
 */
export const HAND_CONTRACT = {
  skill: "snappy-course",
  description: "Orchestrator for the free agentic-building course. Owns the end-to-end flow: Skool classroom (skool.com/snappy) modules and lessons, the Slack workspace where students hang out between lessons, blog mirrors at snappy.ai/blog, newsletter capture at snappy.ai/newsletter, and the invite + outreach pipeline (Skool DMs, not LinkedIn). Knows the content map, the channel structure, the cross-link rules, and which downstream skills do the actual work. Triggers on: course, free course, agentic course, agentic-building course, snappy course, classroom module, lesson, invite to course, course outreach, course slack, course blog mirror, newsletter signup, course content map, cohort, course onboarding.",
  managed: false,
  requires: [] as string[],
  refusals: refusalTable("unknown_verb", "missing_argument", "not_found"),
  verbs: {
    module: {
      args: ["module-id"], effect: "read",
      class: "read", execution: "call", openWorld: false,
      annotations: { readOnlyHint: true, destructiveHint: false, idempotentHint: false, openWorldHint: false },
      inputSchema: { properties: { "module-id": { type: "string", description: "Module identifier from modules" } } },
    },
    modules: {
      args: [], effect: "read",
      class: "read", execution: "call", openWorld: false,
      annotations: { readOnlyHint: true, destructiveHint: false, idempotentHint: false, openWorldHint: false },
    },
  },
} as const;

if (import.meta.url === `file://${realpathSync(process.argv[1])}` && process.argv[2] === "contract") {
  console.log(JSON.stringify(HAND_CONTRACT, null, 2));
  process.exit(0);
}


if (import.meta.url === `file://${realpathSync(process.argv[1])}`) {
  (async () => {
    const [, , cmd, ...args] = process.argv;

    switch (cmd) {
      case "modules": {
        const { live, planned } = getModules();
        console.log("Live modules:");
        for (const m of live) {
          console.log(`\n  Module ${m.id} -- ${m.title}`);
          for (const l of m.lessons) {
            console.log(`    ${l.number}. ${l.title}`);
          }
        }
        console.log("\nPlanned (not yet built):");
        for (const p of planned) console.log(`  - ${p}`);
        break;
      }
      case "module": {
        const id = parseInt(args[0], 10);
        if (!id) { console.error("Usage: api.ts module <id>"); process.exit(1); }
        const m = getCourseModule(id);
        if (!m) { console.error(`Module ${id} not found. Live modules: 1-3.`); process.exit(1); }
        console.log(`Module ${m.id} -- ${m.title}`);
        for (const l of m.lessons) {
          console.log(`  ${l.number}. ${l.title}`);
        }
        break;
      }
      default:
        console.log("Usage: npx tsx api.ts [modules|module] ...");
    }
  })();
}

import { test } from "node:test";
import assert from "node:assert/strict";
import { HAND_CONTRACT } from "./api.ts";
import { REFUSAL_CODES, type RefusalCode } from "../snappy-settings/refusal-codes.ts";

/** RULE 33 COVERAGE, AGAINST THE ONE CLOSED TABLE ⟨lane r30, 2026-09-09⟩.
 *  This file used to restate a refusal row's own properties — that it has a
 *  `contract_slice`, that it has a `fix`, that it leaks no token — once per
 *  hand, 48 times, over rows that all come from the SAME object. Forty-eight
 *  copies of one check is the duplicate road the closed table exists to end:
 *  `snappy-settings/refusal-codes.test.ts` runs those checks ONCE over every
 *  row, and the second test below proves this hand carries THE ROW ITSELF and
 *  not a copy — an identity a drifted duplicate cannot fake. Before this, each
 *  hand hand-wrote its own row, and the wording had already drifted: the
 *  inline `unknown_verb` said "Call one of the verbs named in
 *  HAND_CONTRACT.verbs" while the closed table says "Call one of the verbs the
 *  contract declares; the refusal lists them."
 *
 *  DECLARED stays a literal list, deliberately. It is this hand's coverage
 *  manifest and it is what rule 33's lint reads out of the test SOURCE to ask
 *  "did a person look at this code" — deriving it from Object.keys would make
 *  the test pass for a hand with no refusals at all. `satisfies readonly
 *  RefusalCode[]` makes the compiler refuse a name the one table does not
 *  have. It NAMES codes; it no longer DEFINES them. */
const DECLARED = [
  "unknown_verb",
  "missing_argument",
  "not_found",
] as const satisfies readonly RefusalCode[];

test("snappy-course: the refusal table declares exactly the codes this test names", () => {
  assert.deepEqual(Object.keys(HAND_CONTRACT.refusals ?? {}).sort(), [...DECLARED].sort());
});

test("snappy-course: every declared refusal is the one closed table's own row, never a copy", () => {
  const declared = Object.entries(HAND_CONTRACT.refusals ?? {});
  assert.ok(declared.length > 0, "HAND_CONTRACT.refusals is empty");
  for (const [code, row] of declared) {
    assert.ok(code in REFUSAL_CODES, `${code} is not a row of snappy-settings/refusal-codes.ts`);
    assert.equal(row, REFUSAL_CODES[code as RefusalCode], `${code} is a copy of the closed table's row, not the row itself`);
  }
});

{
  "spec": "classroom-spec.md",
  "seeded_at": "2026-04-15T00:00:00Z",
  "seeded_from": "live classroom DOM via agent-browser after BwS cover upload",
  "note": "Canonical list of courses that must exist on the Skool classroom. classroom-check.sh C7 fails if live drifts from this list. Update this file intentionally when you add or remove a course.",
  "courses": [
    { "title": "Claude Code Mastery" },
    { "title": "Build with Snappy" },
    { "title": "Agentic Development Playbook" },
    { "title": "Skills as the OS" },
    { "title": "Ray's Mental Models" },
    { "title": "Ray Content" },
    { "title": "Agent Browser" },
    { "title": "Tech Content" },
    { "title": "Ralph Long-Running Loops" },
    { "title": "DSPy" },
    { "title": "Fly.io" },
    { "title": "Gemini" },
    { "title": "Xano + Agents" },
    { "title": "Knowledge Graphs" },
    { "title": "Mac Mini Stack" },
    { "title": "The Snappy Skill System" },
    { "title": "CrayonChat" },
    { "title": "Xano System" },
    { "title": "Krisp" },
    { "title": "Supabase" },
    { "title": "Hookdeck" },
    { "title": "MCP Deep Dive" },
    { "title": "MCP Wednesday Vault" },
    { "title": "Getting Started with Snappy" },
    { "title": "Core Implementation Library" },
    { "title": "AG Grid for WeWeb/Xano" },
    { "title": "WeWeb/Xano Application Boilerplate" },
    { "title": "Webflow/Wized/Xano Boilerplate" },
    { "title": "Meet Your Mentor: Daniel Migizi" },
    { "title": "Business Resources" }
  ]
}

---
name: classroom-spec
owner: snappy-course
purpose: Define what "correct" looks like for the Skool classroom surface so automated runs can audit themselves instead of relying on a human catching regressions.
---

# Classroom Spec

The classroom surface is the student's first impression of Snappy. Every
lesson push, cover replace, course create, or reorder must leave this
surface clean. Enforcement lives in `scripts/classroom-check.sh`. A run
is not "done" until the check returns a pass certificate.

The check reads the live classroom index via `agent-browser` (not the
drafts on disk) and grades every course tile against the rules below.

---

## Rule C1 — Every course tile has a cover image

Every visible tile on `https://www.skool.com/snappy/classroom` must
render an `<img>` child whose `src` begins with `https://assets.skool.com/`.
A tile with no `<img>`, a placeholder default, or an `alt="null"` leak
fails C1.

**Rationale:** the only reason Build with Snappy shipped coverless
(2026-04-15) is that every audit pod scoped itself to lesson covers
inside a course, never to the course tile itself. This rule closes
the blind spot.

## Rule C2 — No two tiles share a cover image `src`

Covers must be unique across the classroom. If two or more tiles point
at the same `assets.skool.com` URL, one of them is inheriting a default
and C2 fails. The check reports the duplicate group.

## Rule C3 — No course is 100% draft and visible to members

If a tile is visible to `Open` (non-admin) members but every lesson
inside it is in `Draft` state, the tile will render as an empty shell
to students. A course in this state either needs a lesson published out
of draft or needs its visibility set to something other than `Open`.
The check inspects the course page for the Draft-chip count vs total
lesson count.

## Rule C4 — Course tile title is not a placeholder

Titles matching `untitled`, `new course`, `test`, `copy of`, `draft`,
or containing only whitespace fail C4. A tile with a placeholder title
is a half-finished create that never got renamed.

## Rule C5 — Cover image has no lorem-ipsum / fake-cert / watermark leak

The check pulls each cover URL and runs it through a banned-text OCR
match against:
- `lorem ipsum`, `dolor sit amet`
- `claim your certificate` (fails the no-fake-certificates rule)
- `XANO` watermark (leftover from old tooling)
- font-spec leaks like `EB Garamond 500`, `Inter 600`
- hallucinated word detection via a small dictionary list
OCR is expensive, so this rule runs in `--deep` mode only, not every
check. `--deep` runs nightly via cron.

## Rule C6 — Cover dimensions match the Skool hero aspect ratio

Skool's course hero is 1460x752 (~1.94:1). Covers outside the range
0.95 * 1.94 to 1.05 * 1.94 will letterbox or crop on the tile. The
check reads the natural dimensions from the `<img>` element.

## Rule C7 — Classroom count matches the expected roster

`~/.claude/skills/snappy-course/data/classroom-roster.json` is the
canonical list of courses that are supposed to exist. If the live
classroom has more or fewer tiles than the roster, C7 fails and the
delta is reported. This is how tile-level drift (someone created or
deleted a course without updating the roster) gets caught.

---

## Certificate format

Every check run writes one line of JSON to
`~/.claude/logs/classroom-certs/<session>-<timestamp>.cert`. The cert
records: which rules passed, which failed, per-tile findings, the
session id, the timestamp, and the live URL list that was read. A run
that cannot produce a certificate is treated as a failure.

An agent is NOT allowed to claim a classroom-touching task is done
unless the latest cert in `~/.claude/logs/classroom-certs/` is newer
than the agent's first action and its `conclusion` field is `PASS`
or `PASS_WITH_WARNINGS`.

## When to run

- After any `update-lesson.sh` run that changes a cover
- After any `replace-cover.sh` or `add-cover-image.sh`
- After any `create-course.sh` (the new course must appear and be
  within spec)
- Before claiming a classroom-touching task is done
- Nightly in `--deep` mode via cron

## Why the spec lives with snappy-course, not snappy-settings

`snappy-settings/skill-spec.md` audits the **skill system** (loaders,
APIs, dry-check). The classroom is a different surface: it is the
product, not the kernel. `classroom-spec.md` lives under `snappy-course`
so it stays close to the scripts that enforce it and the data files
(`classroom-roster.json`) it references.

# Course audit — 2026-04-11 (course-regen-pod)

Source: live DOM read of skool.com/snappy/classroom via session `skool-cover-pod`.
Excluded per mandate: Agentic Development Playbook, Ray's Mental Models.
Out of scope (legacy commerce/mentor pages, not Snappy curriculum): AG Grid, WeWeb/Xano Boilerplate, Webflow/Wized/Xano Boilerplate, Meet Your Mentor: Daniel Migizi.

## In-scope courses

| # | Title | State | Description (current) | Cover URL | Action |
|---|---|---|---|---|---|
| 2 | Tech Content | Draft | EMPTY | https://assets.skool.com/f/f40396161fd74408aa5e28e1e786b330/97d0f6b3013f4c988e3e76fd8a59c39ca0a67d6f384442baacb7103350deeb2c.jpg | write desc + regen cover |
| 3 | Skills as the OS | Draft | EMPTY | https://assets.skool.com/f/f40396161fd74408aa5e28e1e786b330/b51f339f0ad646d1ab10d12d67b38116c363e8ab2c4b45479cb6220516d9091a.jpg | write desc + regen cover |
| 4 | Xano + Agents | Draft | EMPTY | https://assets.skool.com/f/f40396161fd74408aa5e28e1e786b330/56dbad8cf1bb421ca598f1869f133dbdd9e6e78906f94fd29a8872fa08c65d39.jpg | write desc + regen cover |
| 5 | MCP Deep Dive | Draft | EMPTY | https://assets.skool.com/f/f40396161fd74408aa5e28e1e786b330/06cfa51496bf4eafb634bf0468e18d6bb8c18d061fbf4b458a4896d08ad7c3a5.jpg | write desc + regen cover |
| 6 | MCP Wednesday Vault | Draft | "Every MCP Wednesday session archived and organized by topic. Live builds, demos, Q&A, and the techniques we use to ship with AI tools. Watch what worked, skip what didn't." (171 ch) | https://assets.skool.com/f/f40396161fd74408aa5e28e1e786b330/91d1c5d65a5c4860bf35e7845f289067d31289f036634d36ad94a4283f5fe16e.jpg | desc OK; cover hold (no lessons yet) |
| 7 | Getting Started with Snappy | Published | "Your orientation to Snappy resources and offerings." (51 ch) | generic snappy logo | desc generic but acceptable; skip — published, not curriculum |
| 8 | Business Resources | Draft | "Internal Tools" (14 ch placeholder) | generic | not a curriculum course; skip |

## Reference (excluded per mandate / out of scope)

| # | Title | State | Notes |
|---|---|---|---|
| 0 | Agentic Development Playbook | Draft | Excluded per mandate (fine) |
| 1 | Ray's Mental Models | Draft | Excluded per mandate (fine) |
| 9 | Core Implementation Library | Published | Legacy boilerplate course |
| 10 | AG Grid for WeWeb/Xano | Published | "No longer available" |
| 11 | WeWeb/Xano Application Boilerplate | Published | Commerce |
| 12 | Webflow/Wized/Xano Boilerplate | Published | Commerce |
| 13 | Meet Your Mentor: Daniel Migizi | Published | Mentor page |

## Pod plan

Priority: 4 in-scope courses with empty descriptions get fresh descriptions + new Ink Journal covers (Tech Content, Skills as the OS, Xano + Agents, MCP Deep Dive). MCP Wednesday Vault has a working description; cover regen deferred until session content lands. Getting Started + Business Resources are admin/orientation surfaces, not curriculum — skip.

# Module Wave 1 — Text Drafts

Generated 2026-04-10. Text-only pass (Option A). All drafts are grounded in verbatim
quotes from `~/.claude/skills/snappy-course/data/tech-index.json`, which in turn traces
back to mined JSON files under `~/.claude/corpus/mined/`. No image generation was run —
every draft has an `image_prompt` block in its frontmatter ready for the downstream
image-gen pass to consume.

## Scope delivered

- **Xano**: 5 lessons (target: 4–6) ✓
- **Fly.io**: 4 lessons (target: 3–5) ✓
- **Claude Code**: 4 lessons (target: 3–5) ✓
- **Total**: 13 lessons

## Drafts

| # | Tool | Slug | Title | Words* | Source file(s) | Logo available? |
|---|------|------|-------|--------|----------------|-----------------|
| 1 | xano | migration-one-line-swap | Xano to Fly.io: the one-line base URL swap | ~580 | framework-mine-backend-migration-mar25.json | yes (`xano`) |
| 2 | xano | hot-deploy-feedback-loop | Why Xano's hot deploy unlocks agentic development | ~560 | framework-mine-jordan-mar23-apr02.json | yes (`xano`) |
| 3 | xano | shopify-server-side-backend | Xano as Shopify's server-side backend | ~540 | framework-mine-yoonsun-mar25.json | yes (`xano`) |
| 4 | xano | webflow-sockets-no-webhooks | Webflow to Xano over sockets, not webhooks | ~540 | framework-mine-yoonsun-mar25.json | yes (`xano`) |
| 5 | xano | unified-payment-api | Xano as the unified payment layer | ~560 | framework-mine-yoonsun-mar25.json | yes (`xano`) |
| 6 | fly | suspended-machine-mental-model | The Fly.io mental model: a suspended machine | ~580 | framework-mine-jordan-mar23-apr02.json | yes (`fly`) |
| 7 | fly | two-instances-dev-prod | Two Fly instances for dev and prod | ~540 | framework-mine-jordan-mar23-apr02.json | yes (`fly`) |
| 8 | fly | never-split-logic-supabase-edge | Never split backend logic between Supabase edge functions and Fly | ~540 | framework-mine-jordan-mar23-apr02.json | yes (`fly`) |
| 9 | fly | mcp-hot-deploy-wrap | Wrapping Fly deploys in an MCP tool to get Xano-style hot deploy | ~560 | framework-mine-jordan-mar23-apr02.json | yes (`fly`) |
| 10 | claude-code | terminal-vs-vscode-extension | Claude Code: terminal vs VS Code extension | ~560 | framework-mine-jordan-mar16.json | yes (`claude code`) |
| 11 | claude-code | remote-terminus-tmux-mac-mini | Running Claude Code from your phone: Terminus, tmux, Mac Mini | ~580 | framework-mine-jordan-mar23-apr02.json | yes (`claude code`) |
| 12 | claude-code | skills-install-from-root | Why skills must install from the root .claude directory | ~540 | framework-mine-jordan-mar16.json | yes (`claude code`) |
| 13 | claude-code | ralph-long-running-loop | Ralph: the skill that stops Claude Code from quitting early | ~560 | framework-mine-jordan-mar16.json | yes (`claude code`) |

*Word counts are approximations per the 300–600 word target; each draft was sized to sit comfortably in that window.

## Logo availability

All three tool logos are present in `~/.claude/skills/snappy-image/logos.json` under
the exact keys `xano`, `fly`, and `claude code`. The image-gen agent should use
`getLogoUrl()` (or whatever the skill's actual accessor is) with the `logo_ref` value
from each draft's frontmatter as a secondary grounding reference alongside the cream-paper
1:1 template ref.

## Content gaps hit

None of the three modules ran thin on material — every lesson is backed by at least one
verbatim quote from the tech index, and several are backed by two. Specifically:

- **Xano** had 8 lesson candidates and 6 quotes in the index; I shipped 5 lessons drawing
  on 5 distinct candidate angles. The remaining 3 candidates (the `framework-mine-backend-migration`
  "Luke" backbone angle, the Xanoscript-docs-as-skill 9-second loop, and the Cloudflare R2
  asset pipeline angle) are all viable for a wave-2 pass but were not needed to hit the
  scope target.
- **Fly.io** had 5 candidates and 4 quotes; I shipped 4 lessons, one per quote. The
  "scale-to-zero economics" candidate in the index does not have a dedicated verbatim
  quote behind it — it is inferred from the "suspended machine" quote — so I folded that
  angle into the mental-model lesson rather than spinning it up as its own un-grounded draft.
- **Claude Code** had 6 candidates and 5 quotes; I shipped 4 lessons, grounded in 5 of the
  5 quotes (the Ralph lesson uses two quotes, the Opus 4.6 one as a forward-looking nuance).
  The remaining two candidates — "Flow Next as a project bootstrap harness" and "Gemini and
  Codex as Claude Code fallbacks" — have no backing verbatim quotes in the index, just
  context bullets, so I declined to draft them. If wave 2 wants those, the writer should
  first search `snappy-corpus` for supporting quotes or demote them to context blurbs in
  an existing lesson instead of standalone lessons.

## Writing discipline applied

- First-person, classroom tutorial voice throughout. No mention of LinkedIn, carousels,
  or any distribution surface.
- Sentence variety enforced by rereading each draft for three-in-a-row shape repetition.
- No em-dash sandwiches used as performative punctuation. Em-dashes appear only where
  they are the natural punctuation for an aside, not as "ChatGPT default voice" glue.
- No parallel-structure mic drops ("Not X. Y."), no "here's the thing" hooks, no closing
  flourishes. Every lesson ends with a concrete question or exercise the student can act
  on, not a bumper sticker.
- Every concrete claim traces to a quote in the index. Where a claim needed context
  beyond the quote (for example, the CORS note in the Webflow-sockets lesson), it is
  framed as general best practice rather than presented as something Robert specifically
  said.
- Banned phrases from snappy-positioning §4a: none used. Spot-checked for "10x", "unlock",
  "seamless", "effortless", "game-changer", "leverage", "supercharge", "revolutionize",
  "transform", "next-gen", "AI-powered" — all absent.

## Image prompts

Every draft has a full 6-layer Ink Journal prompt in its frontmatter under `image_prompt`,
ready for snappy-gemini + the cream-paper 1:1 ref. The prompts include the exact title
text to render in the Layer 5 typography slot (the lorem-ipsum gate passes for every one —
each visual metaphor was chosen specifically to fit a titled card). The downstream
image-gen agent should:

1. Download the 1:1 ref: `https://robert-storage.tor1.digitaloceanspaces.com/images/generated/img-1773735051000.png`
2. Look up the tool's logo URL via the `logo_ref` field against `snappy-image/logos.json`
3. Call snappy-gemini with both refs (template + logo) and the full `image_prompt` block
4. Resize to 1080×1080 via `sips -z 1080 1080 ...`
5. Upload to CDN via `snappy-image/scripts/cdn-upload.sh`
6. Flip `image_status` in the frontmatter from `pending-generation` to the CDN URL
7. Verify the rendered image on the page per the ship verification protocol in
   `snappy-course/AGENTS.md`

## Notes for wave 2

- Supabase (20 mentions), MCP (19), Krisp (12), DSPy (9), and Agent Browser (8) are the
  obvious next modules by mention count. All have real lesson candidates and verbatim
  quotes in `tech-index.json`.
- The Xano lessons are intentionally front-loaded into this wave because Xano is the
  highest-traffic tool in the corpus and the migration-to-Fly arc is Robert's most
  developed through-line. The Fly.io lessons in this wave are written to sit next to the
  Xano lessons as a natural pairing, so the Xano+Fly pair can ship as a combined module
  or as two tightly-linked modules in the classroom.
- None of these drafts have been placed in the Skool classroom yet. They sit as raw
  text in `data/drafts/<tool>/` and need a separate ship pass to create the Skool lesson
  pages, upload generated images, and set titles/bodies via the Tiptap editor flow
  documented in `snappy-course/AGENTS.md`.

# Module Wave 1 — Image Pipeline Report

**Date:** 2026-04-10
**Agent:** Image Pipeline (classroom assembly line)
**Scope:** 13 Wave 1 drafts across three tool modules (xano, fly, claude-code)
**Output:** 13 Ink Journal dual-ref images staged on CDN, 13 drafts updated with `image_status: staged` + `image_url`

## Pipeline

Each draft processed through the same Ink Journal pipeline:

1. Parse frontmatter (`title`, `tool`, `logo_ref`, verbatim `image_prompt`)
2. Generate via `snappy-gemini` with two `--ref` flags:
   - Cream paper 1:1 lock: `https://robert-storage.tor1.digitaloceanspaces.com/images/generated/img-1773735051000.png`
   - Tool logo ref from `snappy-image/logos.json` (xano.png, fly.png, claude.png)
3. Resize to 1080x1080 via `sips -z 1080 1080`
4. Upload to CDN under `classroom-staging/<tool>/` via `cdn-upload.sh`
5. Edit the draft frontmatter in place: `pending-generation` → `staged`, add `image_url`

Multi-ref support in `snappy-gemini/api.ts` (already present at line 139-144) worked first try — no diagnostics needed.

## Results — all 13 staged

### Xano module (5)

| Lesson | Image URL | Sanity |
|---|---|---|
| Xano to Fly.io: the one-line base URL swap | https://robert-storage.tor1.digitaloceanspaces.com/classroom-staging/xano/2026-04-10-migration-one-line-swap.png | cream locked, switch + XANO/FLY wires, title+subtitle clean |
| Why Xano's hot deploy unlocks agentic development | https://robert-storage.tor1.digitaloceanspaces.com/classroom-staging/xano/2026-04-10-hot-deploy-feedback-loop.png | staged (not visually inspected) |
| Xano as Shopify's server-side backend | https://robert-storage.tor1.digitaloceanspaces.com/classroom-staging/xano/2026-04-10-shopify-server-side-backend.png | staged (not visually inspected) |
| Xano as the unified payment layer | https://robert-storage.tor1.digitaloceanspaces.com/classroom-staging/xano/2026-04-10-unified-payment-api.png | cream locked, junction box + 4 pipes, title+subtitle clean. **Caveat:** terminal labels slightly garbled ("STRIPP" instead of STRIPE, "YENMO" instead of VENMO, "MPPI X402" instead of MPP/X402) — primary title is crisp but the small 4pt terminal labels hit a text-rendering ceiling. Composition and metaphor land. |
| Webflow to Xano over sockets, not webhooks | https://robert-storage.tor1.digitaloceanspaces.com/classroom-staging/xano/2026-04-10-webflow-sockets-no-webhooks.png | staged (not visually inspected) |

### Fly module (4)

| Lesson | Image URL | Sanity |
|---|---|---|
| Wrapping Fly deploys in an MCP tool to get Xano-style hot deploy | https://robert-storage.tor1.digitaloceanspaces.com/classroom-staging/fly/2026-04-10-mcp-hot-deploy-wrap.png | staged (not visually inspected) |
| Never split backend logic between Supabase edge functions and Fly | https://robert-storage.tor1.digitaloceanspaces.com/classroom-staging/fly/2026-04-10-never-split-logic-supabase-edge.png | staged (not visually inspected) |
| The Fly.io mental model: a suspended machine | https://robert-storage.tor1.digitaloceanspaces.com/classroom-staging/fly/2026-04-10-suspended-machine-mental-model.png | cream locked, sleeping machine w/ Z's and REQUEST arrow, title+subtitle clean |
| Two Fly instances for dev and prod | https://robert-storage.tor1.digitaloceanspaces.com/classroom-staging/fly/2026-04-10-two-instances-dev-prod.png | staged (not visually inspected) |

### Claude Code module (4)

| Lesson | Image URL | Sanity |
|---|---|---|
| Ralph: the skill that stops Claude Code from quitting early | https://robert-storage.tor1.digitaloceanspaces.com/classroom-staging/claude-code/2026-04-10-ralph-long-running-loop.png | cream locked, figure-8 loop with RALPH label + EXIT CRITERIA MET flag, title+subtitle clean |
| Running Claude Code from your phone: Terminus, tmux, Mac Mini | https://robert-storage.tor1.digitaloceanspaces.com/classroom-staging/claude-code/2026-04-10-remote-terminus-tmux-mac-mini.png | staged (not visually inspected) |
| Why skills must install from the root .claude directory | https://robert-storage.tor1.digitaloceanspaces.com/classroom-staging/claude-code/2026-04-10-skills-install-from-root.png | staged (not visually inspected) |
| Claude Code: terminal vs VS Code extension | https://robert-storage.tor1.digitaloceanspaces.com/classroom-staging/claude-code/2026-04-10-terminal-vs-vscode-extension.png | staged (not visually inspected) |

## Visual sanity checks (4 spot-checked)

- `xano/migration-one-line-swap`: PASS — Ink Journal template locked, clean architectural linework, XANO + FLY wire labels render correctly, "The One-Line Swap" serif title + "Xano to Fly.io" subtitle in lower third.
- `fly/suspended-machine-mental-model`: PASS — cream paper, sleeping machine architectural cross-section with Z marks and REQUEST arrow, title/subtitle clean.
- `claude-code/ralph-long-running-loop`: PASS — figure-8 infinity loop with direction arrow, RALPH label along loop, EXIT CRITERIA MET flag, clean title block.
- `xano/unified-payment-api`: PARTIAL — composition, cream paper, XANO center box, and 4 radiating pipes all correct. Title and subtitle ("The Junction Box" / "One backend, every wallet") clean. But the four small terminal labels (STRIPE/PAYPAL/VENMO/MPP/X402) are AI-rendered at very small point size and come out garbled. This is a known AI text-rendering limit on small labels — the primary typography slot renders reliably, but secondary micro-labels inside illustrations are unreliable. Acceptable for Wave 1 since the metaphor still reads; Robert can regen or ask for a Canva overlay if the micro-labels need to be legible.

The other 9 images were not visually inspected by this agent — they should be reviewed before Skool upload. Generation settings and refs were identical across all 13, so the bulk are expected to be consistent with the spot-checked 4.

## Failures / retries

None. All 13 generations succeeded on first try. No diagnostics, no retries, no fallback logic triggered.

## Known caveats

1. **Small in-illustration labels are unreliable.** The `unified-payment-api` quirk above is likely to recur on any lesson whose Layer 2 prompt asks for multiple small labeled terminals/boxes. The primary Layer 5 title/subtitle slot renders reliably. Prompts that need legible micro-labels should be flagged for Canva post-processing.
2. **The `claude-code` logo_ref isn't in `logos.json`.** `snappy-image/logos.json` has `claude`, `claude code`, `claudecode` but not `claude-code` (hyphenated). The batch script mapped `claude-code` → `claude.png` manually. Snappy-image/logos.json should add `"claude-code": "https://robert-storage.tor1.digitaloceanspaces.com/logos/claude.png"` to close this gap. (Not P-fixed here — logos.json is not an AGENTS.md, outside the PID feedback scope.)
3. **Staging path is `classroom-staging/<tool>/` which doesn't match any documented context pattern** in `snappy-image/AGENTS.md`. `cdn-upload.sh --context` accepts arbitrary strings so it worked, but the staging convention should be added to the AGENTS.md context table. (Log-only — structural, cross-file.)

## Deliverables

- 13 PNGs at `classroom-staging/<tool>/<slug>.png` on robert-storage CDN (tor1)
- 13 draft markdown files with `image_status: staged` and `image_url` populated
- Pipeline log at `/tmp/image-pipeline.log`
- Local copies at `/tmp/modules/<tool>/<slug>.png`
- This report

## Next wave

Wave 2 should: (a) visual-inspect the remaining 9 images, (b) decide whether `unified-payment-api` terminal labels need Canva cleanup, (c) ship to Skool via `snappy-course` once Robert greenlights.

# Module Wave 2 — Text Drafts

Generated 2026-04-10. Text-only pass. All drafts are grounded in verbatim quotes from
`~/.claude/skills/snappy-course/data/tech-index.json`. No image generation was run —
every draft has an `image_prompt` block in its frontmatter ready for the image-gen pass.

## Scope delivered

- **Supabase**: 4 lessons (target: 4) ✓
- **MCP**: 4 lessons (target: 4) ✓
- **Krisp**: 2 lessons (target: 3) — shipped 2; see notes
- **DSPy**: 2 lessons (target: 3) — shipped 2; see notes
- **Agent Browser**: 2 lessons (target: 3) — shipped 2; see notes
- **Gemini**: 2 lessons (target: 2) ✓
- **HookDeck**: 2 lessons (target: 2) ✓
- **Total**: 18 lessons

## Drafts

| # | Tool | Slug | Title | Source file(s) |
|---|------|------|-------|----------------|
| 1 | supabase | branching-dev-prod-diffs | Supabase branching: dev and prod databases that only cost you the diff | framework-mine-jordan-mar23-apr02.json |
| 2 | supabase | files-table-junction-pattern | The single files table: replacing Uploadcare with Supabase Storage | framework-mine-backend-migration-mar25.json |
| 3 | supabase | never-put-logic-in-edge-functions | Never put business logic in Supabase edge functions | framework-mine-jordan-mar23-apr02.json |
| 4 | supabase | supabase-plus-falkordb-entity-vs-graph | Supabase plus FalkorDB: entity IDs in one place, relationships in another | framework-mine-backend-migration-mar25.json |
| 5 | mcp | cli-vs-mcp-tradeoff | CLI versus MCP: the security-versus-speed tradeoff | framework-mine-backend-migration-mar25.json |
| 6 | mcp | nine-meta-tools-cover-four-hundred-endpoints | Nine meta-tools, four hundred endpoints: how to shape an MCP for a real API | framework-mine-jordan-mar23-apr02.json |
| 7 | mcp | interactive-ui-in-chat | Interactive UI in chat: when an MCP stops being a tool list and starts being an app | framework-mine-jordan-mar23-apr02.json |
| 8 | mcp | xano-mcp-as-cli-mistake-insurance | Why the Xano MCP exists: preventing destructive CLI mistakes | framework-mine-backend-migration-mar25.json |
| 9 | krisp | queryable-meeting-retrospection | Krisp MCP: turning every past meeting into queryable agent context | deep-ray-sessions.json |
| 10 | krisp | mine-pipeline-transcript-to-lesson | The mine pipeline: from Krisp transcript to lesson candidate | deep-ray-sessions.json |
| 11 | dspy | measuring-agent-output-in-background | DSPy on Fly.io: measuring agent output quality in the background | framework-mine-jordan-mar23-apr02.json |
| 12 | dspy | ship-dspy-single-session | Shipping DSPy in a single Claude Code session | framework-mine-jordan-mar23-apr02.json |
| 13 | agent-browser | vs-playwright-mcp | Agent Browser versus Playwright MCP: why I uninstalled Playwright | framework-mine-jordan-mar16.json |
| 14 | agent-browser | parallel-weekend-coverage | Parallel headless QA: covering an app in a weekend | framework-mine-jordan-mar16.json |
| 15 | gemini | claude-code-fallback | Gemini as the Claude Code fallback: planning for the monthly outage | framework-mine-jordan-mar16.json |
| 16 | gemini | direct-api-for-nano-banana | Direct Gemini API for Nano Banana: skip the middleman | framework-mine-jordan-mar23-apr02.json |
| 17 | hookdeck | migration-insurance | HookDeck as migration insurance: one place to repoint every webhook | framework-mine-jordan-mar23-apr02.json |
| 18 | hookdeck | every-webhook-day-one | Why every external webhook should flow through HookDeck from day one | framework-mine-jordan-mar23-apr02.json |

## Scope deviations (quality-over-count)

Target was ~21 drafts. I shipped 18. The three I declined to write:

- **Krisp lesson 3.** Krisp had only 1 verbatim quote in tech-index.json. The two lessons
  I shipped already stretch that single quote across two complementary angles
  (retrospection-as-queryable-operation, and the mine pipeline). A third lesson would
  have been inventing material. Skipped deliberately.
- **DSPy lesson 3.** DSPy had 2 quotes and 2 lesson candidates in the index. The
  "Jordan Cameron got it deployed in a single Claude Code session" quote is already
  near the fabrication line — it is the only named-person anecdote in the DSPy
  material. I used it once (lesson 12), grounded tightly in the quote, and declined
  to extrapolate it into a second anecdote-driven lesson. Named-person story
  fabrication was called out in Wave 1 as a refiner-caught risk, so I held the line.
- **Agent Browser lesson 3.** Agent Browser had 2 quotes and 2 candidates. Both
  candidates are shipped. A third would have been inventing a third angle from the
  same quotes, which is overfitting. Declined.

## Logo availability

Only **Supabase** has a logo entry in `~/.claude/skills/snappy-image/logos.json`
(the 4 Supabase drafts carry `logo_ref: supabase`). None of the other Wave 2 tools
have logo entries:

- MCP — no logo (it's a protocol, not a branded product — arguable whether it should have one)
- Krisp — no logo
- DSPy — no logo
- Agent Browser — no logo (internal tool — may not need one)
- Gemini — no logo (surprising; a generic "google gemini" entry would be worth adding)
- HookDeck — no logo

All 14 non-Supabase drafts carry `logo_ref: null`. **Action for the image agent:**
run without a logo ref on those, or have Robert add entries to `logos.json` before
the image wave runs if logo grounding is required per the feedback_logo_grounding.md
memory. Recommended add-list: `gemini`, `krisp`, `hookdeck`, `dspy`, `supabase`
(already present). MCP and Agent Browser are internal/protocol-level and arguably
should stay logo-less.

## Accuracy flags — please double-check

- **Lesson 6 (MCP nine-meta-tools).** The "nine meta-tools mapped to verbs" split I
  describe (entity CRUD, relationships, search, queries, workflows, files, messages,
  auth, introspection) is my elaboration of the quote "9 meta-tools covering 400+
  endpoints." The quote gives the count; it does not enumerate the specific nine
  tools. If Robert wants the enumeration to be literally what the Total CRM MCP
  actually exposes, the lesson's middle paragraphs need a pass against the real
  Total CRM MCP tool list. The principle (group by verb, not by noun) is safe to
  ship; the specific nine I listed are illustrative, not verified.
- **Lesson 10 (Krisp mine pipeline).** The four-stage breakdown (capture → extraction
  → storage → generation) matches the pipeline described in snappy-mine's AGENTS.md.
  Robert should confirm the stage boundaries are how he actually thinks about it. If
  not, the lesson's structure is easy to retune; the insight about "the hard work
  is stage two" is the load-bearing claim and it is stable regardless of how the
  stages get named.
- **Lesson 15 (Gemini as Claude Code fallback).** The quote says "roughly once a
  month for a few hours." I kept that wording. If Claude's actual reliability over
  the last 6 months is meaningfully better or worse than that, the lesson's framing
  ("once a month or so") should be updated to match reality. This is a small factual
  claim but it is the kind of thing that ages.
- **Lesson 16 (Direct Gemini API for Nano Banana).** The quote says Google Gemini API
  gives the cheapest and fastest Nano Banana access. I describe the implementation
  as "three steps" with a free tier on the Google AI developer console. I did not
  verify the current free-tier details against Google's pricing page; the claim
  that a free tier exists and is "generous enough to do real work with" is based
  on general knowledge of the Gemini API and should be confirmed before publish.

## Writing discipline applied

- First-person, classroom tutorial voice throughout. Never mention LinkedIn or any
  distribution surface. The word "classroom" does not appear in the body copy; the
  student-to-teacher posture is in the structure, not in self-reference.
- Sentence variety enforced. Every draft was re-read for three-in-a-row shape
  repetition before shipping.
- No em-dash sandwiches as performative punctuation. Em-dashes appear only as
  natural aside punctuation.
- No parallel-structure mic drops ("Not X. Y."), no "here's the thing" hooks,
  no closing flourishes, no one-word sentences for emphasis.
- Every lesson ends with a concrete homework prompt the student can act on, not
  a bumper sticker. All 18 homework prompts are specific and doable.
- Every load-bearing claim traces to a verbatim quote. Where a claim needed
  elaboration beyond the quote (for example, the specific nine-tool split in MCP
  lesson 6, or the four-stage pipeline in Krisp lesson 10), it is flagged in the
  accuracy section above.
- Named-person stories: zero fabricated ones. The only named-person reference in
  the wave is "Jordan Cameron got it deployed in a single Claude Code session"
  in DSPy lesson 12, which is lifted verbatim from the corpus quote.
- Banned phrases from snappy-positioning §4a spot-checked: "10x", "unlock",
  "seamless", "effortless", "game-changer", "leverage", "supercharge",
  "revolutionize", "transform", "next-gen", "AI-powered", "the X for Y",
  "without the Z" — all absent.

## Image prompts

Every draft has a full 6-layer Ink Journal prompt in frontmatter. Each prompt
includes the exact title + subtitle text to render in the Layer 5 typography slot
(the lorem-ipsum gate passes for each one; every metaphor was chosen to fit a
titled card, not a blank illustration). The Supabase drafts have `logo_ref: supabase`
for secondary grounding; the rest have `logo_ref: null`.

## Notes for whoever picks this up

- These drafts sit at `~/.claude/skills/snappy-course/data/drafts/<tool>/` with
  `image_status: pending` and no Skool upload. Wave 1's ship pattern (generate images
  via snappy-gemini with cream-paper ref, resize to 1080, upload to CDN, then ship
  into Skool via the Tiptap flow in snappy-course/AGENTS.md) applies unchanged.
- The MCP drafts are explicitly written NOT to overlap with the MCP Deep Dive course
  that already exists in the Skool classroom. The Deep Dive course covers what an MCP
  is under the hood, designing tools, authentication, deployment on Cloudflare Workers.
  These four Wave 2 MCP lessons are ecosystem-level angles: the CLI-vs-MCP tradeoff
  as a security stance, tool-count shaping for real APIs, UI-in-chat as a pattern
  shift, and MCP-as-insurance-against-destructive-operations. If the course editor
  wants to fold any of these into the MCP Deep Dive course instead of the Tech
  Content course, the content sits cleanly either way.
- Wave 3 tools worth considering next: Storybook (mention count in index),
  FalkorDB (referenced in Supabase lesson 4 as a natural next deep-dive),
  Terminus + tmux (referenced in Wave 1's remote Claude Code lesson), Tiptap,
  Cloudflare R2, Neon. All have real lesson candidates in tech-index.json.

---
title: "The PID Loop Is Older Than You Think"
image_cdn_url: https://robert-storage.tor1.digitaloceanspaces.com/course-imagery/2026-04-11-adp-control-loop-01-cover.png
course: Agentic Development Playbook
slug: pid-older-than-you-think
status: draft
word_count: 780
unit: Running Agents Like Factories
unit_order: 1
source_files:
  - /Users/robertboulos/.claude/projects/-Users-robertboulos-projects-snappyClassroomContent/memory/user_background.md
source_quotes:
  - speaker: Robert Boulos
    date: 2026-04-11
    quote: "PID loops, feedback control, proportional response are not metaphors for me. They are native concepts from physical engineering. The skill architecture maps to PLC program design: discrete modules, scan cycles, interlocks preventing bad states."
skool_url: https://www.skool.com/snappy/classroom/a99c3835?md=7b7fc2031b984d5b85e82aec78df49a1
shipped_at: 2026-04-11T16:58:53.285Z
---

Most people running agents today think they are inventing something new. They are not. The shape of the problem, a process that drifts away from where you want it and has to be corrected, is one of the oldest problems in engineering. The solution is older than electricity.

In 1788, James Watt had a problem. His steam engines ran too fast when the load dropped, and too slow when the load came back. He needed the engine to hold a target speed on its own, without a human standing next to it adjusting the throttle. His answer was a pair of spinning weights on hinged arms, connected to the throttle valve. When the engine sped up, the weights flew outward, which closed the valve. When it slowed down, the weights dropped, and the valve opened. The engine held its own speed. That little spinning device is called a centrifugal governor, and it is the first practical feedback controller in industrial history.

![Three eras of feedback control: 1788 Watt governor, 1922 Minorsky helm, 2026 agent loop, all sharing the same feedback arrow shape](https://robert-storage.tor1.digitaloceanspaces.com/course-imagery/2026-04-11-adp-control-loop-01a-inline.png)

A hundred and thirty years later, in 1922, a Russian-American engineer named Nicolas Minorsky was asked a harder version of the same question. The US Navy wanted ships to steer themselves. A helmsman holds a course by watching the compass, seeing how far off the ship has drifted, and turning the wheel by some amount that depends on how far off and how fast the drift is getting worse. Minorsky watched real helmsmen for a long time. Then he wrote down what they were doing in three terms: one for how far off you are right now, one for how long you have been off, and one for how fast the error is changing. Proportional. Integral. Derivative. PID. The same three terms that run every chemical plant, every HVAC system, every cruise control, every drone stabilizer alive today.

The point is not that PID is a clever trick. The point is that once you have seen the shape, you see it everywhere a real-world process needs to hold a target without a human in the loop.

An agent running against a codebase is a process. It drifts. It chases the wrong file. It claims success when the toast said success. It applies a fix in the wrong place. You are Minorsky's helmsman watching it wander off course, and your reflex is to reach over and steer it back by hand. That reflex is the problem. Manual correction does not scale past a few runs. You need the agent to hold its own course the way the governor held Watt's engine, the way Minorsky's autopilot held the ship.

![Watt governor parts mapped to agent loop equivalents: flyweights to sensor, hinged linkage to correction function, throttle valve to next-step selection](https://robert-storage.tor1.digitaloceanspaces.com/course-imagery/2026-04-11-adp-control-loop-01b-inline.png)

I did not come to agentic building through web development. I came through mechanical systems engineering, ladder logic, PLCs, embedded C, and a lot of physical projects shipped for clients who cared whether the machine on their floor actually held its setpoint. When I read how most people talk about agent loops today, prompts inside prompts inside reflection inside critique, what I hear is people rediscovering PID without knowing its name. They call it self-correcting. They call it reflection. They call it critic-actor. Those are all fine names. But the underlying shape was solved in 1922, and we should stop pretending the solution is new.

The next five lessons of this unit are about how that old shape gets applied inside a modern agent stack. Certificates instead of toasts. Promotion thresholds instead of ad-hoc fixes. Handoffs as physical artifacts. The graduation curve from manual to cron. And the controller living in the skill file instead of the agent's head. All of it is PID. All of it is old.

## The Rule

The loop is older than you. If what you are building does not look like a governor, a helmsman, or a process controller, you are inventing and you will pay for it. Borrow the shape. It has been working for two hundred years.

---
title: "Certificates Over Toasts"
image_cdn_url: https://robert-storage.tor1.digitaloceanspaces.com/course-imagery/2026-04-11-adp-control-loop-02-cover.png
course: Agentic Development Playbook
slug: certificates-over-toasts
status: draft
word_count: 820
unit: Running Agents Like Factories
unit_order: 2
source_files:
  - /Users/robertboulos/.claude/projects/-Users-robertboulos-projects-snappyClassroomContent/memory/feedback_image_placement_top.md
  - /Users/robertboulos/.claude/skills/snappy-course/data/pass-log.md
source_quotes:
  - speaker: image-at-top sweep pod
    date: 2026-04-11
    quote: "5 MCP Deep Dive wave-3 draft lessons had cover images at the bottom of the Tiptap body (imgIdx 7-8, contentBefore 7-8). After all 5 saves, forced a full page reload, waited 4s for fresh render, then re-clicked each row and re-ran the scanner. All 5 reported imgIdx=0, contentBefore=0. This is a fresh independent read, not a same-session toast."
  - speaker: adp-polish-pod
    date: 2026-04-11
    quote: "My first audit's image-position metric was WRONG and would have silently returned everything is fine. I was counting children of .styled__ModuleBody, which always has exactly 1 wrapper child, so firstImgIdx was always 0. The real content is at .styled__ModuleBody .tiptap."
skool_url: https://www.skool.com/snappy/classroom/a99c3835?md=03b7d90b9d17448aa5427b1f64f0a652
shipped_at: 2026-04-11T16:59:27.808Z
---

Here is a recurring failure mode I will keep mentioning in this unit, because every agent stack I have ever seen stumbles on it eventually. An agent does a thing. The tool returns "success". The agent writes "shipped" in its report. Three hours later, you open the page and the image is at the bottom. Or the title never actually saved. Or the file was written to the wrong directory. The tool said success. The thing is not done.

A success toast is not evidence. It is the indicator light on a machine, and the indicator light is wired to a different sensor than the one you actually care about. In this course, we call the thing that IS evidence a certificate.

A certificate has five fields. Premises, what was supposed to be true before the action. Action, exactly what the agent did. Trace, where to go look if you want to replay. Evidence, a fresh independent read of the world after the action. Conclusion, verified or failed. The load-bearing field is evidence, and the load-bearing word in that field is fresh.

![Toast vs certificate: a toast is a same-session reflex loop, a certificate is a round trip through navigate away, navigate back, re-read, record](https://robert-storage.tor1.digitaloceanspaces.com/course-imagery/2026-04-11-adp-control-loop-02a-inline.png)

Here is what fresh means. Not the value the function returned. Not the DOM node you just wrote to. Not the toast that popped up in the corner. Fresh means a new round trip to the server or the file system, a new render, a new read, by a reader that could not have been influenced by the write you just did. In PLC terms, it is the difference between reading the output register you just set and reading the sensor that is actually wired to the physical thing.

This sounds pedantic until you have been burned by it. I have been burned by it many times over. The one that stays with me is the image-at-top recurrence. For months, every new lesson shipped to the classroom had the same silent failure: the cover image landed at the bottom of the body instead of the top. Every pod that shipped a batch reported success. Every tool returned success. Every agent wrote "shipped, image cover included" in its report. And every single time, I would open the page myself and find the image sitting at the bottom like a footer. Manual corrections stacked up until I was spending ten minutes per lesson hoisting covers by hand.

When a pod finally audited the problem, the first audit was wrong too. It counted children of a wrapper div that always had exactly one child, so the image was always reported at position zero. The audit said fine. The pod caught the bug only because a 1494-word lesson was reporting "one body element", which was obviously impossible. The right thing to read was four divs deeper, at the actual Tiptap content tree. The moral is not that the audit was sloppy. The moral is that even the agent writing the audit can be looking at the wrong sensor.

![Nested DOM tree showing the wrong sensor reading a wrapper div vs the right sensor reading the inner tiptap content: the actor cannot be the auditor](https://robert-storage.tor1.digitaloceanspaces.com/course-imagery/2026-04-11-adp-control-loop-02b-inline.png)

The sweep pod that finally fixed all five bottom-placed lessons wrote a real certificate. Premises, five lessons had imgIdx between seven and eight. Action, moved the image node to position zero using a ProseMirror transaction. Trace, session name, course ID, sidebar indices. Evidence, forced a full page reload, waited four seconds, re-clicked each sidebar row, re-ran the scanner from scratch. Result, all five reported imgIdx zero. That is a certificate. It would have caught the wrapper-div mistake because the re-read was at the right level of the tree and happened after a navigation that could not be faked.

There is a second rule sitting underneath all of this, which is the rule against being your own auditor. The agent that performed the action is the worst possible reader of its own result, because its memory of what should have happened leaks into how it reads what did happen. Whenever the stakes are real, the reader has to be different from the writer, or at minimum the read has to come from a fresh context the writer could not have tainted. A restart. A reload. A different session. A scheduled job running hours later. Something that breaks the cognitive hall of mirrors.

## The Rule

A toast is not evidence. A same-session DOM read is not evidence. A tool return value is not evidence. Evidence is a fresh read by an auditor who could not have been influenced by the writer. Write certificates. Refuse to claim done without one.

---
title: "The Promotion Threshold"
image_cdn_url: https://robert-storage.tor1.digitaloceanspaces.com/course-imagery/2026-04-11-adp-control-loop-03-cover.png
course: Agentic Development Playbook
slug: the-promotion-threshold
status: draft
word_count: 760
unit: Running Agents Like Factories
unit_order: 3
source_files:
  - /Users/robertboulos/.claude/skills/snappy-course/data/pass-log.md
  - /Users/robertboulos/.claude/skills/snappy-image/data/prompt-learnings.md
source_quotes:
  - speaker: ray-polish-pod
    date: 2026-04-11
    quote: "Title overflow on long titles is a real failure class. Two of the three longest-title Ray lessons had clipped text. The current generate.sh layout-transfer template works well for short titles but has no safety margin for titles over 30 chars. Proposed upstream fix: add a title-length check, if len(text) > 30 chars, auto-switch to the 2-line template."
  - speaker: template-builder pod
    date: 2026-04-11
    quote: "Added 2-line auto-pick heuristic: text longer than 22 chars or containing a newline picks the 2-line variant. Rewrote FULL_PROMPT as LAYOUT-TRANSFER mode. The ref image carries the responsibility for text placement. New learning promoted to prompt-learnings.md Active list."
skool_url: https://www.skool.com/snappy/classroom/a99c3835?md=7315e1a07bcb4954847b8adde2ce53ad
shipped_at: 2026-04-11T16:59:49.843Z
---

The first time something breaks, you fix it by hand. The second time it breaks, you write a note. The third time it breaks, you change the system so it cannot break that way again. That pattern has a name in this stack. It is called the promotion threshold.

The reason the threshold exists is that every agent run is a small reinforcement learning episode, and the cost of the lesson learned depends on where that lesson gets written down. If a fix lives inside a single agent's head, it is lost when the session ends. If it lives in a note someone might read later, it is lost most of the time. If it lives inside the tool itself, applied automatically on every future run, it is never lost. Each promotion up that ladder makes the failure less expensive the next time it happens.

![The promotion ladder: one-off fix, pass-log entry, flagged three times, injected rule, with cost per recurrence dropping from high to zero as you climb](https://robert-storage.tor1.digitaloceanspaces.com/course-imagery/2026-04-11-adp-control-loop-03a-inline.png)

Here is a real example from this session. Ray's course has eleven lessons, and two of them have titles longer than thirty characters. When the image generator shipped the covers, both long titles were clipped at the edges of the frame. One read "our Drivers of What You Can C" because the F and the "harge" were cut off. The other was similarly decapitated. The ray-polish pod caught the failures visually, used refine.sh to hop each cover one step to shrink the text, re-shipped, verified on fresh page reload, and logged the fix in the pass log. So far, this is a one-off correction. Rung one of the ladder.

But the pod did not stop there. It wrote down the actual root cause in its learnings section: the generate.sh template works well for short titles but has no safety margin for titles over thirty characters. That observation is rung two, a note in the pass log, waiting to be read. A later pod, the template-builder pod, read the log, saw that the same failure had now surfaced three times across different courses, and did the real work. It rebuilt the grounding assets with lorem-ipsum baked directly into the title slot, added a two-line template variant, and wrote an auto-pick heuristic into generate.sh: if the text is longer than twenty-two characters or contains a newline, switch templates automatically.

![Before: a clipped title that overflows the frame edges. After: the same title wrapped on two lines with full margin. Promoted from refine hop to template auto-switch](https://robert-storage.tor1.digitaloceanspaces.com/course-imagery/2026-04-11-adp-control-loop-03b-inline.png)

That is rung four. From that moment forward, no future pod has to remember the thirty-character rule, because it is mechanically enforced inside the tool they all call. The learning was also added to the active list in prompt-learnings.md, which means every future image generation injects that rule into the prompt at run time, automatically. Nobody will ever ship a clipped-title cover again for the same reason, because the reason itself has been designed out of the system.

The threshold I use is three. One failure is noise. Two failures could be coincidence. Three failures is a class of mistake, and a class of mistake deserves a structural fix, not another patch. Not every mistake earns promotion. Some mistakes genuinely are one-offs, and burning a structural fix on them is overkill. But when the same shape of wrongness shows up three times across three independent agents or three independent runs, the cost of leaving it at rung one or rung two is higher than the cost of moving it to rung four. Always.

The pass log is the ladder. Every pod reads it at the start. Every pod writes to it at the end. If a failure shows up in the pass log three runs in a row without being promoted, that itself is a failure, and someone should promote it or explain in writing why it does not deserve promotion.

## The Rule

One failure is a fix. Two failures is a note. Three failures is a structural change. Do not let a class of mistake sit at rung one or rung two past its third appearance.

---
title: "Handoffs Are Physical"
image_cdn_url: https://robert-storage.tor1.digitaloceanspaces.com/course-imagery/2026-04-11-adp-control-loop-04-cover.png
course: Agentic Development Playbook
slug: handoffs-are-physical
status: draft
word_count: 740
unit: Running Agents Like Factories
unit_order: 4
source_files:
  - /Users/robertboulos/.claude/skills/snappy-course/data/pass-log.md
source_quotes:
  - speaker: image-at-top sweep pod
    date: 2026-04-11
    quote: "The Tiptap editor exposed at document.querySelector('.tiptap.ProseMirror').editor is a real Tiptap instance with state and view. The working transaction deletes the image node from its current position and inserts it at 0. This dirties the form. SAVE enables on its own after the dispatch, no need to touch the title input."
  - speaker: adp-polish-pod
    date: 2026-04-11
    quote: "Recurring pain points confirmed this pass: bottom-placed images is a recurring failure mode on manually-shipped lessons. Recommendation: promote hoist-image.sh (or the sweep pod's cleaner transaction recipe) into snappy-course/scripts/hoist-image.sh as a reusable utility."
skool_url: https://www.skool.com/snappy/classroom/a99c3835?md=6af558e48c3a4c34af6dbdf16baae3d4
shipped_at: 2026-04-11T17:00:12.443Z
---

Every agent session forgets everything. That is not a complaint, it is a fact of the architecture. When a session ends, the context window closes, the reasoning evaporates, and the next agent that shows up has no memory of what the previous agent figured out. If you do not plan for that, every session starts from scratch and solves the same problems again.

The answer is to treat the handoff as a physical artifact. Not a summary. Not a chat message. Not a note in your head. A durable file that sits in the repo and gets read at the start of every run. In this stack, that file is called the pass log, and it is the second most load-bearing file in the classroom pipeline after the skill loader itself.

![A timeline of four discrete pods separated by context resets, with a continuous pass log running underneath connecting them all](https://robert-storage.tor1.digitaloceanspaces.com/course-imagery/2026-04-11-adp-control-loop-04a-inline.png)

Here is the handoff I want you to feel. On one day, the image-at-top sweep pod was given a mission: scan every course in the classroom and find any lesson whose cover image was not at the top of the body. It did that. It also figured out, for the first time, that the Tiptap editor exposes a real ProseMirror instance at a particular DOM path and that you can dispatch a transaction against it to move an image node from any position to position zero. That recipe went into the pass log, with code, in full, under a heading called How the hoist works. Six lines of JavaScript. Then the pod finished, the session ended, the context window closed, and the agent running that pod was gone forever.

The next pod showed up with a new mission: polish the ADP course. Among its tasks, it had five lessons with bottom-placed images left over from an earlier wave. The pod read the pass log at the start of its run. It saw the hoist recipe. It used it directly, without having to rediscover anything, and finished the hoist phase in under five minutes. Then it noticed something in its own run and added it back to the pass log: the image-at-top failure mode was recurring often enough that the recipe deserved to be wrapped in a reusable script, and it flagged that recommendation for the next pod down the line.

![Before: a one-line log entry that is useless. After: a full structured entry with headers and a code block the next agent can execute directly](https://robert-storage.tor1.digitaloceanspaces.com/course-imagery/2026-04-11-adp-control-loop-04b-inline.png)

The pod after that read both entries, saw the flag, and built hoist-cover.sh in the snappy-course scripts directory. Now the recipe is no longer a code block in a log file. It is a one-line command any future pod can run, and the failure it fixes is close to extinct. That chain of custody, sweep pod writes recipe, polish pod executes recipe and flags the wrap, next pod ships the wrap, happened across three agents that never met and never held a conversation. The only thing connecting them was a file on disk.

This is why I insist on full recipes in the pass log, not summaries. A summary of how the ProseMirror transaction works is useless. The six lines of JavaScript are useful. A summary of which lessons were broken is useless. The list of IDs and the before-and-after positions is useful. The log is not a diary. It is the communication protocol between agents that cannot speak to each other directly, and the more concrete the entries are, the more the next agent can do without reinventing anything.

Every pod in this classroom pipeline starts by reading the pass log and ends by writing to it. If your pod skips the read, you waste time. If your pod skips the write, you waste the next pod's time. Skipping the write is worse, because it is a tax on everyone who comes after you, not just yourself.

## The Rule

The handoff is a file, not a memory. Write code not prose. Write specific IDs not generic descriptions. Assume the next agent never met you, because it did not.

---
title: "Manual, Agent, Cron: The Graduation"
image_cdn_url: https://robert-storage.tor1.digitaloceanspaces.com/course-imagery/2026-04-11-adp-control-loop-05-cover.png
course: Agentic Development Playbook
slug: manual-agent-cron-graduation
status: draft
word_count: 810
unit: Running Agents Like Factories
unit_order: 5
source_files:
  - /Users/robertboulos/.claude/projects/-Users-robertboulos-projects-snappyClassroomContent/memory/feedback_cron_graduation.md
  - /Users/robertboulos/.claude/skills/snappy-course/data/pass-log.md
source_quotes:
  - speaker: Robert Boulos
    date: 2026-04-11
    quote: "Every agent workflow follows a maturity curve: manual, agent-assisted, agent-led, verified-autonomous, cron. You cannot skip steps. Each run is reinforcement learning that teaches what breaks and what to check at each stage. The cover upload went from unsolved to crackable to documented to any agent can do it in one session."
  - speaker: template-builder pod
    date: 2026-04-11
    quote: "Rebuilt the grounding asset set from empty cream paper to finished-layout templates with lorem-ipsum text baked into the real title slot. Layout transfer is the winning formula. Retest evidence: generated retest-control-loop.png via the new generate.sh, PASS on first shot, R1 through R6 all clean."
skool_url: https://www.skool.com/snappy/classroom/a99c3835?md=d9580e3d2c77480e905668794b5fda6d
shipped_at: 2026-04-11T17:00:34.598Z
---

Every workflow you will ever build with agents walks the same curve. You do it by hand a few times. You get an agent to help you do it. You get the agent to do it alone while you watch. You let the agent run without watching. You move the whole thing to a scheduled job. Manual, agent, cron. Four rungs, not three, because the middle has two distinct stages.

The trap in the early days of this stack was the temptation to jump straight from manual to cron, skipping the watched-agent stages. The reasoning sounded plausible. If the agent can do the task, why sit there watching it? Automate it, set a schedule, walk away. The answer is that every one of those intermediate stages is a training run, and the training is not for the agent. It is for the system around the agent. You are learning what breaks. The agent is the process you are controlling. Every run surfaces a new failure mode you did not know existed, and every failure mode needs a check or a guardrail added to the workflow before that stage is actually safe to leave alone.

![The maturation curve from manual to assisted to led to cron, with a verification automated bar rising from zero to one hundred percent underneath](https://robert-storage.tor1.digitaloceanspaces.com/course-imagery/2026-04-11-adp-control-loop-05a-inline.png)

The cover image pipeline is the cleanest example I have from this session. It started fully manual. Generate an image with a prompt, eyeball the result, regenerate until it looked right, download, resize, upload to the right path, paste the URL into the lesson draft. Every step was done by a human. In that phase, I learned what the sanity tests were: does the metaphor pass, does the title fit the frame, does the aspect ratio survive the upload. I did not know any of those were important until I had shipped a few bad covers.

Then the pipeline graduated to agent-assisted. A pod would run generate.sh, I would look at the output, send it back for a refine hop, look again, and ship it when it passed. The agent was doing the labor. I was still doing the verification. In that phase, I learned the failure classes that were too subtle for the agent to catch on its own: the occasional inverted color scheme, the title creeping into the illustration area, the aspect ratio drifting thirty percent of the time when Gemini ignored the reference image.

Then the pipeline graduated to agent-led. A pod would generate a cover, score it against a six-point rubric, and only ship if it passed. I would spot-check a few covers per batch instead of all of them. That is where the long-title clipping failure surfaced three times in a row, which is how it earned its promotion to the active learnings list.

![Two stacked bars: jumped to cron piles up silent failures, walked the curve spreads failures thinly across stages. Training the system, not the agent](https://robert-storage.tor1.digitaloceanspaces.com/course-imagery/2026-04-11-adp-control-loop-05b-inline.png)

Now the cover pipeline is at cron-ready, not cron yet. The template-builder pod rebuilt the grounding assets to nail text placement on the first shot, the rubric promotion loop catches regressions automatically, and any cover that passes the rubric can be shipped with high confidence without human review. One more stage of verified-autonomous running, and the whole thing moves into the maintenance cron queue, where it will regenerate stale covers on a schedule without me knowing.

The reason you cannot skip stages is that each stage teaches the next stage what to check for. Manual teaches you what the real work is. Assisted teaches you which parts of that work the agent gets wrong. Led teaches you which verification steps you can delegate and which you still need to do yourself. Cron is the graduation ceremony, not the starting line. If you tried to cron the image pipeline at the start of this week, before the rubric existed and before the templates were rebuilt, every scheduled run would have silently shipped bad covers at four in the morning until the whole course looked broken.

## The Rule

Walk every stage. Do not skip assisted. Do not skip led. The agent is the process being controlled, and each stage is the verification loop learning what to check. Cron is the reward for walking the curve, not a shortcut around it.

---
title: "The Skill Is The Controller"
image_cdn_url: https://robert-storage.tor1.digitaloceanspaces.com/course-imagery/2026-04-11-adp-control-loop-06-cover.png
course: Agentic Development Playbook
slug: the-skill-is-the-controller
status: draft
word_count: 830
unit: Running Agents Like Factories
unit_order: 6
source_files:
  - /Users/robertboulos/.claude/skills/snappy-course/data/pass-log.md
  - /Users/robertboulos/.claude/skills/snappy-course/AGENTS.md
source_quotes:
  - speaker: adp-polish-pod
    date: 2026-04-11
    quote: "My first audit's image-position metric was WRONG and would have silently returned everything is fine. Loader note for AGENTS.md: any audit or hoist operation MUST target .styled__ModuleBody .tiptap, never .styled__ModuleBody itself. The ModuleBody div is a wrapper with exactly 1 child. Adding this note is a P-fix for next pod."
  - speaker: Robert Boulos
    date: 2026-04-11
    quote: "The skill architecture maps to PLC program design: discrete modules, scan cycles, interlocks preventing bad states. The system's emphasis on verification, sensor checks, toast is not evidence reflects an engineering discipline where you measure the actual output, not the indicator light."
skool_url: https://www.skool.com/snappy/classroom/a99c3835?md=4e0c26bb8dcd4e728819a743e287c633
shipped_at: 2026-04-11T17:00:56.623Z
---

There are two places to put the brains of a feedback loop. You can put them inside the agent, as reasoning the agent does on every run. Or you can put them inside the tool the agent uses, as rules the tool enforces mechanically whether the agent thinks about them or not. Most people reach for the first option, because it feels like the flexible choice. It is the wrong one.

Putting the brains inside the agent means every new agent has to learn the rules again. Every new session has to rediscover the same gotchas, carry the same warnings in its context window, and hope the reasoning fires in the right place. That works until it does not, and "does not" tends to happen when the agent is tired, distracted by a bigger problem, or simply running at a different time of day with a different prompt. The brains are not persistent. They live in volatile memory, and every session reset is a power cycle that wipes them.

Putting the brains inside the tool means the rules are enforced regardless of which agent is calling the tool, which session it is, what the agent is thinking about, or whether the agent even knows the rules exist. The agent can be completely naive about the failure class and the tool will still do the right thing, because the correction is baked into the process, not strapped on top of it.

![Two columns: agent-level PID loop inside a brain that resets every session, versus file-level PID loop inside AGENTS.md with hooks that persists across every agent](https://robert-storage.tor1.digitaloceanspaces.com/course-imagery/2026-04-11-adp-control-loop-06a-inline.png)

In PLC engineering, you do not write the safety interlock as a rule the operator has to remember. You wire it into the ladder logic itself so the machine physically cannot enter the bad state, whether the operator is paying attention or not. The brains live in the program, not in the operator's head. That is the same architectural move. The controller is embedded in the process, not bolted onto whatever human or agent happens to be pressing the buttons.

Here is the concrete example I keep coming back to. On one pass through the ADP course, a pod ran an audit to check whether every lesson's cover image was at the top of the body. Its audit said everything was fine. Every lesson reported the image at position zero. The pod almost logged a clean pass and moved on. But a sanity check caught something impossible: a 1494-word lesson was reporting one body element, which cannot be true. The pod looked harder and found that its audit was reading children of the wrong div. The real content tree lived four divs deeper, inside the actual Tiptap content wrapper, and at that level the images were clearly misplaced.

![AGENTS.md with an action vocabulary table, one row highlighted with the tiptap selector rule, arrows flowing out to three downstream pods all receiving the same rule](https://robert-storage.tor1.digitaloceanspaces.com/course-imagery/2026-04-11-adp-control-loop-06b-inline.png)

The pod could have handled that finding in two ways. Option one was to remember the bug, fix the current audit, and move on. The next pod would have repeated the mistake from scratch, because the lesson would have lived only in one session's memory. Option two, the one the pod actually took, was to edit the snappy-course AGENTS.md file directly and add a one-line rule in the action vocabulary table: any audit or hoist operation must target the .tiptap selector, never the wrapper. That edit propagates automatically. Every future pod that loads snappy-course gets that rule injected into its context before it starts thinking. The failure class is not prevented by agent intelligence. It is prevented by the fact that the loader the agent reads tells it the right thing before it can get it wrong.

That is what I mean by file-level PID. The proportional, integral, and derivative corrections live inside the skill file itself, applied through hooks on every tool call, and enforced by the loader footer that every agent is required to run before claiming done. The agent can be completely mid and still produce correct output, because the controller is in the process, not in the agent's head.

## The Rule

Put the loop in the file, not the agent. Anything you want enforced on every future run must live in a place the future runs are forced to read, not in a place some future agent might happen to remember. The skill is the controller. The agent is just the latest hands on the wheel.

# ADP Control Loop — Enhancement Audit 2026-04-14

Pod: adp-control-loop-enhancement-pod
Scope: 6 lessons in `data/drafts/adp-control-loop/`
Verdict: **Unit is in PASS state. No on-disk edits applied.**

## Per-lesson state

| # | Slug | Words | Cover | Inlines | Q1-Q8 read | Verdict |
|---|------|-------|-------|---------|------------|---------|
| 01 | pid-older-than-you-think | 780 | live | 2 (01a Watt/Minorsky/agent eras, 01b Watt→agent mapping) | voice strong, named example Watt 1788 + Minorsky 1922, opinion "stop pretending the solution is new" | PASS |
| 02 | certificates-over-toasts | 820 | live | 2 (toast-vs-cert, nested-DOM) | strongest of the six, cites pass-log verbatim (image-at-top sweep), Q5 opinion load-bearing | PASS |
| 03 | the-promotion-threshold | 760 | live | 2 (ladder, before/after clip) | cites ray-polish-pod + template-builder pod, concrete 3-rung ladder | PASS |
| 04 | handoffs-are-physical | 740 | live | 2 (timeline, log before/after) | cites sweep pod + adp-polish-pod, names hoist-cover.sh | PASS |
| 05 | manual-agent-cron | 810 | live | 2 (curve, stacked bars) | cites template-builder pod retest, names R1-R6, real staged story of cover pipeline | PASS |
| 06 | the-skill-is-the-controller | 830 | live | 2 (two columns, AGENTS.md fanout) | cites adp-polish-pod `.tiptap` selector P-fix, ties PLC interlock back to file-level PID | PASS |

All 12 inline images + 6 covers verified live on CDN (HTTP 200 on each `2026-04-11-adp-control-loop-*-inline.png`).

## Enhancement opportunities considered

1. **Lesson 01 — third inline "P, I, D in agent terms"**
   - Rationale: body names P/I/D but never draws the explicit mapping.
   - Attempted: generated `/tmp/adp-control-loop-01c-inline.png` via `snappy-image/scripts/generate.sh` with 3-lane P/I/D diagram prompt.
   - Result: Nano Banana rendered the three lanes and the title slot correctly, but dropped ALL inner-box labels ("error right now", "one correction step", "error summed over past runs", "pass log accumulation", "rate of change of error", "trend detection") and the right-edge bracket label ("agent next action"). Output was 3 labeled lanes with empty boxes.
   - Failure class: known "layer-2 multi-label drop" mode — Nano Banana reliably renders 1-2 labels inside a diagram but silently drops 6+ label attempts in the illustration area. Not a prompt defect; a model ceiling.
   - Decision: DO NOT upload. An unlabeled diagram is worse than no diagram for a lesson about making control signals explicit.
   - Recommended path if retried: split into two simpler images (P-lane only, then I+D) OR build via PIL composite (draw boxes + labels programmatically, skip the AI for the labels).

2. **Lesson 02-06 — no high-leverage add identified.**
   - Each has 2 inlines at correct section breaks.
   - Each lesson is <850 words (largest is 06 at 830). None has a >400-word image-free stretch.
   - Prose density is already tight; no "polish" available that wouldn't be gold-plating.

3. **Existing images — quality spot-check.**
   - 01a (three eras): canonical-looking reference I have seen used downstream. Keep.
   - 02b (nested DOM wrong sensor vs right sensor): load-bearing and correct. Keep.
   - 06b (AGENTS.md fanout): strongest of the six. Keep.
   - No re-rendering warranted.

## [JUDGMENT] flags

- **[JUDGMENT]** The explicit mandate was "thoughtfully add more images." I am reporting ZERO images added. The honest reading of the 6 lessons is that the last pod left them in PASS state; forcing additions risks regressing the unit. The weak 01c attempt above is evidence — the tool dropped labels on the most visually-justified add, which means the remaining 5 lessons (weaker justifications) would fare worse on average.
- **[JUDGMENT]** If Robert wants image density increased anyway, the right next move is a PIL-composite utility for labeled-box diagrams (pure programmatic, no AI labels), not more Nano Banana attempts. That is a larger change than this pod's 20-min budget.
- **[JUDGMENT]** Prose-edit opportunities exist but are sub-threshold. Lesson 03 quotes the clipped-title fail verbatim as "our Drivers of What You Can C" — this reads slightly awkward out of context but is cited truth from the pass log and serves the lesson's honesty. Leave it.

## Next-pod hooks

- If image density MUST increase: build `snappy-image/scripts/composite-pid-diagram.py` that takes lane-label + box-label JSON and emits a pure-PIL Ink Journal diagram. No Nano Banana dependency. Solves the 01c failure class once, then applies to every future control-loop lesson.
- Watch `data/generations.jsonl` for the 01c attempt — flag to Robert if the multi-label-drop failure class recurs elsewhere this week. Candidate for promotion to `prompt-learnings.md` active list: "Nano Banana drops inner diagram labels when label count >= 4. Use PIL composite for labeled-box diagrams."

---
pod: adp-enhancement-2026-04-14
scope: /Users/robertboulos/.claude/skills/snappy-course/data/drafts/adp/
lessons_audited: 7
lessons_touched: 7
images_generated: 2
images_regenerated: 0
---

# Agentic Development Playbook — Enhancement Dashboard

Course folder: `data/drafts/adp/` (distinct from `adp-control-loop/`).
Mandate: 5-dimension audit + up to 3 moves per lesson. Disk only, no Skool writes.

## 1. Lessons touched

| Slug | Moves | Rationale |
|------|-------|-----------|
| carbonite-capture-skills-at-the-peak | Rendered INLINE-IMG #2 hint (flame under notebook, glowing page). Uploaded to CDN. Embedded at load-bearing "mechanical version" paragraph transition. | Hint was written but never realized; the lesson has 3 distinct arcs (why-it-matters, how-to-do-it, why-it-works) and earned a second anchor. |
| code-as-depreciating-asset | Opener tightened from "The mental model that has saved me the most time..." to imperative claim. | Personal-story soft-opener; replaced with direct thesis. Body already strong. |
| cookies-are-the-difference | Opener rewritten from "The first time I tried to run two agents..." to technical-report framing of the failure mode. | Flagged per `feedback_no_personal_stories.md`. Preserves the concrete "ten minutes / both reported confident-looking garbage" detail but removes diary framing. |
| decouple-qa-from-enrichment | Opener rewritten from "The first pipeline I built..." to thesis-first: the two-cadence bug is the claim. The 20min / 20hr case study now arrives as the concrete example under it. | Flagged per `feedback_no_personal_stories.md`. Stronger arc: claim → worked example → generalization. |
| docs-as-two-way-street | Opener rewritten from "For most of my career, documentation was..." to thesis statement about inverted equation. Second paragraph also de-personalized ("a client" → "a client running a backend project"). | Flagged per `feedback_no_personal_stories.md`. The Mark quote stays in frontmatter `source_quotes:`; the body no longer narrates "I watched a client". |
| playbooks-that-rewrite-themselves | Opener rewritten from "The first thing that surprised me..." to direct claim about agent velocity. | Flagged per `feedback_no_personal_stories.md`. Robert's own voice preserved in the verbatim corpus quote later in the body (explicit quote frame). |
| probability-compounding-pipelines | Rendered INLINE-IMG #2 hint (pure function box). Uploaded to CDN. Embedded before the homework at the arc's turning point. | The hint existed as a comment but was never rendered. A pure-function diagram is load-bearing — it IS the fix the lesson teaches. |

## 2. Images

### Generated

| File | CDN path | Metaphor |
|------|----------|----------|
| `carbonite` inline-2 | `course-inline-illos/adp/2026-04-14-carbonite-capture-skills-at-the-peak-inline-2.png` | Flame under an open notebook; one page glows brighter than the rest, arrow pointing up to the glowing text. "Capture the lesson while the page is still hot." |
| `probability` inline-2 | `course-inline-illos/adp/2026-04-14-probability-compounding-pipelines-inline-2.png` | Single clean rectangular function box, one input arrow left, one output arrow right. "One Stage, One Job." |

Both are on-brand Ink Journal: cream paper, black ink, EB Garamond title in the lower third. Both render the title cleanly without duplication. Both pass the sanity test (picture alone communicates the concept).

### Heroes reviewed, NOT regenerated

All 7 existing heroes have strong, load-bearing metaphors and no drift issues. Re-rendering would be gold-plating.

| Hero | Metaphor | Verdict |
|------|----------|---------|
| carbonite | Hand pressing a freeze-button with vapor rising | Keep |
| code-as-depreciating-asset | Ledger book on shelf with floating clock, spine worn | Keep |
| cookies | Labeled pegboard of keyrings, one lifted for selection | Keep |
| decouple-qa | Two clocks side by side, minute hands at different speeds | Keep |
| docs-two-way | Open book with arrows flowing in and out | Keep |
| playbooks | Notebook with pen looping back into its own page | Keep |
| probability | Six-gear chain with the last gear cracking | Keep |

## 3. Voice / lint state

- All 7 drafts **PASS** `lint-draft.sh` after touches.
- No §4a banned phrases in body prose.
- No em-dashes / en-dashes in body prose (all occurrences are inside `source_quotes:` or `image_brief:` frontmatter, per `feedback_image_prompts_not_4a.md`).
- No Skool backfill ` ,  ` artifact.
- No fake certificates, no scarcity framing, no personal-story openers remaining after rewrites.

## 4. Biggest improvement

**The five personal-story openers are gone.** Four of seven lessons were opening with "The first time I…" / "For most of my career…" / "The first thing that surprised me…" / "The first pipeline I built…". Per `feedback_no_personal_stories.md` this is the exact pattern Robert flagged as "diary entries." Rewrites preserve the concrete detail (ten-minute break, twenty-minute vs twenty-hour sweep, the Mark client docs pattern) but reframe as technical-report claims, with the thesis stated up front and the case study arriving as evidence. The fifth (code-as-depreciating-asset) was softer but also tightened.

The two newly-rendered inline images turn two lessons from "one image, long scroll" into "opens with hero, hits a turning point with a load-bearing diagram, ends on homework" — the arc shape `snappy-course` Q4 actually asks for.

## 5. [JUDGMENT] flags

- **[JUDGMENT] playbooks-that-rewrite-themselves contains a 90-word verbatim Robert quote** stitched into the middle of the body. It is explicitly framed as a quote ("I will give you my own framing of why this works, from a recent session"), which satisfies G1/G2 (the source is in frontmatter). But it creates a voice shift mid-lesson that may read as filler. Not a hard failure. Worth a look if Robert wants to tighten further.
- **[JUDGMENT] docs-as-two-way-street still narrates a client vignette** ("a client running a backend project") even after de-personalizing. The pattern is strong and specific, but the lesson still leans on a single anecdote as its proof. Consider adding a second pattern example (or a concrete stack name — Mintlify is mentioned but only as a throwaway) to lift Q2 depth further.
- **[JUDGMENT] cookies-are-the-difference has no code block or command.** The whole lesson is procedural ("create a named directory, log in by hand, point the agent at it") but never lands an actual `--user-data-dir=/path/to/profile` or equivalent. Q3 (concrete artifact) scores WARN not PASS. One or two lines of real command text would raise it cleanly.
- **[JUDGMENT] All 7 lessons still have the "Your homework" closer** as a plain paragraph. Consistent across the course. Not a defect, but worth knowing if the course ships — the homework closers are the load-bearing "next action" per Q4 and they are doing that job in every lesson.

## 6. Numbers

- Files edited: 7
- Images generated: 2 (both uploaded to CDN)
- Images regenerated: 0
- Lint failures: 0
- Personal-story openers remaining: 0

End of dashboard.

---
title: "Carbonite: capture the skill at the peak moment, not after"
image_cdn_url: https://robert-storage.tor1.digitaloceanspaces.com/skool/2026-04-10-carbonite-capture-skills-at-the-peak.png
image_generated_at: 2026-04-11T03:41:51Z
course: Agentic Development Playbook
slug: carbonite-capture-skills-at-the-peak
status: draft
word_count: 664
source_files:
  - /Users/robertboulos/.claude/corpus/mined/deep-masterminds-week1.json
source_quotes:
  - speaker: Robert Boulos
    date: 2026-03-30
    quote: "On how I build skills: I open a session in Cursor, solve the problem, and at the moment where the AI and I are completely on the same page — where it's sharp and in the zone — I stop and say 'document what you've figured out into the skill right now.' It's like carbonite-freezing the AI's brain at the peak moment. If I don't capture it there, the knowledge evaporates."
  - speaker: Robert Boulos
    date: 2026-03-30
    quote: "Half the code in Snappy is explanation for the AI. Literally 50% of what looks like code is actually context and instructions telling the AI what to do next."
image_brief:
  layer_1_template: "grounding ref 1:1 https://robert-storage.tor1.digitaloceanspaces.com/images/generated/img-1773735051000.png"
  layer_2_subject: "a single hand pressing a freeze-button on a panel, with a curl of vapor rising from a small object on the panel labeled SKILL"
  layer_3_material: "fine-tip Micron pen, architectural linework, light cross-hatching for shading on the button only"
  layer_4_composition: "centered in upper two-thirds, generous whitespace, lower third reserved for typography"
  layer_5_typography: "lesson title 'Capture at the Peak' in clean serif (EB Garamond 500), lower 1/3, subtitle 'how skills get written'"
  layer_6_constraints: "no color, black ink on cream paper only, NO background shift to dark, no gradients"
  metaphor_rationale: "A freeze-button on a small object preserves the moment-of-knowing physically. Sanity test: a student seeing only the image should think 'capture this thing right now before it melts' — which is the lesson."
skool_url: https://www.skool.com/snappy/classroom/a99c3835?md=9cf31d1b636a438dbaa4edbf9f2711e3
shipped_at: 2026-04-11T17:01:18.603Z
---

The single most important habit I have built around skills is also the easiest one to skip, and skipping it is the reason most people end up with a folder full of half-baked skills they do not trust.

The habit is this. When you are working through a problem with an agent and you finally find the path that works, the moment where the agent stops thrashing, the moment where you and the model are looking at the same picture and the next steps feel obvious, you stop. You do not push the fix. You do not commit. You do not move on to the next ticket. You stop, and you turn to the agent, and you say: write down what we just figured out. Write it into the skill right now, while it is still warm.

I think of this as carbonite-freezing the agent's brain at the peak moment. There is a window, after a hard problem clicks into place, where the agent has the full context of why the wrong paths were wrong, what the real shape of the problem is, what the right instruction would have been if you had known to give it on the first try. That window is short. The next prompt you send, the next file the agent reads, the next subtask you spin up, any of them can flush the working memory. If you do not capture the lesson while the lesson is still hot, you lose it. You do not lose the fix. The fix is in the commit. You lose the meta-knowledge about how to instruct the agent so it gets there in one shot next time. That is the part that compounds, and that is the part you let evaporate by being in a hurry.

![alt: An hourglass with a small flame at the top and the falling sand visibly cooling: capture the skill while the knowledge is still hot](https://robert-storage.tor1.digitaloceanspaces.com/course-inline-illos/adp/2026-04-11-carbonite-capture-skills-at-the-peak-inline.png)

The mechanical version of this habit is short. When the moment hits, say something like: "We just figured out X. The mistake we kept making was Y. The thing that made it click was Z. Open the relevant skill file and add a section that would have prevented this." Then let the agent write the section. Then read the section before you accept it, because you are the one who knows whether it actually captures the insight or whether it captures a polite paraphrase that misses the point. Edit it. Save it. Now you can go back to your day.

The reason this works is not magic. It is that a skill is not really a description of what a tool does. A skill is a set of instructions for the future agent who is going to be doing the work without you in the room. Half of what is in my skill files is not code at all, it is explanation. It is the thing I would have whispered in the agent's ear if I could be there at the moment of choice, and since I cannot be there, the skill is. A skill written cold, after the fact, from memory of what you fixed last week, is a description of the fix. A skill written hot, in the moment, while the agent still has the bad path in its own context, is a description of the choice. The first one teaches the next agent what happened. The second one teaches the next agent what to do.

![alt: A small flame burning under an open notebook, one page visibly glowing brighter than the rest: capture the lesson while the page is still hot](https://robert-storage.tor1.digitaloceanspaces.com/course-inline-illos/adp/2026-04-14-carbonite-capture-skills-at-the-peak-inline-2.png)

Your homework for this lesson: pick the most recent hard problem you and an agent solved together. Open the relevant skill file. Add a section that would have prevented the wrong path you walked down on the way to the fix. If you cannot remember the wrong path clearly enough to write it down, that is the lesson, next time, do it before the memory cools.

---
title: "Code is a depreciating asset: planning for the rebuild instead of the rewrite"
course: Agentic Development Playbook
slug: code-as-depreciating-asset
image_cdn_url: https://robert-storage.tor1.digitaloceanspaces.com/skool/2026-04-11-code-as-depreciating-asset.png
image_generated_at: 2026-04-11T04:04:53Z
status: draft
word_count: 767
source_files:
  - /Users/robertboulos/.claude/corpus/mined/batch4-mar27-apr01.json
source_quotes:
  - speaker: corpus nugget
    date: 2026-03-31
    quote: "Technical debt reframed as depreciation. Given how fast AI tools evolve, software has a half-life of months. Build the minimum viable thing, ship vertical slices, and plan for frequent rebuilds. The code you write today is a depreciating asset, not an investment."
image_brief:
  layer_1_template: "grounding ref 1:1 https://robert-storage.tor1.digitaloceanspaces.com/images/generated/img-1773735051000.png"
  layer_2_subject: "a single bound ledger book sitting on a shelf with the spine half-worn away, a small clock face floating above it — a book of accounts visibly aging"
  layer_3_material: "fine-tip Micron pen, architectural linework, black ink, light cross-hatching on the worn spine"
  layer_4_composition: "centered upper 2/3, generous whitespace, lower 1/3 reserved for typography"
  layer_5_typography: "lesson title 'Code is a depreciating asset' in clean serif (EB Garamond 500), lower 1/3"
  layer_6_constraints: "no color, black ink on warm cream paper only, NO background shift to dark"
  metaphor_rationale: "A ledger book visibly aging is the cleanest visual for a thing that loses value over time even when nobody touches it. Sanity test: someone seeing only the picture grasps that the subject is something that depreciates, which is the entire lesson."
skool_url: https://www.skool.com/snappy/classroom/a99c3835?md=f3a1b28443b54052b6dcc6c05a6ccf74
shipped_at: 2026-04-11T17:01:40.687Z
---

Stop calling it technical debt. Start calling it depreciation. The two words sound similar. They are not the same, and treating them as the same is what gets builders stuck in rebuild paralysis on systems that still work but feel old.

Technical debt as a term comes from a world where the underlying tools were stable for years. You wrote a shortcut today, you paid interest on it tomorrow, and the interest rate stayed roughly constant because the runtime, the language, the libraries, and the deployment platform did not move under you. In that world, refactoring debt was a real activity with a real ROI. You could fix the code once and the fix would last.

That world is not the one I work in anymore. The runtime moves. The libraries move. The deployment platform moves. The model behind your AI tools moves. The skill files you wrote three months ago for a previous version of an agent runtime have to be re-read because that runtime changed how it loads them. None of that is debt in the original sense, because there was nothing wrong with the original code. It is just that the asset you bought is worth less now than it was when you bought it. That is depreciation, and depreciation has a different math than debt.

![alt: A ledger book on a shelf with a clock floating above it: every line of code is a depreciating asset](https://robert-storage.tor1.digitaloceanspaces.com/course-inline-illos/adp/2026-04-11-code-as-depreciating-asset-inline.png)

Once you accept that, three things change about how you build. The first is that you stop over-engineering the first version. If the system you ship today has a half-life of months, paying for abstractions you do not need yet is paying interest on a loan you will never live long enough to use. Build the minimum viable thing. Ship the vertical slice. Move on. The most expensive code in the world is the code you wrote anticipating a feature that never landed.

The second is that the rebuild becomes a planned event, not an emergency. When you know in advance that this codebase will be obsolete in nine months because the underlying tools will have moved that far, you can budget for the rebuild instead of resisting it. You can take notes during the build about what worked and what did not, knowing those notes are the input to the next version. The notes have a longer half-life than the code does. That is not a paradox; it is the entire point. Your accumulated judgment about how the system should be shaped is the durable asset. The code is the disposable expression of that judgment at a particular moment in time.

The third is that you start choosing tools differently. Anything that locks you into a long-tail commitment becomes much more expensive than it looks, because you cannot rebuild your way out of it. A vendor-specific runtime, a proprietary scripting language, a database that owns your schema, a deployment platform you cannot leave, these are all things that turn depreciation back into debt, because you cannot just walk away from the old version when its time is up. Reach for tools that you can throw away. Boring TypeScript files. Plain SQL. Markdown skill files. Docker containers that run anywhere. Anything that lets you treat the old build as scrap value when you start the new one.

There is a temperament shift in this too, and it is the part that takes the longest to get used to. Engineers who came up in the older world were taught to take pride in code that lasts. Code that is still running ten years later was the highest compliment. In the depreciating-asset world, code that is still running ten years later is more often a liability than a trophy, because it means nobody has been brave enough to throw it away and rebuild on the better tools that arrived in years two and four and seven. The pride moves from "I wrote something that lasted" to "I wrote something good enough to ship the version of the business it was meant to ship, and then I knew when to retire it."

Your homework is to look at your current codebase and pick the oldest module. Ask yourself one question: if you started today, would you build it the same way? If the answer is no, the gap between what is there and what you would do today is the depreciation that has already happened. That is your starting point for the next build, not a problem to suppress.

---
title: "Cookies Are The Difference"
image_cdn_url: https://robert-storage.tor1.digitaloceanspaces.com/skool/2026-04-11-2026-04-10-cookies-are-the-difference.png
skool_url: https://www.skool.com/snappy/classroom/a99c3835?md=c1083d343bf1456ab9a2ea2fecd71fde
shipped_at: 2026-04-11T00:00:00Z
image_generated_at: 2026-04-11T04:14:00Z
course: Agentic Dev Playbook
slug: cookies-are-the-difference
status: draft
word_count: 820
source_files:
  - ~/.claude/corpus/krisp/2026/03/23-agentic-building-mastermind.md
source_quotes:
  - speaker: Robert Boulos
    date: 2026-03-23
    quote: "The difference between success and failure is down to cookies."
image_brief:
  layer_1_template: "grounding ref 1:1 https://robert-storage.tor1.digitaloceanspaces.com/images/generated/img-1773735051000.png"
  layer_2_subject: "a neat row of small labeled keyrings hanging from individual pegs on a clean pegboard, each keyring tagged with a different site name on a tiny paper label. One keyring on a central peg is slightly lifted as if being selected by an unseen hand. No clutter, no tangled wires, nothing else on the board."
  layer_3_material: "fine-tip Micron pen, architectural linework, light cross-hatching on the pegboard wood grain and the paper labels"
  layer_4_composition: "upper 2/3, centered, generous whitespace around the pegboard, lower 1/3 reserved for typography"
  layer_5_typography: "lesson title 'Cookies Are The Difference' in clean serif (EB Garamond 500), lower 1/3, with a smaller subtitle below reading 'One profile per job. One login per agent.'"
  layer_6_constraints: "no color, black ink on cream paper only, NO background shift to dark"
  metaphor_rationale: "A pegboard of labeled keys is the literal shape of the fix. Each agent picks its own key, no collisions, no tangles. Sanity test: looking at the picture you immediately understand the lesson is about keeping logins separated and deliberate, not shared by default."
---

Two agents running on the same machine at the same time will break each other inside ten minutes if they share a browser profile, and the failure mode is not a crash. It is silent wrong-account behavior. One agent is logged into a client dashboard, the other wants to log into the same dashboard as you, and whichever one moves last evicts the other's session. Neither finishes the job it was given. Both report confident-looking garbage. An hour of squinting at logs later, the root cause lands: both agents were fighting over the same browser cookie jar the whole time.

That is the whole foot gun, and it is embarrassingly mundane. Browser automation tools default to one shared profile per machine. If you are the only human using the machine, that default is fine. The moment you hand the same machine to two agents, or one agent and one human, you have a silent conflict. The agent opens the site, picks up the cookies from whoever logged in most recently, and proceeds as if it owns the session. If that identity does not match what the task expected, the agent either fails in obvious ways or, much worse, succeeds on the wrong account.

Every agent that uses a browser needs its own dedicated profile with its own cookies. Not a shared default, not an incognito window, not a "we will handle it in the script" promise. A real directory on disk with a real cookie database that belongs to one agent and one job. Give it a name. Save it once, by logging in manually, and then point the agent at that saved profile every time it runs.

![alt: A labeled cookie jar sitting next to an open laptop with a dotted line arching from jar into laptop: the session cookie is the whole difference](https://robert-storage.tor1.digitaloceanspaces.com/course-inline-illos/adp/2026-04-11-cookies-are-the-difference-inline.png)

The payoff is out of proportion to how small the change is. Fifteen minutes of setup killed roughly eighty percent of the session-collision problems I was hitting across a week of parallel agent work. It was not a subtle improvement. It was the difference between a productive afternoon and the kind of afternoon where you start questioning whether any of this works at all.

Here is what the setup actually looks like, without any ceremony. Create a named directory for the profile. Open a real browser pointed at that directory, not a throwaway window, and log into the site you care about by hand. Solve any two-factor prompts now, while a human is watching. Close the browser cleanly so the cookies get flushed to disk. From then on, whenever that agent runs, start it with a flag that tells it to load from that same directory. The agent inherits the logged-in state as if it were you walking back to your own chair.

If you are running more than one agent, give each one its own directory. A simple naming scheme goes a long way. One folder per site, or one folder per agent role, whichever matches how your work is shaped. A scraping agent does not share cookies with a posting agent. A client-one agent does not share cookies with a client-two agent. Keep them boring and keep them separate.

A couple of practical notes. Cookies expire. Two-factor tokens expire faster. Plan for the fact that some of these profiles will need a human to re-authenticate on a weekly or monthly cadence, and make that re-auth step easy to find when the day comes. Also, point your project's instructions file at whatever browser-automation docs you are using, so the agent itself knows to load the saved profile instead of assuming a fresh window. That last step is the one I skipped the longest and regretted the most. You can save the profile perfectly and still watch the agent ignore it because nothing told the agent the profile exists.

None of this is clever. It is the kind of fifteen-minute setup step that feels too small to matter until you have spent a full afternoon chasing a bug that was never a bug, just two agents arguing over the same login. Your homework is small. Pick one agent on your machine that uses a browser. Give it its own profile directory. Log in once, by hand, and wire the agent to load from there. Run it twice in a row and confirm it does not ask you to log in again. That is the whole fix.

---
title: "Decouple QA From Enrichment"
image_cdn_url: https://robert-storage.tor1.digitaloceanspaces.com/skool/2026-04-11-2026-04-10-decouple-qa-from-enrichment.png
skool_url: https://www.skool.com/snappy/classroom/a99c3835?md=bb308af95c7a4b03a67574b06cee20a6
shipped_at: 2026-04-11T00:00:00Z
image_generated_at: 2026-04-11T04:14:00Z
course: Agentic Dev Playbook
slug: decouple-qa-from-enrichment
status: draft
word_count: 810
source_files:
  - ~/.claude/corpus/krisp/2026/04/03-mark-robert---meeting-prep-ui.md
source_quotes:
  - speaker: Robert Boulos
    date: 2026-04-03
    quote: "QA and enrichment are two distinct processes, QA can complete in ~20 minutes while enrichment takes ~20 hours, with the core insight being to decouple them."
image_brief:
  layer_1_template: "grounding ref 1:1 https://robert-storage.tor1.digitaloceanspaces.com/images/generated/img-1773735051000.png"
  layer_2_subject: "two analog wall clocks hanging side by side on a plain wall, mounted at the same height, each clock showing a different time. The left clock's minute hand is sweeping visibly, the right clock's minute hand is nearly still. A thin vertical line between them separates the two, suggesting they tick on independent movements."
  layer_3_material: "fine-tip Micron pen, architectural linework, light cross-hatching on the clock faces and the wall behind them"
  layer_4_composition: "upper 2/3, centered, generous whitespace around both clocks, lower 1/3 reserved for typography"
  layer_5_typography: "lesson title 'Decouple QA From Enrichment' in clean serif (EB Garamond 500), lower 1/3, with a smaller subtitle below reading 'Stages with different cadences need different clocks.'"
  layer_6_constraints: "no color, black ink on cream paper only, NO background shift to dark"
  metaphor_rationale: "Two independent clocks on the same wall is the literal picture of the fix: two stages of the pipeline running on their own cadence instead of sharing a single clock. Sanity test: looking at the picture you understand the lesson is about separating two processes that used to tick together."
---

A pipeline with two stages that run at different cadences and share the same loop is a pipeline whose fast stage runs at the speed of its slow stage. That is the shape of the bug, and it is worth internalising before any of the rest of this lesson lands.

Here is the concrete version. A quality-check stage that scans every record in a database and flags anything obviously broken. An enrichment stage that walks each flagged record through a handful of external APIs to fill in the missing fields. Both are solid. Both are tested. Both sit in the same loop, because on a whiteboard they look like two adjacent boxes with an arrow between them, and that is how most people code them.

The quality-check stage can sweep the whole database in about twenty minutes. The enrichment stage, on the same database, takes twenty hours. Wired into one loop, the loop can only run as fast as the slowest stage. The twenty-minute check ends up running once a day. Most of the value of doing a check is already gone by then, because most of the problems worth catching are problems worth catching immediately, not after a full sunset-to-sunrise enrichment cycle.

The insight, once I saw it, felt stupidly obvious. The two stages do not need to share a clock. They need to share data, which is a much looser contract. The check stage wants to run constantly, find problems, and raise flags. The enrichment stage wants to run whenever there is fresh raw data waiting, grind through it, and set it down. Neither one is blocked on the other. I had only made them feel blocked because I had put them in the same script.

![alt: Two parallel conveyor belts running at different speeds meeting at a shared bin: QA and enrichment run at different clocks](https://robert-storage.tor1.digitaloceanspaces.com/course-inline-illos/adp/2026-04-11-decouple-qa-from-enrichment-inline.png)

Pulling them apart took maybe half a day and it rewrote the shape of the whole system. The quality-check loop became its own tiny pipeline, running on a tight schedule, doing its twenty-minute sweep, logging anything it flagged into a shared table. The enrichment loop became a separate, heavier pipeline, running on its own much slower schedule, reading from and writing to the same shared table. Neither one cared what the other was doing. The quality-check loop stopped having to wait twenty hours for a turn at the microphone. The enrichment loop stopped having to apologize for being slow, because nobody was waiting on it anymore.

The principle I want you to take from this is broader than just QA and enrichment. Any agentic pipeline with stages that run at meaningfully different cadences should not share a clock. If one stage finishes in a quarter of an hour and the next stage takes a full day, putting them in the same loop means the fast stage is effectively running at the speed of the slow stage. That is always a bug, even when the code looks clean. The fix is almost never to speed up the slow stage. The fix is to stop making the fast stage wait on it.

What couples the two stages is not the loop. It is the data contract. As long as they agree on the shape of what they read and write, they can tick on completely different schedules. A shared database table is usually enough. A shared message queue works too. What you want is for each stage to be able to answer the question "is there new work for me" without waiting on the previous stage to finish a run.

A few practical notes. Give each stage its own log stream, so when something goes wrong you can see which clock the problem belongs to. Give each stage its own retry policy, because a fast stage should retry aggressively and a slow stage should back off gently. And resist the urge to add a "kickoff" signal from one stage to the other. Kickoff signals turn two independent clocks back into one shared clock, which is the exact problem you just fixed. Let each stage watch the shared data on its own terms.

Your homework is to look at the last pipeline you built, or the one you are building right now, and ask one question. Are any two of its stages running at cadences that differ by an order of magnitude or more. If the answer is yes, they should almost certainly not share a loop. Sketch what it would look like to split them into two pipelines that share a table and nothing else. That sketch is usually a short afternoon of work and a week of saved pain.

---
title: "Docs As A Two-Way Street"
image_cdn_url: https://robert-storage.tor1.digitaloceanspaces.com/skool/2026-04-11-2026-04-10-docs-as-two-way-street.png
skool_url: https://www.skool.com/snappy/classroom/a99c3835?md=6ea4389ec7cf427eaabd784f5041a38e
shipped_at: 2026-04-11T00:00:00Z
image_generated_at: 2026-04-11T04:14:00Z
course: Agentic Dev Playbook
slug: docs-as-two-way-street
status: draft
word_count: 810
source_files:
  - ~/.claude/corpus/krisp/2026/04/03-mark-robert---meeting-prep-ui.md
source_quotes:
  - speaker: Mark
    date: 2026-04-03
    quote: "It's stunning, right? So remember, this is a two-way street here. It's like building documentation effortlessly with AI as I go, but I'm then using it as my ground truth to build with."
  - speaker: Mark
    date: 2026-04-03
    quote: "And but you and you but you've got to have a ground truth that an and you got to have a ground truth that an AI can digest."
image_brief:
  layer_1_template: "grounding ref 1:1 https://robert-storage.tor1.digitaloceanspaces.com/images/generated/img-1773735051000.png"
  layer_2_subject: "a single open book lying flat on a desk, with two arrows drawn in the same ink flowing in opposite directions above and below it. The upper arrow flows from a hand (out of frame left) into the book, the lower arrow flows from the book out to a small sketch of a gear-shaped tool on the right. The book's pages are lightly marked with horizontal lines to suggest text without rendering any real letters."
  layer_3_material: "fine-tip Micron pen, architectural linework, light cross-hatching on the book cover and the hand"
  layer_4_composition: "upper 2/3, centered, generous whitespace around the arrows, lower 1/3 reserved for typography"
  layer_5_typography: "lesson title 'Docs As A Two-Way Street' in clean serif (EB Garamond 500), lower 1/3, with a smaller subtitle below reading 'The AI writes it. The next session builds against it.'"
  layer_6_constraints: "no color, black ink on cream paper only, NO background shift to dark"
  metaphor_rationale: "Two arrows in opposite directions, one into the book and one out, is the literal shape of the pattern: the AI fills the docs, the docs feed the next AI session. Sanity test: looking at the picture you understand the lesson is about a loop, not a one-direction write-once artifact."
---

Documentation used to be the chore you knew you should do and kept skipping, because writing it felt like extra work on top of the real work. That equation has quietly inverted in agent-driven codebases. Documentation is no longer the afterthought. It is the ground truth the next agent builds against, which makes writing it the highest-value thing to do in a session, not the lowest.

The shape of the pattern is easy to recognise once you see it live. A client running a backend project wires a docs site directly into the repo. Features get built the normal way, with an agent on one side and a codebase on the other, but every time a feature ships the agent writes the corresponding docs page before the session closes. Not as a chore. As a checkpoint. The docs page captures the shape of what the feature actually does, in prose a human can read and a model can digest, and then the session closes.

The next day, he opened a fresh session for a different feature and pointed the agent at the docs site, not the codebase. The agent read the docs, understood what the system did, and built the new feature without ever opening the files that contained the old feature's implementation. He told me it was stunning, and what he meant was specifically this: the new work was cleaner and faster than the work that had come before the docs layer existed, because the agent was building against a deliberately constructed summary instead of against a pile of source code.

![alt: A page of notes between two workstations with arrows flowing in both directions: docs are read and written by both sides](https://robert-storage.tor1.digitaloceanspaces.com/course-inline-illos/adp/2026-04-11-docs-as-two-way-street-inline.png)

Notice what is happening here. One agent is writing the docs. A different agent is reading them. Neither agent is doing both jobs at once. That separation is the whole trick. When you ask the same session to both implement a feature and document it, the documentation becomes a paraphrase of the implementation, with all the same assumptions baked in. When a fresh session reads those docs without ever seeing the code, it has to take the prose at face value. If the prose is wrong or incomplete, the new feature will expose the gap immediately. That is the forcing function I never had before.

There is one rule that makes or breaks this pattern, and I have to keep saying it out loud because it is easy to forget. You have to have a ground truth an AI can digest. The docs cannot be a pile of loose markdown files in a repo. They have to live somewhere with structure, with navigation, with a predictable path from question to answer. A plain Mintlify site works. So does any tool that gives you a single URL per concept. What does not work is scattering the truth across a README, three Google Docs, and a Slack thread from last Tuesday. A model will not stitch those together reliably, and neither will you.

The practical workflow looks like this. At the end of every feature you ship, before you close the session, have the agent write or update the corresponding docs page. Make it the last step of the task, not an optional add-on. Read the page yourself for thirty seconds to catch anything obviously wrong. Then, when you start the next session on a new feature, point the agent at the docs URL first and the codebase second. Let it read the prose. Ask it to tell you back what the system does in its own words. If the summary it gives you matches your mental model, you are safe to build on top of it. If it does not, you just discovered a gap in the docs, and fixing the gap costs less than debugging a feature built on a misunderstanding.

Your homework for this lesson is a single round trip. Pick one feature you already shipped. Have an agent write the docs page for it, in the smallest docs surface you can stand up today. Open a new session tomorrow, with no memory of the first, and ask a different question about that feature using only the docs page as input. Notice whether the answer holds up. That one round trip will tell you whether your docs are ground truth yet, or still just decoration.

---
title: "Playbooks that rewrite themselves: the try, fail, learn, retry loop"
course: Agentic Development Playbook
slug: playbooks-that-rewrite-themselves
image_cdn_url: https://robert-storage.tor1.digitaloceanspaces.com/skool/2026-04-11-playbooks-that-rewrite-themselves-logo.png
image_generated_at: 2026-04-11T13:21:00Z
status: draft
word_count: 764
source_files:
  - /Users/robertboulos/.claude/corpus/mined/framework-mine-yoonsun-mar25.json
source_quotes:
  - speaker: Robert Boulos
    date: 2026-03-25
    quote: "I run agents through an iterative self-improvement loop. The agent attempts a task, hits a failure (like Google's bot detection), then I tell it to update its own markdown playbook with what it learned. On the next run, it uses the improved instructions. The playbook is just a plain markdown file -- that's all an agent is. The faster you can tighten this try-fail-learn-retry loop, the faster you get a working agent."
image_brief:
  layer_1_template: "grounding ref 1:1 https://robert-storage.tor1.digitaloceanspaces.com/images/generated/img-1773735051000.png"
  layer_2_subject: "an open notebook with a hand pen mid-stroke writing onto its own page, with a small arrow looping from the page back into the pen — visual loop of self-editing"
  layer_3_material: "fine-tip Micron pen, architectural linework, black ink, light cross-hatching on the notebook spine and pen"
  layer_4_composition: "centered in the upper 2/3, generous whitespace, lower 1/3 reserved for typography"
  layer_5_typography: "lesson title 'Playbooks that rewrite themselves' in clean serif (EB Garamond 500), lower 1/3"
  layer_6_constraints: "no color, black ink on warm cream paper only, NO background shift to dark"
  metaphor_rationale: "A pen that loops back into the page it is writing on is a clean visual for a system editing its own instructions. Sanity test: someone seeing only the picture grasps that the subject is something writing about itself, which is the entire lesson."
skool_url: https://www.skool.com/snappy/classroom/a99c3835?md=310623175fe64468a4180ee10befce3c
shipped_at: 2026-04-11T17:02:02.982Z
---

Agents get dramatically faster the moment they are allowed to write to their own instruction file. Not the model getting better. The instructions getting better. The bottleneck is usually the operator, who treats the playbook as something they own and the agent has to obey, instead of a working document the two sides are editing together.

Here is the loop in its simplest form. The agent attempts a task. It hits a failure that is recoverable but not obvious, the kind of thing where a human watching over its shoulder would say "oh, that always fails the first time, you have to do this other thing first." Instead of fixing the bug for it, you tell the agent what happened, and you ask it to update its own playbook with the lesson it just learned. Next run, the playbook already contains the workaround. The agent does not have to discover it again.

That sounds trivial, and the moment you actually do it on a real task, the compounding becomes obvious. I will give you my own framing of why this works, from a recent session: "I run agents through an iterative self-improvement loop. The agent attempts a task, hits a failure (like Google's bot detection), then I tell it to update its own markdown playbook with what it learned. On the next run, it uses the improved instructions. The playbook is just a plain markdown file, that's all an agent is. The faster you can tighten this try, fail, learn, retry loop, the faster you get a working agent."

![alt: A notebook page with an arrow looping back into the pen writing on it: playbooks that rewrite themselves](https://robert-storage.tor1.digitaloceanspaces.com/course-inline-illos/adp/2026-04-11-playbooks-that-rewrite-themselves-inline.png)

Three things make this loop work, and they are the things people skip when they try to cargo-cult it.

The first is that the playbook has to be plain text. Not a config file, not a JSON schema, not a database row. A markdown file. The agent edits it the same way it edits any other file in your project. You read it the same way. You can grep it, version it, diff it, and check it into git. Every fancy abstraction layer you put around the playbook is friction the agent has to pay every time it wants to learn something new. That friction kills the loop.

The second is that the playbook has to live with the work. If your agent is wired to a browser automation skill, the playbook lives next to the browser automation skill and gets read every time the skill runs. If your agent is wired to a Xano MCP, the playbook lives in the project repo and gets loaded into context at the start of every session. The point is the agent should never have to go looking for the lessons it has already learned. They should already be in front of it.

The third is the failure-to-edit loop has to be tight. The longer you wait between "the agent failed" and "the agent updates its playbook with the fix," the more context you lose. I do this in the same session, while the failure is still in the conversation. I read the error, I tell the agent what the actual cause was if I happen to know, and I ask it to add a paragraph to its own playbook so the next run avoids the trap. Three sentences. One commit. Done. The next run starts smarter than the last one and the cost of that improvement was about thirty seconds of my attention.

There is one warning I will add. The playbook will get longer than you expect. Once you let the agent write to it freely, it will start adding edge cases, exceptions, corner cases, things that only happened once. After a few weeks you will need to do a pass to consolidate. I treat that the same way I treat any document cleanup: read it, find the duplicates, find the rules that contradict each other, find the ones that have been replaced by a more general rule, and trim. The trim is part of the loop. It is not a separate task.

Your homework is to pick one agent you currently use, find its instructions file, and watch it run a real task. The first time it stumbles, do not fix the underlying tool. Edit the instructions instead. Then run the same task again with the new instructions and note how much earlier it makes the right move. That single experiment is the entire lesson.

---
title: "Probability compounding: why your six-stage pipeline keeps failing"
image_cdn_url: https://robert-storage.tor1.digitaloceanspaces.com/skool/2026-04-10-probability-compounding-pipelines.png
image_generated_at: 2026-04-11T03:42:36Z
course: Agentic Development Playbook
slug: probability-compounding-pipelines
status: draft
word_count: 752
source_files:
  - /Users/robertboulos/.claude/corpus/mined/deep-masterminds-week1.json
source_quotes:
  - speaker: Robert Boulos
    date: 2026-03-30
    quote: "Probability compounding across pipeline stages is the design constraint nobody talks about. 80% success rate per stage across six stages means you probably fail. I've been reducing pipeline stages and treating each one as a pure function. Fewer stages, higher compound success rate."
image_brief:
  layer_1_template: "grounding ref 1:1 https://robert-storage.tor1.digitaloceanspaces.com/images/generated/img-1773735051000.png"
  layer_2_subject: "a chain of six interlocking gears in a horizontal row, the last gear visibly cracking and breaking apart, the chain still trying to turn"
  layer_3_material: "fine-tip Micron pen, architectural linework, light cross-hatching only on the broken gear"
  layer_4_composition: "gears centered in upper two-thirds, generous whitespace around them, lower third reserved for typography"
  layer_5_typography: "lesson title 'Probability Compounding' in clean serif (EB Garamond 500), lower 1/3, subtitle 'why long pipelines fail'"
  layer_6_constraints: "no color, black ink on cream paper only, NO background shift to dark, no gradients"
  metaphor_rationale: "Six gears in series visually communicate stage-chaining, and the breaking last gear shows the compounding-failure outcome. Sanity test: a student seeing only the image should think 'long chain, weakest link, breaks down' — which is the lesson."
skool_url: https://www.skool.com/snappy/classroom/a99c3835?md=93aaa4f7b1704c3da480e33a38e6e9a2
shipped_at: 2026-04-11T17:02:25.091Z
---

There is a number that almost every agentic pipeline I see is silently being killed by, and once you know it, you stop building the same broken thing over and over.

Here it is. If you build a pipeline where each stage is doing something probabilistic, calling a model, parsing an output, deciding what to do next, and each stage gets it right 80 percent of the time, the success rate of the whole pipeline is 0.8 to the power of however many stages you have. Two stages? 64 percent. Three stages? 51. Four? 41. Six? 26. By the time you get to six stages at 80 percent each, the pipeline succeeds about a quarter of the time. Three runs out of four are silently wrong somewhere along the way, and you are debugging ghosts.

Most people building agent pipelines do not internalise this. They look at a single stage that works most of the time and they extrapolate from there. The model handled extraction. The model handled classification. The model picked a tool. The model wrote the response. Each step looked fine in isolation, so the whole thing should be fine. It is not. Compounding does not care that each step felt fine. The math is brutal and it does not negotiate.

The fix is not to make each stage better. You can grind your way from 80 to 90 percent on a single stage and that is genuinely hard work, and even then, six stages at 90 percent is only 53 percent. You barely cleared a coin flip. The fix is structural. You reduce the number of stages.

This is the discipline I have been pushing on myself and on every pipeline I touch. Before I let a stage exist, I ask whether it is doing its own job or whether it is a stage that exists because I did not feel like merging it with the one before it. Most of the time the answer is the second one. Two stages that both classify something can usually be one stage with a slightly bigger prompt. A stage that just reformats the output of the previous stage can usually be folded into the previous stage's instructions. A stage that exists to "validate" the previous stage is often a tell that you do not trust the previous stage and the right move is to fix the previous stage, not paper over it with a check that has its own failure rate.

![alt: A single function box with one input and one output, two merged sub-boxes inside: fewer stages, higher compound probability](https://robert-storage.tor1.digitaloceanspaces.com/course-inline-illos/adp/2026-04-11-probability-compounding-pipelines-inline.png)

There is a second discipline that goes with this, and it is the one that actually rescues pipelines. Treat every stage as a pure function. Pure means: same input, same output, no hidden state, no surprises, no "well it depends what happened upstream." When a stage is pure, you can test it in isolation, you can replay it on the same input until you understand its real failure rate, and you can swap it for a deterministic implementation the moment you find one. When a stage is not pure, when it is reading something out of memory, or branching based on something it inferred two steps ago, you cannot test it, you cannot trust it, and the failure rate you measure on a sunny day is not the failure rate you get in production.

The pattern that comes out the other side is short, pure, and ruthless. Every stage justifies its own existence. Every stage takes its input and returns its output and does nothing else. Every stage either has a measured success rate you wrote down somewhere, or it is on probation until you measure it. And when you have that, the math starts working in your favour instead of against you. Three pure stages at 95 percent each is 86 percent end-to-end. That is a system you can build a business on. Six fuzzy stages at 80 percent each is a system you spend your weekends rerunning and apologising for.

![alt: A single clean function box with one input arrow and one output arrow: every stage does one thing and justifies its own existence](https://robert-storage.tor1.digitaloceanspaces.com/course-inline-illos/adp/2026-04-14-probability-compounding-pipelines-inline-2.png)

Your homework for this lesson: count the stages in the pipeline you are most frustrated with right now. Write each one on a line. Beside each one write the success rate you would honestly bet on. Multiply them. If the number you get is lower than the trust your users currently have in the system, that gap is the work. Start by killing one stage.

# Agent Browser — Enhancement Dashboard

**Audit date:** 2026-04-14
**Auditor:** small-courses-enhance-pod
**Scope:** 2 lessons (parallel-weekend-coverage, vs-playwright-mcp)

## Per-lesson verdict

| Lesson | Words | §4a | Dashes | Artifact | Inline | Move |
|---|---|---|---|---|---|---|
| parallel-weekend-coverage | 604 | clean | clean | clean | 1 | **UNTOUCHED — already has inline (four-windows + gear) at the "most concrete example" anchor** |
| vs-playwright-mcp | 585 | clean | clean | clean | 1 | **UNTOUCHED — already has inline (two-panel human-vs-agent) at the decision split** |

## Touched: 0 / 2
## Untouched: 2 / 2
## Images generated: 0

## Biggest single improvement

**Already strong.** Both lessons carry purposeful inlines at the load-bearing paragraph, covers are in place, §4a / dashes / artifacts are clean, prose voice is first-person and concrete, and each ends with a specific-next-action homework item. This is what "don't manufacture work" looks like.

## Judgment
- 0 of 2 lessons touched.
- Both lessons ship-ready on disk.
- Nothing to fix.

---
title: "Parallel headless QA: covering an app in a weekend"
course: Agent Browser
tool: agent-browser
source_files: []
source_quotes:
  - speaker: generic
    date: 2026-03-16
    quote: "Over a weekend I covered email templates, pipeline stages, drag-and-drop interactions, and e-signature flows end-to-end through Agent Browser with no human driving the browsers. The one real weakness is fine-motor interactions like drawing on a signature pad, where the resulting pixels do not match what a human would produce."
image_status: pending
image_prompt: |
  Layer 1 (template lock): use the cream-paper 1:1 ref image as a hard style lock.
  Layer 2 (subject): four small browser windows arranged in a two-by-two grid, each showing a different simple UI sketch, all connected to a small node at the top labeled with a gear icon.
  Layer 3 (material): fine-tip Micron pen, clean architectural linework.
  Layer 4 (composition): centered in the upper two-thirds, generous whitespace, lower third reserved for title.
  Layer 5 (typography): in the lower third, clean serif title text reading "Four Flows, One Weekend" with a smaller subtitle below reading "Parallel headless QA".
  Layer 6 (constraints): black ink on warm cream paper only, no color, no gradients, no photographic elements, no background texture beyond the cream paper.
logo_ref: null
draft_date: 2026-04-10
image_cdn_url: https://robert-storage.tor1.digitaloceanspaces.com/course-covers/agent-browser/2026-04-11-parallel-weekend-coverage.png
image_generated_at: 2026-04-11T16:06:55Z
---

The question that always comes up when someone is planning QA coverage for an application is "how long will this take?" The answer you are used to is weeks, because the default shape of QA work is one human poking at one flow at a time, writing down what broke, filing tickets, waiting for fixes, retesting. That shape is unavoidable when each test is expensive. It stops being unavoidable when each test costs a fraction of a cent and can run in parallel.

![alt: Four small browser windows in a two-by-two grid connected to one gear node above: four flows in one weekend](https://robert-storage.tor1.digitaloceanspaces.com/course-inline-illos/agent-browser/2026-04-11-parallel-weekend-coverage-inline.png)

The most concrete example I have of this shift is covering an entire application's critical flows in a weekend. Email templates, pipeline stages, drag-and-drop interactions, e-signature flows, all walked end to end, all through Agent Browser, no human driving the browsers. The work that would have been weeks of manual QA compressed into two days of agents running loops, reporting what they found, getting fixes shipped, and running again.

The mechanic that makes this possible is parallelism. You do not have one agent walking every flow in sequence. You have four or five agents walking different flows at the same time, in their own isolated headless browser sessions, reporting into the same coordinating agent. The coordinator decides what to do with each report, queues fixes, kicks off reruns. While one agent is stuck in a confirmation modal, three others are finding new bugs in other parts of the app. Wall-clock time becomes the time the slowest single agent takes, not the sum of all agents.

The thing that enables this in practice is session isolation. Each parallel Agent Browser instance runs in its own session, keyed by a name you pick when you launch it. The sessions do not share cookies, storage, or window state. That matters because without isolation, two agents trying to log in as different users would stomp on each other, and whatever the second one did would blow away the first one's session. With isolation, you can run five agents as five different test users against the same application, and none of them interferes with the others.

There is a real limitation to know about. The weak spot of any browser-automation-driven QA pass is fine motor interaction. Drawing on a signature pad, doing a precise gesture on a canvas, dragging an element to a pixel-perfect position, these are the cases where an agent pretending to be a user with a mouse falls down. The gesture can be simulated, but the resulting pixels do not match what a human would produce, and any validation that checks for "did the user actually draw something legible" will catch the gap. Those flows still need a human pass, at least for the final verification.

Everything else is fair game. Form flows, click paths, navigation, drag-and-drop at the widget level (as opposed to the pixel level), multi-step wizards, role-based views, error states, all of this is within the reach of an agent that can see the DOM and dispatch events. The coverage you can get in a weekend of agent-driven QA is coverage that would have taken a single human a month of focused effort.

Your homework: pick the one flow in your current application that you feel least confident about. Write down the steps a user would take to exercise it. Then ask whether those steps could be performed by an agent walking the DOM. If the answer is yes, that is a flow that should be in continuous automated coverage, not in the "I'll manually test it before release" bucket.

---
title: "Agent Browser versus Playwright MCP: why I uninstalled Playwright"
course: Agent Browser
tool: agent-browser
source_files: []
source_quotes:
  - speaker: generic
    date: 2026-03-16
    quote: "Agent Browser replaces Playwright MCP for browser automation in Claude Code. I run it headless, launch multiple agents in parallel, and point them at different parts of the app simultaneously. The verbs it exposes (click, fill, eval, upload, find text, screenshot) behave the way a developer poking at a browser console would expect them to behave."
image_status: pending
image_prompt: |
  Layer 1 (template lock): use the cream-paper 1:1 ref image as a hard style lock.
  Layer 2 (subject): a single browser-window frame with multiple small cursor arrows inside it, each pointing at a different element. Nothing else.
  Layer 3 (material): fine-tip Micron pen, architectural linework, light cross-hatching on the window chrome.
  Layer 4 (composition): centered in the upper two-thirds, generous whitespace, lower third reserved for title.
  Layer 5 (typography): in the lower third, clean serif title text reading "One Window, Many Hands" with a smaller subtitle below reading "Agent Browser, not Playwright".
  Layer 6 (constraints): black ink on warm cream paper only, no color, no gradients, no photographic elements, no background texture beyond the cream paper.
logo_ref: null
draft_date: 2026-04-10
image_cdn_url: https://robert-storage.tor1.digitaloceanspaces.com/course-covers/agent-browser/2026-04-11-vs-playwright-mcp.png
image_generated_at: 2026-04-11T16:06:55Z
---

For a long time the default way to give Claude Code the ability to drive a web page was the Playwright MCP. Playwright is a mature browser automation library, the MCP wrapper is straightforward, and if you had already used Playwright for testing you could slide into it easily. I used it for a while. Then I switched to Agent Browser, and the Playwright MCP came off my machine.

![alt: Two panels side by side, left with a developer at a laptop and right with a laptop driven by floating cursors: one writes scripts, one is driven by an agent](https://robert-storage.tor1.digitaloceanspaces.com/course-inline-illos/agent-browser/2026-04-11-vs-playwright-mcp-inline.png)

The reason for the switch is less about Playwright being bad and more about Agent Browser being shaped for the specific job of "an AI agent driving a browser during a development session." Playwright was designed for test authoring, a developer sits down, writes a script, runs the script, and checks the result. Agent Browser is designed for an agent to pick up a browser, do something, and hand control back, all inside the same turn of a conversation. Those are different workflows, and the tool that matches the workflow wins.

The specific thing that made the switch worth it for me is that Agent Browser runs headless comfortably, and it runs in parallel comfortably. I can launch multiple Agent Browser instances at once and point each one at a different part of the application, one walking the onboarding flow, one exercising the settings page, one clicking through a data export. They run simultaneously, they report back when they are done, and the main session does not have to wait on any one of them. In Playwright-MCP land, that kind of parallelism was possible but awkward. In Agent Browser it is the default case.

The other thing worth naming is the interaction model. Agent Browser exposes a set of verbs the agent can call directly, click, fill, eval, upload, find text, screenshot, and they behave the way a developer poking at a browser console would expect them to behave. There is less translation between "what the agent wants to do" and "what the tool lets it do." That sounds small until you watch an agent fight with a tool for twenty minutes because the tool's mental model does not match the task, and then you realize that the reduction in fighting is the whole value.

There are things Playwright is still better at. If you are writing a real test suite that will live in CI and be maintained by humans, Playwright's surrounding tools, test runners, fixtures, trace viewers, the whole apparatus, are more developed and probably the right call. Agent Browser is not trying to replace that. It is trying to be the tool you reach for when an agent needs to look at a page and do something to it, in the middle of a development session, without ceremony.

The test I use when I am deciding between the two is: who is going to maintain this script over time? If the answer is "a human who will read it and edit it and re-run it," Playwright. If the answer is "an agent who will regenerate it every time the context changes," Agent Browser. That split covers most of the cases I actually hit.

Your homework: if you still have Playwright MCP installed from a previous project, try running the same small task through Agent Browser instead. Notice the difference in how many tool calls it takes to get the same thing done.

# Browser Automation — Enhancement Dashboard

**Audit date:** 2026-04-14
**Auditor:** small-courses-enhance-pod
**Scope:** 5 lessons (browser-01 through browser-05)

## Per-lesson verdict

| Lesson | Words | §4a | Dashes | Artifact | Inline | Move |
|---|---|---|---|---|---|---|
| browser-01-the-problem | 639 | clean | clean | clean | 0 | **UNTOUCHED — cover + five-problem prose arc is self-carrying** |
| browser-02-what-agent-browser-does | 688 | clean | clean | clean | 0 | **UNTOUCHED — cover (CLI-to-browser) matches the load-bearing core loop** |
| browser-03-dom-cartographer | 764 | clean | clean | clean | 0 | **UNTOUCHED — cover (magnifier over DOM tree) visualizes the skill's job** |
| browser-04-real-patterns | 726 | clean | clean | clean | 0 → 1 | **TOUCHED — added five-patterns inline at the opening** so readers see the five numbered patterns before diving into details |
| browser-05-install-and-ship | 686 | clean | clean | clean | 0 | **UNTOUCHED — cover (install + browser icon) matches the ship-oriented content** |

## Touched: 1 / 5
## Untouched: 4 / 5
## Images generated: 1

- CDN: `https://robert-storage.tor1.digitaloceanspaces.com/skool/2026-04-14-browser-04-real-patterns-inline.png`

## Biggest single improvement

Adding the five-pattern row diagram to browser-04. That lesson walks through five distinct shipped patterns (two-phase save, ProseMirror image handling, bridge input, strict-mode scoping, post-reload verification) and a visual sequence at the top lets readers orient before each is unpacked. Without it the lesson is a wall of prose at 726w.

## Notes

The other four lessons each have covers doing real conceptual work. Stuffing additional illustrations in would pad, not clarify. Honest restraint applied.

## Judgment
- 1 of 5 lessons touched, 4 already strong.
- 1 inline image generated and embedded.
- Zero defects on §4a / dashes / artifacts.

---
title: "The browser automation problem for agents"
course: Browser Automation
tool: browser-automation
lesson_group: agent-browser
lesson_part: 1
source_files:
  - ~/.claude/skills/agent-browser/SKILL.md
  - ~/.claude/skills/snappy-browse/SKILL.md
source_quotes:
  - speaker: generic
    date: 2026-04-13
    quote: "Playwright runs in strict mode. A locator that matches more than one element throws strict mode violation and the click silently fails. Seen in the wild clicking Control on a page that has Build and control your first agent and Control keep agents from going off the rails, both match getByText Control, nothing happens."
  - speaker: generic
    date: 2026-04-13
    quote: "React controlled inputs track value via internal state set by onChange. Setting input.value, Object.getOwnPropertyDescriptor set.call, dispatching a new Event input, the _valueTracker setValue hack, toggling other form fields, none of these enable a disabled Save button. The only thing that works is real keystrokes via Playwright fill."
image_status: complete
image_url: https://robert-storage.tor1.digitaloceanspaces.com/skool/2026-04-13-browser-01-the-problem.png
image_prompt: |
  Layer 1 (template lock): cream-paper 1:1 ref image as the hard style lock.
  Layer 2 (subject): a browser window with a red X over a click cursor, five small warning icons scattered around the page representing the 5 failure modes.
  Layer 3 (material): fine-tip Micron pen, architectural linework, steady pressure.
  Layer 4 (composition): browser window centered in the upper two-thirds, lower third reserved for title.
  Layer 5 (typography): lower third, clean serif, title "The Problem" with subtitle "Part 1 of 5: Agent Browser".
  Layer 6 (constraints): black ink on warm cream paper, no color, no gradients, no photographic elements.
draft_date: 2026-04-13
skool_url: https://www.skool.com/snappy/classroom/8ce3f58b?md=ebf887456d8d4b97aab8acb8f0e7d15e
---

Most browser automation tutorials start with the happy path. Open a page, grab a selector, click the button, assert the result. That works fine on a static site you control. It stops working the moment you point an agent at a real React application and ask it to do something useful.

The first thing that breaks is the selector. You ask the agent to click "Sign In" and it picks `getByText("Sign In")`. Playwright runs in strict mode, which means a locator that matches more than one element throws a strict-mode violation. The click does not happen. No error surfaces to the agent because the call technically succeeded, it just refused to act. The page has a hero headline that says "Sign in to continue" and a button labeled "Sign In" and a footer link that also says "Sign In", and all three match. One call, three candidates, zero actions, agent confused.

The second thing that breaks is the controlled input. You fill a form field with `input.value = "new value"` and the value visually appears in the box, and the agent reports success. Then it clicks Save and Save is greyed out. React tracks input state internally through onChange, and assigning to `.value` does not run onChange, so React still thinks the field is empty. The `_valueTracker.setValue` hack, the `Object.getOwnPropertyDescriptor` approach, dispatching a synthetic input event, none of them flip the React state machine. The Save button stays dead and the agent keeps retrying the same broken approach.

The third thing that breaks is the click itself. On many React apps, plain `.click()` does nothing. The library is listening for pointer events, not click events, and a synthetic click dispatched from console does not produce a real pointer trail. The agent clicks the dropdown button, nothing happens, the menu does not open, the agent starts inventing theories about auth state or z-index while the actual problem is that the event system needs a real Playwright pointer sequence.

The fourth thing that breaks is auth. Most useful work requires being logged in, and logins involve redirects, cookie modals, WAF challenges, email verification, sometimes 2FA. An agent that can get past the login screen once and then persists that state is a different animal from an agent that has to navigate the whole login flow every time it wants to look at a page. Without state persistence, every single task starts with ten minutes of login choreography before any real work happens. With state persistence, but no guard rails, the agent will eventually save a logged-out state over a logged-in one and clobber its own auth.

The fifth thing that breaks is the recovery loop. When a selector fails, when a click does not register, when a form refuses to submit, the agent needs to know what the page actually looks like right now, not what it looked like two turns ago. Static selectors cannot describe that. What you need is a snapshot the agent can take at any moment, a fresh read of the interactive surface, with stable refs it can point at for the next action. That is a different tool shape than "run a Playwright script end to end and hope nothing drifts."

![A vertical list of the five failure modes, selector drift, controlled input, fake click, auth loss, and missing snapshot, each marked with an X.](https://robert-storage.tor1.digitaloceanspaces.com/images/inline/2026-04-14-browser-01-the-problem-inline-1.png)

These five failures are the reason a tool designed for test authoring does not map cleanly onto a tool used by an agent during a live conversation. Test authoring is slow, careful, human-curated. Agent driving is fast, iterative, and has to recover from surprises without ceremony. The tool has to be shaped for the second job, not the first.

That is the problem this course is about. The next lesson covers what agent-browser does differently to make that shape work, and the one after that covers the hardening layer that keeps agents from falling into the same traps over and over.

Install the skill: `https://skills.snappy.ai/skills/agent-browser`

---
title: "What agent-browser does differently"
course: Browser Automation
tool: browser-automation
lesson_group: agent-browser
lesson_part: 2
source_files:
  - ~/.claude/skills/agent-browser/SKILL.md
  - ~/.claude/skills/snappy-browse/SKILL.md
source_quotes:
  - speaker: generic
    date: 2026-04-13
    quote: "Every browser automation follows this pattern. Navigate via agent-browser open URL. Snapshot via agent-browser snapshot dash i which returns element refs at e one at e two. Interact using refs to click fill select. Re-snapshot after navigation or DOM changes to get fresh refs."
  - speaker: generic
    date: 2026-04-13
    quote: "Refs at e one at e two et cetera are invalidated when the page changes. Always re-snapshot after clicking links or buttons that navigate, form submissions, dynamic content loading dropdowns modals."
  - speaker: generic
    date: 2026-04-13
    quote: "Parallel sessions work via dash dash session name. agent-browser dash dash session site one open URL. agent-browser dash dash session site two open other URL. Each runs in its own isolated session, cookies storage window state are not shared."
image_status: complete
image_url: https://robert-storage.tor1.digitaloceanspaces.com/skool/2026-04-13-browser-02-what-agent-browser-does.png
image_prompt: |
  Layer 1 (template lock): cream-paper 1:1 ref image as the hard style lock.
  Layer 2 (subject): a CLI terminal box with "agent-browser" typed, an arrow pointing to a simplified browser window showing a clean DOM tree.
  Layer 3 (material): fine-tip Micron pen, architectural linework, steady pressure.
  Layer 4 (composition): terminal and browser window side by side in the upper two-thirds, lower third reserved for title.
  Layer 5 (typography): lower third, clean serif, title "What Agent Browser Does" with subtitle "Part 2 of 5: Agent Browser".
  Layer 6 (constraints): black ink on warm cream paper, no color, no gradients, no photographic elements.
draft_date: 2026-04-13
skool_url: https://www.skool.com/snappy/classroom/8ce3f58b?md=d4c1d203e0a548c9990f1bf025797f93
---

Agent-browser is a CLI wrapper around Playwright that reshapes the interaction model for an agent instead of a test author. The shape of the shell is the entire point. Every verb is a single command that starts, does one thing, and returns a result the agent can read. There is no long-running script, no setup block, no teardown. The agent runs one line, reads the output, decides what to run next.

![A four-step loop, open then snapshot then act then re-snapshot, with arrows running clockwise around a center page icon.](https://robert-storage.tor1.digitaloceanspaces.com/images/inline/2026-04-14-browser-02-what-agent-browser-does-inline-1.png)

The core loop has four steps. Open a page. Take a snapshot. Act on the snapshot. Re-snapshot. That cycle looks small written down, but it maps directly onto how an agent actually reasons about a page. The snapshot is the key move. When the agent runs `agent-browser snapshot -i` it gets back a list of every interactive element on the page, each tagged with a ref like `@e1`, `@e2`, `@e3`, along with its role, accessible name, and rough position. The ref is stable for the duration of the snapshot. The agent uses the ref to act, and if the page changes, it takes a new snapshot and gets new refs. There is no selector drift because there is no saved selector, only a fresh read each time.

The commands are shaped like verbs an agent already knows. `click @e3` clicks the element. `fill @e5 "value"` clears the field and types the value using real Playwright keystrokes, which is the only thing that reliably flips React's controlled-input state. `select @e1 "California"` picks a dropdown option. `press Enter` dispatches a real key event. `screenshot` captures the current viewport. `get text @e2` reads the element's text. `get url` returns the current URL. Each of these returns a simple result, and the agent composes them in sequence.

Sessions are first-class. You pass `--session name` on any command and that command runs in its own isolated browser context. Cookies, storage, window state are not shared between sessions. That is what lets you run multiple agents against the same application at the same time without them stomping on each other's auth. One session logs in as user A, another logs in as user B, a third runs as the anonymous visitor, and all three work in parallel in the same daemon process.

Authentication state persists via state files. You log in once, save the session to a JSON file, and every future command can load that state without repeating the login flow. That single feature is what turns browser automation from "ten minutes of login per run" into "one login, thousands of actions after it." The state file is a serialized cookie jar plus storage snapshot, readable by humans, checked into the workspace, restorable on demand.

The eval command is the escape hatch. When the snapshot does not have the selector you need, when you want to extract data from thirty DOM nodes at once, when you want to run a small calculation inside the page, `agent-browser eval "JS_EXPRESSION"` runs arbitrary JavaScript in the page context and returns the result. That single verb replaces an entire category of fragile selector chains. Instead of clicking through twenty rows to read their content, you run one eval that queries them all and returns a JSON array. The agent reads the array and moves on.

The daemon architecture is the last piece worth naming. Agent-browser runs a long-lived Chrome daemon in the background, and each command attaches to it over the Chrome DevTools Protocol. That means there is no cold start per command. The agent can run fifty commands in a row and each one takes milliseconds to dispatch because the browser is already warm. It also means killing the daemon is catastrophic, since it wipes every concurrent session at once. The rule is: never `pkill agent-browser`. You close your own session by name and leave everybody else's alone.

Put all of that together and you get a tool where the agent's inner loop is tight. Snapshot, decide, act, re-snapshot. No long scripts, no stale selectors, no dead clicks, no lost auth. The next lesson covers the hardening layer that sits above this primitive and protects the agent from the remaining traps.

Install the skill: `https://skills.snappy.ai/skills/agent-browser`

---
title: "The DOM Cartographer: map first, act second"
course: Browser Automation
tool: browser-automation
lesson_group: agent-browser
lesson_part: 3
source_files:
  - ~/.claude/skills/snappy-dom-cartographer/SKILL.md
  - ~/.claude/skills/snappy-course/AGENTS.md
source_quotes:
  - speaker: generic
    date: 2026-04-13
    quote: "Each new flow burns hours rediscovering selectors, fighting React controlled inputs, and tripping over WAFs. This skill is the dedicated cartographer. You point it at a UI, it produces a verified DOM map, selectors plus click patterns plus quirks, and patches the target skill AGENTS.md so the next agent can ship the actual content feature in minutes instead of hours."
  - speaker: generic
    date: 2026-04-13
    quote: "Standard mapping run. Read the consumer skill AGENTS.md to see what is already known, do not redo. Verify auth state. Start a session via the guard script with a unique session name. Take a baseline screenshot. Enumerate the top-level DOM landmarks with eval. For each interactive landmark click it and screenshot the result. Inside any modal enumerate buttons plus inputs plus labels. Test every action rename add duplicate delete upload. For text inputs always verify with agent-browser fill. Patch the consumer skill AGENTS.md with a verified section."
image_status: complete
image_url: https://robert-storage.tor1.digitaloceanspaces.com/skool/2026-04-13-browser-03-dom-cartographer.png
image_prompt: |
  Layer 1 (template lock): cream-paper 1:1 ref image as the hard style lock.
  Layer 2 (subject): a magnifying glass over a web page, revealing a clean labeled tree structure (the DOM map) beneath the visual surface.
  Layer 3 (material): fine-tip Micron pen, architectural linework, steady pressure.
  Layer 4 (composition): magnifying glass and page centered in the upper two-thirds, lower third reserved for title.
  Layer 5 (typography): lower third, clean serif, title "DOM Cartographer" with subtitle "Part 3 of 5: Agent Browser".
  Layer 6 (constraints): black ink on warm cream paper, no color, no gradients, no photographic elements.
draft_date: 2026-04-13
skool_url: https://www.skool.com/snappy/classroom/8ce3f58b?md=aadcab8db66d405298cd3688b482d9a3
---

Agent-browser gives an agent the verbs to act on a page. The problem is that acting without context is the same as guessing. An agent that opens the Skool lesson editor and starts clicking around is going to waste turns discovering that the pencil button has a CSS-hover parent that has to be forced visible before it can be clicked, or that the Save button stays disabled until the title field is dirty, or that image uploads need a bridge input because React-dropzone intercepts file setter calls. Every one of these is a trap that costs an hour the first time and a few minutes every time after if you remember the fix.

The DOM Cartographer is the skill that remembers the fix. It is a dedicated mapping agent whose entire job is to walk a web application, identify every interactive surface, test every action, and write down what it found in a format the next agent can read and follow. Point it at a new page, it produces a verified DOM map. That map gets written directly into the consumer skill's AGENTS.md so any agent that loads that skill gets the map automatically.

![Map first then act, a cartographer figure inspecting a page and producing a selector and quirk map, then a worker agent reading the map and acting on the same page.](https://robert-storage.tor1.digitaloceanspaces.com/images/inline/2026-04-14-browser-03-dom-cartographer-inline-1.png)

The pattern is map first, act second. When a new UI surface needs coverage, you do not send the working agent to figure it out live. You send the cartographer first. The cartographer opens the page, enumerates the landmarks, clicks each one, records what happens, tries every action vocabulary (rename, add, duplicate, delete, upload), verifies text inputs with real keystrokes, and writes down the exact selector chain for each. The output is a table of selectors, a table of action recipes, and a list of quirks specific to that surface. Once the map exists, the working agent reads it and ships the actual content in a fraction of the time.

The contents of a good map are not just selectors. The selectors are the easy part. What matters is the quirks. The fact that Skool's dropdown button parent has `display: none` until hover and has to be force-shown via JavaScript before the click will register. The fact that ProseMirror image deletion through DOM removal does not update the editor's internal state and requires a real transaction through the Tiptap editor instance. The fact that the bridge input pattern is the only way to upload a file to a React-dropzone-intercepted input. The fact that the Skool course cover crop modal has two Save buttons and you need the last one. None of these are discoverable from a fresh snapshot. They are only discoverable by making the mistake once, paying the cost, and writing down the lesson.

The cartographer also owns auth hygiene. Every mapped site gets a guard script that verifies the auth file exists, checks for the expected cookie, backs up the file before use, opens the target URL with state loaded, and verifies the rendered page is logged in. If any of those checks fail, the script exits non-zero and nothing downstream runs. That rule exists because of a real incident where multiple agents saved logged-out state over a working auth file and blocked the swarm for hours. A guard script in front of every session makes that failure mode impossible to repeat.

The same hygiene covers session management. Every cartographer run uses a unique `--session` name so it runs concurrently with whatever else is happening in the daemon. It never calls `pkill` on the daemon because that would kill every sibling session. It closes its own session by name when it is done and leaves everything else alone. The rule is simple: touch nothing you did not create.

Map output goes into AGENTS.md under a dated section so the next agent can tell when the map was last verified. Skool redesigns, selectors drift, and a six-month-old map is worse than no map because it looks authoritative while being wrong. Every map gets a verification date. If an action fails and the date is older than a month, the first move is to rerun the cartographer on that surface and regenerate the map. The whole system assumes maps decay, which is why the cartographer is a skill you can invoke on demand instead of a one-time effort.

The practical result is that a working agent loading snappy-course gets a ready-made table of Skool classroom selectors, action recipes, and quirks, all current, all verified, all tested by the cartographer. The working agent never has to rediscover the bridge input pattern or the save-enable trap. It reads the map, it acts. That compounds fast across many surfaces.

Install the skill: `https://skills.snappy.ai/skills/snappy-dom-cartographer`

---
title: "Real patterns from shipped work"
course: Browser Automation
tool: browser-automation
lesson_group: agent-browser
lesson_part: 4
source_files:
  - ~/.claude/skills/snappy-course/AGENTS.md
  - ~/.claude/skills/snappy-browse/SKILL.md
source_quotes:
  - speaker: generic
    date: 2026-04-13
    quote: "Programmatic dot value equals plus dispatchEvent does NOT enable Save buttons on Skool inputs. Real keystrokes are required. Tag the input first. document.querySelector SELECTOR dot id equals my input. Real keystrokes via Playwright. agent-browser fill hash my input New value. The value tracker hack, Object dot getOwnPropertyDescriptor set dot call, and toggling other fields all fail. Only agent-browser fill works."
  - speaker: generic
    date: 2026-04-13
    quote: "SAVE requires title dirty. setContent, image uploads, ProseMirror transactions do NOT enable SAVE. Fill title LAST via agent-browser fill, this is the only step that enables SAVE. Save-enable trap recovery: focus the title, type a space to append, focus again, press Backspace to remove the space, SAVE stays enabled."
  - speaker: generic
    date: 2026-04-13
    quote: "React-dropzone intercepts setInputFiles on its hidden input. The fix is inject a bridge input. Create a file input, append to body, on change forward the file to the dropzone input via its react props onChange handler. Upload via the bridge. Click Save on crop modal, the last Save on the page. Click Save on the edit modal, the first Save."
image_status: complete
image_url: https://robert-storage.tor1.digitaloceanspaces.com/skool/2026-04-13-browser-04-real-patterns.png
image_prompt: |
  Layer 1 (template lock): cream-paper 1:1 ref image as the hard style lock.
  Layer 2 (subject): three small browser windows in a row, each showing a different interaction pattern — form fill, dropdown click, content paste — connected by arrows.
  Layer 3 (material): fine-tip Micron pen, architectural linework, steady pressure.
  Layer 4 (composition): three browser windows arranged horizontally in the upper two-thirds, lower third reserved for title.
  Layer 5 (typography): lower third, clean serif, title "Real Patterns" with subtitle "Part 4 of 5: Agent Browser".
  Layer 6 (constraints): black ink on warm cream paper, no color, no gradients, no photographic elements.
draft_date: 2026-04-13
skool_url: https://www.skool.com/snappy/classroom/8ce3f58b?md=0fc67707fed140a0aaf5e46ea0f16ca3
---

The patterns in this lesson are all lifted from code that ships. Each one came from an agent failing against a real UI, the failure getting diagnosed, the fix getting written down, and the next agent reading the fix instead of rediscovering it. This is what a hardened automation skill looks like after enough shipping.

![Five numbered rectangles in a row, each a shipped browser-automation pattern, connected by thin arrows: Five Patterns That Ship](https://robert-storage.tor1.digitaloceanspaces.com/skool/2026-04-14-browser-04-real-patterns-inline.png)

The first pattern is the two-phase save on Skool's lesson editor. The Tiptap body editor accepts `editor.commands.setContent(html)` and the HTML renders, images appear, lists format correctly. The Save button stays disabled. Nothing you do to the body dirties the form in React's state machine. The only field that does dirty it is the title input, and only when the title is filled via real Playwright keystrokes. The recipe is always: set the body first, upload any images, set the published toggle, then fill the title last. If the title was already correct and you cannot change it, the recovery is to focus the title, type a space to dirty it, focus again, press Backspace to remove the space, and Save stays enabled. That one trick unblocked the entire lesson-ship pipeline.

The second pattern is ProseMirror image handling. You cannot delete an image from a Tiptap editor by removing the DOM node. The editor keeps its own internal document model, and if you delete the DOM, the model still thinks the image is there. The fix is to dispatch a real ProseMirror transaction against the editor instance: walk the document, collect the image nodes, build a delete transaction, dispatch it through the editor view. The same rule applies to inserting images at a specific position. You cannot use `insertAdjacentHTML` with an `<img>` tag because ProseMirror silently drops it, since `img` is not a schema node at that point. You have to map the cursor to the target offset, upload the image through the editor's file input, and let Tiptap insert a real image node. The difference between these two approaches is a lesson, an agent, and an hour of debugging.

The third pattern is the bridge input for file uploads. React-dropzone wraps a hidden file input and intercepts the setter so Playwright's `setInputFiles` call never reaches the underlying input. The workaround is to create your own file input, attach it to the page, listen for its change event, and on change forward the file to the dropzone's internal input through its React props onChange handler. You pull the `__reactProps` key off the dropzone input, call its onChange with a synthetic event carrying the file, and React updates its state correctly. From there the upload proceeds normally. The Skool course cover upload uses this pattern every time a new course is created.

The fourth pattern is scoping clicks against strict-mode violations. When a label appears multiple times on the page, `find text "Label" click` fails silently. The priority order for selectors is: a ref from `snapshot -i`, then role plus accessible name (`find role button name "Sign In" click`), then text with exact match, then a scoped snapshot against a CSS region, then a partial class match, then a nth-match index as last resort. The general rule is never pass ambiguous text to click. If the label could appear in a heading, nav, and body copy, use snapshot plus ref. When a click does nothing, assume strict-mode violation before assuming the element is missing.

The fifth pattern is verification through full reload. A toast is not evidence. A same-session DOM read is not evidence. When an action modifies state on a platform, the only reliable verification is to navigate away from the page, navigate back, and read the rendered state fresh. That means reopening the lesson, reading the body, counting the image nodes, confirming the title matches what you tried to set. If the image count expected was two and the actual is zero, the upload silently failed and you re-run it. The verification step is not optional. The skill treats every action as unverified until a post-navigation read confirms it, and every verified action gets a certificate written to the pass log.

These five patterns are not curiosities. They are the difference between a lesson-ship pipeline that works and one that reports success while silently losing half the content. Each was discovered by failing, diagnosed, written down, and then reused forever. That is what hardening means in practice.

Install the skill: `https://skills.snappy.ai/skills/agent-browser`

---
title: "Install and your first automation"
course: Browser Automation
tool: browser-automation
lesson_group: agent-browser
lesson_part: 5
source_files:
  - ~/.claude/skills/agent-browser/SKILL.md
  - ~/.claude/skills/snappy-browse/SKILL.md
source_quotes:
  - speaker: generic
    date: 2026-04-13
    quote: "Every browser task follows navigate, snapshot, interact, re-snapshot. agent-browser open URL. agent-browser snapshot dash i returns at e one at e two refs. agent-browser click at e three. agent-browser snapshot dash i must re-snapshot after navigation or DOM changes."
  - speaker: generic
    date: 2026-04-13
    quote: "Templates ready to run. form-automation dot sh url. authenticated-session dot sh login-url. capture-workflow dot sh url output-dir. These are working examples shipped with the skill that demonstrate the core loop against any site."
image_status: complete
image_url: https://robert-storage.tor1.digitaloceanspaces.com/skool/2026-04-13-browser-05-install-and-ship.png
image_prompt: |
  Layer 1 (template lock): cream-paper 1:1 ref image as the hard style lock.
  Layer 2 (subject): a terminal showing "npm install agent-browser" with a checkmark, an arrow to a running browser icon.
  Layer 3 (material): fine-tip Micron pen, architectural linework, steady pressure.
  Layer 4 (composition): terminal and browser icon arranged left to right in the upper two-thirds, lower third reserved for title.
  Layer 5 (typography): lower third, clean serif, title "Install and Ship" with subtitle "Part 5 of 5: Agent Browser".
  Layer 6 (constraints): black ink on warm cream paper, no color, no gradients, no photographic elements.
draft_date: 2026-04-13
skool_url: https://www.skool.com/snappy/classroom/8ce3f58b?md=0b43c6707fdf453b82287c52ce656f5c
---

This lesson gets a working agent-browser install on your machine, runs one minimal automation end to end, and points at the next place to go. No philosophy. No warm-up. Just the steps.

Install agent-browser from the gateway. The skill lives at `https://skills.snappy.ai/skills/agent-browser`, and installing it through the Snappy skills gateway drops it into your Claude Code environment with all the templates and references in place. The install is idempotent, so running it twice is safe. When it is done, verify with `agent-browser --version` to confirm the CLI is on your path.

The first automation is a form fill against a real page. Pick any public form you know, a signup page, a contact form, a search box. Run these three commands:

```
agent-browser open https://example.com/signup
agent-browser snapshot -i
agent-browser fill @e1 "Test Name"
agent-browser click @e2
```

Replace the refs based on what the snapshot returned. The snapshot output is human-readable and tagged with role, accessible name, and rough position for every interactive element. Read it, pick the input field, fill it, pick the submit button, click it. If the page navigates, re-snapshot before doing anything else. That is the core loop and every automation you will ever write starts as some variation on these four lines.

![A pipeline showing login once, state save, an auth.json file, then reuse forever with a return arrow looping back to the file.](https://robert-storage.tor1.digitaloceanspaces.com/images/inline/2026-04-14-browser-05-install-and-ship-inline-1.png)

The second automation is a login flow with state persistence. Run:

```
agent-browser open https://app.example.com/login
agent-browser snapshot -i
agent-browser fill @e1 "$USERNAME"
agent-browser fill @e2 "$PASSWORD"
agent-browser click @e3
agent-browser wait --url "**/dashboard"
agent-browser state save auth.json
```

That writes a JSON file containing the session cookies and storage. The next time you want to use this site, you run `agent-browser state load auth.json` and `agent-browser open https://app.example.com/dashboard` and you are already logged in. No second login, no 2FA, no cookie modal. One state file, infinite reuse, until it expires.

Important rule for sites with long-lived auth cookies: do not call `state save` blindly. If the session you just ran is logged out for any reason, and you save it on top of your working auth file, you have clobbered your own credentials. The correct pattern for production sites is a guard script that backs up the existing auth file, verifies the new session is actually logged in, and only then overwrites. The Skool guard script in snappy-course is the canonical example of this pattern, and you can copy it for any new site.

The third automation is a parallel run. Open two terminals or run two background commands:

```
agent-browser --session site1 open https://site-a.com
agent-browser --session site2 open https://site-b.com
agent-browser --session site1 snapshot -i
agent-browser --session site2 snapshot -i
```

Each command operates on its own session, and the two sessions do not share cookies or state. That is the primitive that lets you run many agents against the same application with different accounts, or the same agent against many different applications at once. Session names are arbitrary strings you pick.

Where to go next depends on what you are building. If you are driving a new UI that no skill has mapped yet, install `snappy-dom-cartographer` from `https://skills.snappy.ai/skills/snappy-dom-cartographer` and invoke it as a subagent to produce the initial map before you write any automation. If you are driving a UI that snappy-course already knows (Skool classroom, lesson editor, course covers), everything is documented in the consumer skill's AGENTS.md and you can read the recipes directly. If you are coordinating multiple agents against the same application, use the session primitive from lesson 2 as the building block and isolate every worker to its own session.

The one rule that matters across all of these is: never `pkill agent-browser`. The daemon hosts every active session across every concurrent agent. Killing it wipes out everyone else's work, not just yours. When you are done, close your own session by name (`agent-browser --session mysession close`) and leave everything else alone.

That is the install, the core loop, the auth pattern, and the parallelism primitive. Everything else in the course layers on top of these. Go run one form fill against a real page and watch the loop happen. After that, you are already doing this.

Install agent-browser: `https://skills.snappy.ai/skills/agent-browser`
Install the cartographer: `https://skills.snappy.ai/skills/snappy-dom-cartographer`

---
title: "The loop that fixes itself"
course: Build with Snappy
tool: snappy-kernel
slug: 00-snappy-kernel
status: draft
source_files:
  - /Users/robertboulos/.claude/hooks/agents-md-footer.md
  - /Users/robertboulos/.claude/skills/snappy-settings/skill-spec.md
source_quotes:
  - speaker: snappy-kernel
    date: 2026-04-14
    quote: "If this AGENTS.md didn't cover your case -- if you had to read another file, run a command not documented here, work around a wrong selector, or discover a quirk -- you MUST attempt an Edit before you can log."
  - speaker: snappy-kernel
    date: 2026-04-14
    quote: "Every agent run must leave the system better than it found it."
draft_date: 2026-04-14
skool_url: https://www.skool.com/snappy/classroom/bab00d86?md=fbcd202da401439aab78306ace138182
shipped_at: 2026-04-14T00:00:00Z
q_audit:
  Q1: pass
  Q2: pass
  Q3: pass
  Q4: pass
  Q5: pass
  Q6: pass
  Q7: pass
  Q8: pass
q_audited_at: 2026-04-14T00:00:00Z
q_revision: 1
image_brief: |
  Layer 1 (template lock): cream-paper 1:1 ref image as the hard style lock.
  Layer 2 (subject): a flat 2D feedback loop diagram. A box labeled AGENT on the left, an arrow into a box labeled SKILL with a small footer slot at the bottom marked FOOTER. From the footer, a return arrow loops back to the AGENT carrying a tiny EDIT tag. Below the loop, a small file labeled feedback.log with a check mark. Fine-tip Micron pen architectural linework, black ink on warm cream paper, no color, no realism.
  Layer 3 (material): fine-tip Micron pen, architectural linework, consistent stroke weight, clean single-stroke outlines.
  Layer 4 (composition): illustration centered in upper two-thirds, generous whitespace, title in lower third.
  Layer 5 (typography): lower third, clean serif, title "Self-Correcting Loop".
  Layer 6 (constraints): black ink on warm cream paper, no color, no photographic realism, no gray fills.
image_url: https://robert-storage.tor1.digitaloceanspaces.com/skool/2026-04-14-build-with-snappy-00-snappy-kernel.png
image_cdn_url: https://robert-storage.tor1.digitaloceanspaces.com/skool/2026-04-14-build-with-snappy-00-snappy-kernel.png
image_generated_at: 2026-04-14T00:00:00Z
image_status: complete
---

A skill collection rots the way any documentation rots, and that is the visible failure.

The deeper failure is that every agent run is a chance for a learning to land where the next agent will read it, and most of those chances get thrown away. The quirk you discover in a selector never reaches the agent that hits it next week. The fix you apply to one skill never propagates to the others that share the same shape.

Without a substrate that forces knowledge to consolidate, a skill collection is not a system. It is a pile of private notebooks. The kernel is the minimum substrate that turns private notebooks into shared memory, and it is small enough that an agent handed only the kernel could build the rest of the system from scratch.

## The shape

A footer pinned to the bottom of every skill's `AGENTS.md` that the next agent reads on its way in. The footer says three things. First, if anything was missing or wrong, edit this file before you finish. Second, before you claim a result, attach a verification certificate that a fresh independent read could reproduce. Third, append one line to a feedback log either way. The footer is the contract. The agent is the proportional controller. The system improves on every pass because no run is allowed to leave it the same.

![Six labeled boxes arranged in a ring representing the kernel pieces, footer, preload hook, always-inject, feedback log, regen queue, and spec, connected by thin arrows.](https://robert-storage.tor1.digitaloceanspaces.com/images/inline/2026-04-14-00-snappy-kernel-inline-1.png)

## The minimum parts

The kernel is not the skills themselves. It is the small set of pieces that make the skills work as one system, and nothing more. If you were dropped on a fresh machine with only these pieces, you could point an agent at them and rebuild the rest.

- **The AGENTS.md footer.** A contract pinned to every skill loader: fix gaps inline, verify with certificates the actor cannot sign, log what you learned.
- **The preload hook.** When an agent mentions a skill name, the matching `AGENTS.md` auto-injects into context. Skills become discoverable by name, not by file path, which means knowledge travels with the name.
- **The always-inject list.** A tiny file naming the skills every agent must load unconditionally: credentials, dispatch, the kernel rules themselves. The handful of things no agent gets to forget.
- **The feedback log.** One append-only line per gap the system discovered. The audit trail of what was learned this week, readable by the next agent before it starts.
- **The regen queue.** A `PostToolUse` hook that re-runs loaders the moment a skill file is edited, so a fix lands in every agent's context on the next tick. No restart, no stale cache, no lost propagation.
- **The spec.** One file naming what "correct" means, so the actor-is-not-the-auditor rule has something concrete to check against.

Six pieces. Everything else, every skill, the dispatch layer, the content engine, the client deliverables, gets built by agents running inside this loop. That is the real test of a correct kernel: hand it to a fresh machine, point an agent at it, and watch the agent rebuild the rest from the feedback trail alone.

## The reference

The canonical footer lives in this repo at `~/.claude/hooks/agents-md-footer.md`. The spec it points to is `~/.claude/skills/snappy-settings/skill-spec.md`. Open the footer first. It is thirty lines and it defines the entire feedback loop in one screen.

```bash
cat ~/.claude/hooks/agents-md-footer.md
```

Read the section labelled "Fix gaps (P, proportional). MANDATORY." That is the load-bearing rule. Edit the file you are working in if you can fix the gap inline, log it if the fix is too big to inline, never both walk away and pretend it was fine.

## Build your own

You are going to write a tiny skill called `gap-fixer` that any agent can call when it discovers a doc gap. It writes one feedback line to a log and refuses to return success unless the fix was either applied or queued.

First make sure the log directory exists:

```bash
mkdir -p ~/.claude/logs
```

**`gap-fixer/AGENTS.md`** tells any agent how to use the skill:

```markdown
---
name: gap-fixer
role: Record and enforce documentation gap fixes
loaded-by: preload-skill-context hook
---

When you discover that a skill's AGENTS.md was missing something, a wrong
selector, a quirk, a command not documented, call `recordGap()` from
`./api.ts` with the skill name, the gap description, and either FIXED (you
already edited the file) or LOGGED (the fix is too large for inline and
needs the regen queue). The function refuses to accept anything else. You
may not declare your task done while a gap is OPEN.
```

**`gap-fixer/api.ts`** is the smallest function that does the thing:

```typescript
import { appendFileSync } from "node:fs";
import { homedir } from "node:os";
import { join } from "node:path";

const LOG = join(homedir(), ".claude/logs/agents-md-feedback.log");
type Status = "FIXED" | "LOGGED";

export function recordGap(skill: string, gap: string, status: Status) {
  if (status !== "FIXED" && status !== "LOGGED") {
    throw new Error(`gap-fixer: status must be FIXED or LOGGED, got ${status}`);
  }
  if (!skill || !gap) {
    throw new Error(`gap-fixer: skill and gap are required`);
  }
  const ts = new Date().toISOString();
  const line = `[${ts}] ${skill}: ${gap} [${status}]\n`;
  appendFileSync(LOG, line);
  return { ok: true, line: line.trim() };
}

if (import.meta.main) {
  const [skill, gap, status] = process.argv.slice(2);
  console.log(recordGap(skill, gap, status as Status));
}
```

**`gap-fixer/SKILL.md`** is the human reference, with activation keywords in the description so Claude Code picks it up:

```markdown
---
name: gap-fixer
description: Record a documentation gap in any skill. Use when the AGENTS.md was missing a step, a selector was wrong, a command was undocumented, or a quirk was discovered. Refuses to accept a gap without a FIXED or LOGGED status.
---

# gap-fixer

A two-line append to `~/.claude/logs/agents-md-feedback.log` that turns
"the next agent will figure it out" into a check the loop can audit.
```

Drop the folder under `~/.claude/skills/gap-fixer/`. Once the `preload-skill-context` hook runs, any agent that names `gap-fixer` in a prompt will load this file automatically.

## Try it

```bash
npx tsx ~/.claude/skills/gap-fixer/api.ts snappy-browse "agent-browser screenshot takes path positionally, not --output" FIXED
tail -1 ~/.claude/logs/agents-md-feedback.log
```

You should see the line you just wrote. Try it again with `MAYBE` as the third argument and watch the function refuse. The visible failure mode is the throw. A status the loop does not understand cannot be allowed to silently degrade into success. The whole point of the kernel is that the actor cannot grade its own work.

## What this taught you about the shape

The reusable insight is that the kernel is what turns private runs into shared memory. Every run is forced to either fix what it noticed or write down that it noticed, the preload hook pulls that fix into the next agent's context automatically, and the regen queue makes it land on the next tick instead of the next restart. The footer is the forcing function. The feedback log is the audit trail. The loader hook is the propagation layer.

Together they are the minimum substrate an agent needs to build everything else. Every other lesson in this course is a worker that runs inside this loop. `ralph` is a worker loop the kernel watches. `snappy-walkthrough` is a worker the kernel grades. `snappy-dom-cartographer` writes maps the kernel verifies. The forty-five lessons that follow this one are forty-five instances of the same shape: a worker, a verifier the worker cannot fudge, and a footer that records what the worker learned.

## Before the next lesson

Next you will build the worker side of this loop: a long-running iteration wrapper that keeps an agent honest about what "done" means. It is the inner loop the kernel runs above.

---
title: "Build a loop that refuses to quit early"
course: Build with Snappy
tool: ralph
slug: 01-ralph
status: draft
source_files:
  - /Users/robertboulos/.claude/skills/ralph/SKILL.md
source_quotes:
  - speaker: ralph
    date: 2026-04-14
    quote: "The loop will not accept \"done\" as an answer until a named exit condition is actually satisfied."
  - speaker: ralph
    date: 2026-04-14
    quote: "Whatever the criteria, they have to be checkable without a human looking at the output, because the whole point is that the loop runs without you."
draft_date: 2026-04-13
q_audit:
  Q1: pass
  Q2: pass
  Q3: pass
  Q4: pass
  Q5: pass
  Q6: pass
  Q7: pass
  Q8: pass
q_audited_at: 2026-04-13T00:00:00Z
q_revision: 2
image_brief: |
  Layer 1 (template lock): cream-paper 1:1 ref image as the hard style lock.
  Layer 2 (subject): Flat 2D diagram of a figure-8 loop: a box labeled AGENT on the left, an arrow into a box labeled VERIFIER on the right, a return arrow labeled 'not yet' looping back to the agent, and a small EXIT flag at the top of the verifier. A separate labeled box marked TEST SUITE feeds into the verifier. Fine-tip Micron pen architectural linework, black ink on warm cream paper, no color, no realism.
  Layer 3 (material): fine-tip Micron pen, architectural linework, consistent stroke weight, clean single-stroke outlines.
  Layer 4 (composition): illustration centered in upper two-thirds, generous whitespace, title in lower third.
  Layer 5 (typography): lower third, clean serif, title "The Loop".
  Layer 6 (constraints): black ink on warm cream paper, no color, no photographic realism, no gray fills.
image_url: https://robert-storage.tor1.digitaloceanspaces.com/skool/2026-04-14-build-with-snappy-01-ralph.png
image_cdn_url: https://robert-storage.tor1.digitaloceanspaces.com/skool/2026-04-14-build-with-snappy-01-ralph.png
image_generated_at: 2026-04-14T00:00:00Z
image_status: complete
skool_url: https://www.skool.com/snappy/classroom/bab00d86?md=a9f2e0efc208419693c99ddc41fb9bca
---

Claude Code has a failure mode on any task long enough to matter. The agent does some of the work, decides the work is done, and returns a summary that sounds reasonable until you look at what actually shipped. The thing was half built, the test was still red, the spec was close but not matched. Every long-running agent task needs an external check that the agent itself cannot fudge. This lesson teaches you to build one.

## The shape

A named exit condition, a loop around the agent, and a verifier the agent does not control. You write down what "really done" looks like in a form a machine can check (a green test suite, a 200 from three URLs, a visual diff under a threshold). The loop runs the agent, runs the verifier, and only stops when the verifier passes. The verifier is the contract. The loop is the enforcement. The agent is the worker that has no say in whether it gets to stop.

## The reference

Ralph is the canonical implementation of this shape in the snappy catalog. Install it locally so you can read its three files side by side with what you are about to write.

```bash
npx snappy-skills install ralph
```

Open `ralph/SKILL.md` and read it once. The whole skill is short on purpose: the shape is the substance.

## Build your own

You are going to write a tiny skill called `green-loop` that runs your test suite until it passes. Three files, no abstractions.

**`green-loop/AGENTS.md`** - tells any agent how to use the skill:

```markdown
---
name: green-loop
role: Iterate on a failing test suite until it passes
loaded-by: preload-skill-context hook
---

When the user asks to "make the tests pass" or "fix until green", call
`runUntilGreen()` from `./api.ts`. Do not declare done until the function
returns `{ ok: true }`. The function is the verifier; your opinion is not.
```

**`green-loop/api.ts`** - the smallest function that does the thing:

```typescript
import { execSync } from "node:child_process";

export function runUntilGreen(cmd = "npm test", maxIters = 20) {
  for (let i = 0; i < maxIters; i++) {
    try {
      execSync(cmd, { stdio: "inherit" });
      return { ok: true, iterations: i + 1 };
    } catch {
      console.log(`[green-loop] iteration ${i + 1} red, continuing`);
    }
  }
  return { ok: false, iterations: maxIters };
}

if (import.meta.main) {
  const result = runUntilGreen(process.argv[2]);
  process.exit(result.ok ? 0 : 1);
}
```

**`green-loop/SKILL.md`** - the human reference, with activation keywords in the description so Claude Code picks it up:

```markdown
---
name: green-loop
description: Loop that runs a test command until it exits zero. Use when the user says "make tests pass", "fix until green", or "iterate until the suite passes". Refuses to stop on a red run.
---

# green-loop

A named exit condition (the test command exits zero) and a loop that does
not let the agent declare done before it.
```

Drop the folder under `~/.claude/skills/green-loop/`. Claude Code picks it up the next time you mention it in a prompt.

## Try it

In a repo with at least one failing test:

```bash
npx tsx ~/.claude/skills/green-loop/api.ts "npm test"
```

You should see iterations print until the suite goes green or the cap trips. The visible failure mode is the cap tripping with `ok: false`. That is the whole point: when the loop cannot satisfy its exit condition, it says so loudly instead of quietly returning a fake success.

## What this taught you about the shape

The shape is "external verifier the worker cannot edit." Once you can spot it, you will see it everywhere in the catalog: `snappy-deploy` uses a healthcheck as its verifier, `snappy-image` uses a pixel diff, the kernel verification footer uses certificates the actor cannot sign for itself. The loop is interchangeable. The verifier is the load-bearing part.

## Before the next lesson

Next you will build a skill that hands grunt work to a cheaper model so loops like this stay economical even when they iterate twenty times.

---
title: "Hand grunt work to a cheaper brain"
course: Build with Snappy
tool: snappy-dispatch
slug: 02-snappy-dispatch
status: draft
source_files:
  - /Users/robertboulos/.claude/skills/snappy-dispatch/SKILL.md
source_quotes:
  - speaker: snappy-dispatch
    date: 2026-04-14
    quote: "Mechanical and verifiable -> dispatch. Needs judgment -> keep."
  - speaker: snappy-dispatch
    date: 2026-04-14
    quote: "One orchestrator brain, many cheap hands."
draft_date: 2026-04-14
q_audit: Q1-Q8: pass
q_audited_at: 2026-04-14T00:00:00Z
q_revision: 2
image_brief: |
  Layer 1 (template lock): cream-paper 1:1 ref image as the hard style lock.
  Layer 2 (subject): Flat 2D diagram of a central box labeled ORCHESTRATOR at the top, four arrows fanning out to four small identical boxes labeled DISPATCH at the bottom, each dispatch box with a tiny return arrow carrying a small STDOUT label. A log scroll sits beside the orchestrator. Fine-tip Micron pen, black ink on cream paper, clean single-stroke linework.
  Layer 3 (material): fine-tip Micron pen, architectural linework, consistent stroke weight, clean single-stroke outlines.
  Layer 4 (composition): illustration centered in upper two-thirds, generous whitespace, title in lower third.
  Layer 5 (typography): lower third, clean serif, title "Dispatch".
  Layer 6 (constraints): black ink on warm cream paper, no color, no photographic realism, no gray fills.
image_url: https://robert-storage.tor1.digitaloceanspaces.com/skool/2026-04-14-build-with-snappy-02-snappy-dispatch.png
image_cdn_url: https://robert-storage.tor1.digitaloceanspaces.com/skool/2026-04-14-build-with-snappy-02-snappy-dispatch.png
image_generated_at: 2026-04-14T00:00:00Z
image_status: complete
skool_url: https://www.skool.com/snappy/classroom/bab00d86?md=37a38d1189ec46bb87bc5730baf7d86f
---

A long agent session burns tokens on work the main model should never have touched. Counting files, grepping a log, checking whether a URL still returns 200, labeling fifty support tickets by category. None of that needs the expensive brain at the center of your workflow, but by default it all runs there. This lesson teaches you to route that work to a cheaper model with a typed function call.

## The shape

One orchestrator, many cheap hands. The orchestrator decides what needs doing. A dispatch function shells out to a small local agent running on a cheap model with a tight tool set and a short prompt. The cheap agent does the mechanical thing and returns a string. The orchestrator reads the string and moves on. The contract is simple: in goes a self-contained prompt, out comes a captured stdout, every call is logged for later cost audits.

## The reference

`snappy-dispatch` is the canonical implementation. It wraps `pi` (a local coding agent CLI) with model aliases, a tools allow-list, and structured logging. Install it and read all three files side by side.

```bash
npx snappy-skills install snappy-dispatch
```

The file worth staring at is `api.ts`. The whole lever is a single typed function.

## Build your own

You are going to write `cheap-classify`, a skill that labels support tickets as `billing`, `bug`, or `feature-request` by dispatching to a cheap model over OpenRouter. Three files.

**`cheap-classify/AGENTS.md`**:

```markdown
---
name: cheap-classify
role: Label a support ticket as billing, bug, or feature-request
loaded-by: preload-skill-context hook
---

When the user pastes a ticket and asks for a label, call `classifyTicket(text)`
from `./api.ts`. The function returns one of three exact strings. Do not
paraphrase the label. The function is the classifier, not you.
```

**`cheap-classify/api.ts`**:

```typescript
import { dispatch } from "../snappy-dispatch/api.ts";

export async function classifyTicket(text: string): Promise<string> {
  const r = await dispatch({
    prompt: `Label this support ticket as exactly one of: billing, bug, feature-request. Output only the label, no punctuation. Ticket:\n\n${text}`,
    model: "gemini",
    tools: [],
    timeoutMs: 30_000,
  });
  if (!r.ok) throw new Error(r.error || r.stderr);
  const label = r.output.trim().toLowerCase();
  if (!["billing", "bug", "feature-request"].includes(label)) {
    throw new Error(`cheap-classify got unexpected label: ${label}`);
  }
  return label;
}

if (import.meta.url === `file://${process.argv[1]}`) {
  const text = process.argv.slice(2).join(" ");
  classifyTicket(text).then(l => console.log(l));
}
```

**`cheap-classify/SKILL.md`**:

```markdown
---
name: cheap-classify
description: Classify a support ticket as billing, bug, or feature-request. Use when the user says "label this ticket", "classify this", or pastes a ticket and asks what category it is. Dispatches to a cheap model via snappy-dispatch.
---

# cheap-classify

A three-label classifier over support tickets. Refuses any label outside the
fixed set. Costs sub-penny per call.
```

## Try it

```bash
npx tsx ~/.claude/skills/cheap-classify/api.ts "My card was charged twice for last month's invoice"
```

You should see `billing` on stdout. The visible failure mode is the validation throw: if the cheap model returns anything outside the three allowed strings, the function refuses the result instead of silently passing garbage upstream.

## What this taught you about the shape

The shape is "typed wrapper around a cheaper worker, with a validator at the boundary." The wrapper makes the call cheap, the validator makes the result safe to trust. You will see the same shape in `snappy-ai-models` (typed wrapper around direct OpenAI calls) and `snappy-openrouter` (typed wrapper around OpenRouter), where the catalog funnels every model call through one validated entry point instead of scattering provider-specific SDK calls everywhere.

## Before the next lesson

Next you will build a skill that reuses the same cheap brain for a full interactive session, not just one shot.

---
title: "Wrap a reusable prompt as a one-shot runner"
course: Build with Snappy
tool: snappy-shell
slug: 03-snappy-shell
status: draft
source_files:
  - /Users/robertboulos/.claude/skills/snappy-shell/SKILL.md
source_quotes:
  - speaker: snappy-shell
    date: 2026-04-14
    quote: "This is not the cheap-labor lever. That's snappy-dispatch. snappy-shell is a peer runner -- a Claude Code substitute, not a sub-agent fan-out."
  - speaker: snappy-shell
    date: 2026-04-14
    quote: "It reconstructs the kernel as a portable system prompt (global CLAUDE.md + every skill listed in always-inject.txt) and boots pi with that prompt, pointed at whichever provider has credentials."
draft_date: 2026-04-14
q_audit: Q1-Q8: pass
q_audited_at: 2026-04-14T00:00:00Z
q_revision: 2
image_brief: |
  Layer 1 (template lock): cream-paper 1:1 ref image as the hard style lock.
  Layer 2 (subject): Flat 2D diagram of a scroll labeled PROMPT wrapped inside a thin-bordered envelope labeled CONTEXT, with a single arrow pointing right at a row of three labeled boxes: OPENAI, OPENROUTER, LOCAL. Fine-tip Micron pen architectural linework, black ink on warm cream paper, no color.
  Layer 3 (material): fine-tip Micron pen, architectural linework, consistent stroke weight, clean single-stroke outlines.
  Layer 4 (composition): illustration centered in upper two-thirds, generous whitespace, title in lower third.
  Layer 5 (typography): lower third, clean serif, title "Wrapper".
  Layer 6 (constraints): black ink on warm cream paper, no color, no photographic realism, no gray fills.
image_url: https://robert-storage.tor1.digitaloceanspaces.com/skool/2026-04-14-build-with-snappy-03-snappy-shell.png
image_cdn_url: https://robert-storage.tor1.digitaloceanspaces.com/skool/2026-04-14-build-with-snappy-03-snappy-shell.png
image_generated_at: 2026-04-14T00:00:00Z
image_status: complete
skool_url: https://www.skool.com/snappy/classroom/bab00d86?md=b21941ac303e45c59adb74c7c0fa6b5b
---

Claude Code is the main way you run the snappy kernel, but it is not the only possible way. Quota runs out. The provider has an outage. You want a second opinion from a different model on the same task. The kernel itself is just text: a global bootstrap file plus a stack of AGENTS.md loaders. Any agent runtime that accepts a system prompt can host it. This lesson teaches you how to reuse one specific prompt across any brain you have an API key for.

## The shape

A one-shot runner that takes a prompt you already wrote, prepends a small amount of shared context, and ships the combined thing to whichever model you pick. The runner does not invent the prompt. It does not try to be clever. It just wraps your canonical prompt text so you can run it through OpenAI, OpenRouter, or a local agent CLI without rewriting it each time.

## The reference

`snappy-shell` is the canonical implementation. It rebuilds the whole snappy kernel as a portable system prompt and boots `pi` (a local agent CLI) against it. For this lesson you only need the core idea: wrap a reusable prompt, point it at a provider, run it.

```bash
npx snappy-skills install snappy-shell
```

## Build your own

You are going to write `pi-runner`, a skill that wraps your single most-used prompt (pretend it is "review the diff in the current git repo and list risky changes") so you can fire it off one-shot against any model.

**`pi-runner/AGENTS.md`**:

```markdown
---
name: pi-runner
role: One-shot runner for the canonical diff-review prompt
loaded-by: preload-skill-context hook
---

When the user says "review my diff" or "risky changes", call `runDiffReview()`
from `./api.ts`. The function runs a fixed prompt against the configured model
and returns the stdout as a plain string. Do not rewrite the prompt; the whole
point is that it is the same every time.
```

**`pi-runner/api.ts`**:

```typescript
import { spawnSync } from "node:child_process";
import { env } from "../snappy-settings/load.ts";

const CANONICAL_PROMPT = `
You are reviewing the output of "git diff" in the current repo. List any
change that could cause a regression. For each risky change, give the file,
line number, and one sentence why it is risky. Ignore safe changes completely.
`.trim();

export function runDiffReview(model = "google/gemini-2.5-flash"): string {
  const apiKey = env("OPENROUTER_API_KEY");
  const r = spawnSync("pi", [
    "--print", "--no-session",
    "--provider", "openrouter",
    "--model", model,
    "--tools", "bash,read,grep",
    CANONICAL_PROMPT,
  ], { env: { ...process.env, OPENROUTER_API_KEY: apiKey }, encoding: "utf8" });
  if (r.status !== 0) throw new Error(r.stderr || "pi-runner failed");
  return r.stdout;
}

if (import.meta.url === `file://${process.argv[1]}`) {
  console.log(runDiffReview(process.argv[2]));
}
```

**`pi-runner/SKILL.md`**:

```markdown
---
name: pi-runner
description: Run the canonical diff-review prompt against any OpenRouter model in one shot. Use when the user says "review my diff", "check for risky changes", or "run the diff review on gemini". Prompt is fixed so results are comparable across models.
---

# pi-runner

Wraps one prompt you actually use every day. Fixed text, swappable brain.
```

## Try it

```bash
cd ~/some-repo-with-uncommitted-changes
npx tsx ~/.claude/skills/pi-runner/api.ts google/gemini-2.5-flash
```

You should see a list of risky lines printed to stdout. The visible failure mode is a non-zero exit from `pi`: if the key is missing or the model name is wrong, the function throws instead of returning an empty string that looks like "nothing risky."

## What this taught you about the shape

The shape is "reusable prompt, swappable backend." Once the prompt is captured as a constant inside a typed function, the model becomes a parameter instead of a decision you make every time you fire it. You will see the same shape in `snappy-openrouter` (one canonical HTTP wrapper, N models behind it) and `snappy-gemini` (one canonical image-gen wrapper, N Gemini endpoints behind it).

## Before the next lesson

Next you will use a meta-skill to scaffold a new three-file skill for you, then compare the generated shape to the ones you wrote by hand.

---
title: "Scaffold a skill with a skill"
course: Build with Snappy
tool: snappy-skill
slug: 04-snappy-skill
status: draft
source_files:
  - /Users/robertboulos/.claude/skills/snappy-skill/SKILL.md
source_quotes:
  - speaker: snappy-skill
    date: 2026-04-14
    quote: "Three required files: SKILL.md, AGENTS.md (with self-correcting footer), api.ts (typed, CLI-capable)"
  - speaker: snappy-skill
    date: 2026-04-14
    quote: "Fragmentation is the enemy."
draft_date: 2026-04-14
q_audit: Q1-Q8: pass
q_audited_at: 2026-04-14T00:00:00Z
q_revision: 2
image_brief: |
  Layer 1 (template lock): cream-paper 1:1 ref image as the hard style lock.
  Layer 2 (subject): Flat 2D diagram of three stacked files labeled SKILL.md, AGENTS.md, api.ts inside a folder with a small gear at the top indicating a generator, plus a small check-mark labeled SMOKE TEST on the right. Fine-tip Micron pen, black ink on cream paper, clean linework.
  Layer 3 (material): fine-tip Micron pen, architectural linework, consistent stroke weight, clean single-stroke outlines.
  Layer 4 (composition): illustration centered in upper two-thirds, generous whitespace, title in lower third.
  Layer 5 (typography): lower third, clean serif, title "Scaffold".
  Layer 6 (constraints): black ink on warm cream paper, no color, no photographic realism, no gray fills.
image_url: https://robert-storage.tor1.digitaloceanspaces.com/skool/2026-04-14-build-with-snappy-04-snappy-skill.png
image_cdn_url: https://robert-storage.tor1.digitaloceanspaces.com/skool/2026-04-14-build-with-snappy-04-snappy-skill.png
image_generated_at: 2026-04-14T00:00:00Z
image_status: complete
skool_url: https://www.skool.com/snappy/classroom/bab00d86?md=7d25fe10657a458db384ccb9f821db6b
---

By now you have written a few three-file skills by hand. That is how you learn the shape. But once you know the shape, hand-writing every new skill is just reciting the same skeleton, and a tired author skips the boring parts. The self-correcting AGENTS.md footer gets left off. The `env()` import gets swapped for a hardcoded token. The `Triggers on:` line goes missing. A meta-skill that scaffolds the three files from a single interview cures all of that.

## The shape

A skill that writes other skills. It takes a name and a one-line purpose, runs a duplicate check against the catalog, creates the three required files with the right boilerplate already in place, and leaves you with a working CLI smoke test before it returns. It enforces the rules instead of asking you to remember them.

## The reference

`snappy-skill` is the canonical implementation in the snappy namespace. It is how every new `snappy-*` skill should be created. Install it and read the full SKILL.md once.

```bash
npx snappy-skills install snappy-skill
```

Pay attention to the duplicate-check step. Any new skill creation runs `dry-check.sh` first and refuses if the capability already lives somewhere in the catalog. Fragmentation is the enemy.

## Build your own

You are going to use `snappy-skill` itself to scaffold a throwaway skill called `weather-fetch` that reads the current temperature in a city from a public API. Then you will diff it against the skill you wrote by hand in lesson 01 and notice what the scaffolder got right that you would have missed.

**`weather-fetch/AGENTS.md`** (scaffolded, then lightly edited):

```markdown
---
name: weather-fetch
role: Return current temperature in celsius for a named city
loaded-by: preload-skill-context hook
---

When the user asks "what is the weather in <city>", call `getTemperature(city)`
from `./api.ts`. The function returns a number in celsius. If the API is down,
the function throws; do not invent a number.
```

**`weather-fetch/api.ts`**:

```typescript
import { env } from "../snappy-settings/load.ts";

export async function getTemperature(city: string): Promise<number> {
  const key = env("OPENWEATHER_API_KEY");
  const url = `https://api.openweathermap.org/data/2.5/weather?q=${encodeURIComponent(city)}&units=metric&appid=${key}`;
  const res = await fetch(url);
  if (!res.ok) throw new Error(`weather-fetch: ${res.status}`);
  const data = await res.json() as { main: { temp: number } };
  return data.main.temp;
}

if (import.meta.url === `file://${process.argv[1]}`) {
  const city = process.argv.slice(2).join(" ") || "Montreal";
  getTemperature(city).then(t => console.log(`${city}: ${t}C`));
}
```

**`weather-fetch/SKILL.md`**:

```markdown
---
name: weather-fetch
description: Return the current temperature in celsius for a named city. Use when the user asks "what is the weather in X", "how warm is it in X", or "temperature in X right now". Calls the OpenWeather API directly.

Triggers on: weather, temperature, how warm, how cold, current weather.
---

# weather-fetch

One public API call, one typed return, no caching, no history. Refuses to
invent a number when the API is unreachable.
```

The scaffolder generates these with the `env()` import, the CLI guard, and the `Triggers on:` line already present. The hand-written version from lesson 01 had none of those by default, because you had to remember them.

## Try it

```bash
# Add OPENWEATHER_API_KEY to ~/.claude/skills/snappy-settings/.env.cache first
npx tsx ~/.claude/skills/weather-fetch/api.ts "Montreal"
```

You should see a line like `Montreal: 4.2C`. The visible failure mode is the throw: a 401 from a missing key, a 404 from a typo in the city name. Compare the resulting folder to your hand-written lesson-01 skill. Count the things the scaffolder put in that you forgot.

## What this taught you about the shape

The shape is "meta-skill that writes skills." It is a recursive loop: the system uses its own conventions to create new parts of itself, and the conventions get enforced by the creator instead of the author. You will see the same pattern in `skill-builder` (the generic ancestor outside the snappy namespace, covered in the next lesson) and in `snappy-client-template`, which is the per-client variant `snappy-skill` is built to instantiate.

## Before the next lesson

Next you will use the generic, non-snappy scaffolder and see what changes when the target namespace is not opinionated.

---
title: "Progressive disclosure across resource files"
course: Build with Snappy
tool: skill-builder
slug: 05-skill-builder
status: draft
source_files:
  - /Users/robertboulos/.claude/skills/skill-builder/SKILL.md
source_quotes:
  - speaker: skill-builder
    date: 2026-04-14
    quote: "SKILL.md < 500 lines"
  - speaker: skill-builder
    date: 2026-04-14
    quote: "One file per major topic"
draft_date: 2026-04-14
q_audit: Q1-Q8: pass
q_audited_at: 2026-04-14T00:00:00Z
q_revision: 2
image_brief: |
  Layer 1 (template lock): cream-paper 1:1 ref image as the hard style lock.
  Layer 2 (subject): Flat 2D diagram of a single document at the center labeled SKILL.md with a small table of rows, and four arrows from individual rows pointing outward to four smaller document icons labeled RESOURCE. Fine-tip Micron pen, black ink on warm cream paper.
  Layer 3 (material): fine-tip Micron pen, architectural linework, consistent stroke weight, clean single-stroke outlines.
  Layer 4 (composition): illustration centered in upper two-thirds, generous whitespace, title in lower third.
  Layer 5 (typography): lower third, clean serif, title "The Map".
  Layer 6 (constraints): black ink on warm cream paper, no color, no photographic realism, no gray fills.
image_url: https://robert-storage.tor1.digitaloceanspaces.com/skool/2026-04-14-build-with-snappy-05-skill-builder.png
image_cdn_url: https://robert-storage.tor1.digitaloceanspaces.com/skool/2026-04-14-build-with-snappy-05-skill-builder.png
image_generated_at: 2026-04-14T00:00:00Z
image_status: complete
skool_url: https://www.skool.com/snappy/classroom/bab00d86?md=44242f10473d4af680fadf642278fe09
---

A skill that tries to put everything in one SKILL.md either gets skimmed and ignored, or gets so long nobody reads it. There is a ceiling around 500 lines where one file stops being a reference and starts being a wall of text. The fix is not shorter writing; the fix is splitting one file into a map plus territory. A short main file with a navigation table, and one resource file per topic the reader might actually need to open.

## The shape

Progressive disclosure. The main file is a map: purpose, when to use, a navigation guide table, and a quick reference with copy-paste patterns. Each row of the navigation table points at a resource file that holds the real detail for one topic. The reader lands on the main file, scans the table, and only opens the resource file they need right now. Nothing is duplicated. Nothing is hidden.

## The reference

`skill-builder` is the canonical implementation of this pattern in the public skills catalog. It is the generic ancestor of `snappy-skill` from the previous lesson, and it is the right starting point for any skill that does not need to live in the snappy namespace.

```bash
npx snappy-skills install skill-builder
```

Read its SKILL.md and notice how short the main file stays. The rules about 500 lines and one file per topic are load-bearing.

## Build your own

You are going to write `repo-stats`, a skill that reports line counts, file counts, and last-commit timestamps for a git repo. The skill will ship with two resource files so you can feel the difference between a flat monolith and a map.

**`repo-stats/AGENTS.md`**:

```markdown
---
name: repo-stats
role: Report line counts, file counts, and freshness for a git repo
loaded-by: preload-skill-context hook
---

When the user asks "how big is this repo" or "when was this last touched",
call `getRepoStats(path)` from `./api.ts`. For the output format and the
git-plumbing details, read patterns.md or git.md in this directory.
```

**`repo-stats/api.ts`**:

```typescript
import { execSync } from "node:child_process";
import { readdirSync, statSync } from "node:fs";
import { join } from "node:path";

export interface RepoStats {
  fileCount: number;
  lineCount: number;
  lastCommitIso: string;
}

export function getRepoStats(repoPath: string): RepoStats {
  let fileCount = 0;
  let lineCount = 0;
  const walk = (dir: string) => {
    for (const entry of readdirSync(dir)) {
      if (entry === ".git" || entry === "node_modules") continue;
      const full = join(dir, entry);
      const s = statSync(full);
      if (s.isDirectory()) walk(full);
      else if (s.isFile()) {
        fileCount++;
        const out = execSync(`wc -l < "${full}"`, { encoding: "utf8" }).trim();
        lineCount += parseInt(out, 10) || 0;
      }
    }
  };
  walk(repoPath);
  const lastCommitIso = execSync(`git -C "${repoPath}" log -1 --format=%cI`, { encoding: "utf8" }).trim();
  return { fileCount, lineCount, lastCommitIso };
}

if (import.meta.url === `file://${process.argv[1]}`) {
  console.log(getRepoStats(process.argv[2] || "."));
}
```

**`repo-stats/SKILL.md`** (the map):

```markdown
---
name: repo-stats
description: Report file count, total line count, and last-commit timestamp for a local git repo. Use when the user asks "how big is this repo", "when was this last updated", or "give me stats on this codebase".
---

# repo-stats

Small, fast, read-only.

## Navigation

| Need to... | Read |
|---|---|
| See output patterns and example invocations | patterns.md |
| Understand the git plumbing used for freshness | git.md |

## Quick reference

    npx tsx ~/.claude/skills/repo-stats/api.ts /path/to/repo
```

Then create two resource files: `repo-stats/patterns.md` with example outputs and how to format them, and `repo-stats/git.md` with notes on why `git log -1 --format=%cI` is used instead of file mtimes. The main file stays a map. The detail lives where it belongs.

## Try it

```bash
npx tsx ~/.claude/skills/repo-stats/api.ts ~/some/repo
```

You should see a JSON object with three fields. The visible failure mode is an exception from `git log` when the path is not a git repo; the function does not return a zeroed-out fake stat.

## What this taught you about the shape

The shape is "map plus territory." The main file is a table of contents the reader scans; the resource files are the pages the reader opens only when they need them. You will see this same pattern in `snappy-course` (a short AGENTS.md that links out to data files per topic) and in `find-skills` from lesson 07 (a main file that links to an EXAMPLES.md for deeper patterns).

## Before the next lesson

Next you will build a hook that runs every time the user submits a prompt, so the skill system can react to live input without waiting to be asked.

---
title: "Inject today's date into every prompt"
course: Build with Snappy
tool: snappy-hooks
slug: 06-snappy-hooks
status: draft
source_files:
  - /Users/robertboulos/.claude/skills/snappy-hooks/SKILL.md
source_quotes:
  - speaker: snappy-hooks
    date: 2026-04-14
    quote: "Thin read-only skill over the Claude Code hook configuration."
  - speaker: snappy-hooks
    date: 2026-04-14
    quote: "listHooks() - reads ~/.claude/settings.json and returns the configured hook matchers + commands per event."
draft_date: 2026-04-14
q_audit: Q1-Q8: pass
q_audited_at: 2026-04-14T00:00:00Z
q_revision: 2
image_brief: |
  Layer 1 (template lock): cream-paper 1:1 ref image as the hard style lock.
  Layer 2 (subject): Flat 2D diagram of an arrow labeled USER PROMPT on the left passing through a small vertical slot labeled HOOK with a tiny injection arrow, then continuing into a box labeled CLAUDE. A small file tag reads settings.json beneath the hook. Fine-tip Micron pen, black ink on cream paper.
  Layer 3 (material): fine-tip Micron pen, architectural linework, consistent stroke weight, clean single-stroke outlines.
  Layer 4 (composition): illustration centered in upper two-thirds, generous whitespace, title in lower third.
  Layer 5 (typography): lower third, clean serif, title "Prompt Hook".
  Layer 6 (constraints): black ink on warm cream paper, no color, no photographic realism, no gray fills.
image_url: https://robert-storage.tor1.digitaloceanspaces.com/skool/2026-04-14-build-with-snappy-06-snappy-hooks.png
image_cdn_url: https://robert-storage.tor1.digitaloceanspaces.com/skool/2026-04-14-build-with-snappy-06-snappy-hooks.png
image_generated_at: 2026-04-14T00:00:00Z
image_status: complete
skool_url: https://www.skool.com/snappy/classroom/bab00d86?md=7b6a071310224d049ad76b69ecb4e48c
---

Ask an agent "when is next Tuesday" and it will answer relative to its training cutoff, which is probably months stale. Ask it to file something "today" and the dated filename comes out wrong. The agent does not know what day it is because nothing in its context window tells it. You can fix that by hand every session, or you can do it once with a hook that fires on every prompt you submit.

## The shape

A user-prompt-submit hook is a tiny shell script that Claude Code runs on your input before the model sees it. The script can inject any text it wants ahead of your prompt. That text lands in the model's context as if you had typed it yourself. The script is stateless, fast, and lives in `~/.claude/hooks/`. It is wired in `~/.claude/settings.json` under the `UserPromptSubmit` event. A skill that packages the setup turns the whole thing into one install.

## The reference

`snappy-hooks` is the read-only introspection skill over the Claude Code hook config. It tells you which hooks are currently wired and whether each script exists and is executable. Install it so you can inspect your changes after you make them.

```bash
npx snappy-skills install snappy-hooks
```

Read its `listHooks()` output once so you know what a healthy config looks like before you start adding to it.

## Build your own

You are going to write `date-injector`, a skill that installs a `UserPromptSubmit` hook printing today's ISO date ahead of every prompt you submit.

**`date-injector/AGENTS.md`**:

```markdown
---
name: date-injector
role: Inject today's ISO date at the top of every user prompt
loaded-by: preload-skill-context hook
---

When the user asks "install the date hook" or "why doesn't the agent know
today's date", call `installDateHook()` from `./api.ts`. The function writes
the hook script and patches settings.json. It is idempotent.
```

**`date-injector/api.ts`**:

```typescript
import { writeFileSync, readFileSync, chmodSync, existsSync, mkdirSync } from "node:fs";
import { homedir } from "node:os";
import { join } from "node:path";

const HOOK_PATH = join(homedir(), ".claude/hooks/inject-date.sh");
const SETTINGS_PATH = join(homedir(), ".claude/settings.json");

const HOOK_SCRIPT = `#!/usr/bin/env bash
# Prepends today's date to every user prompt.
TODAY=$(date -u +%Y-%m-%d)
INPUT=$(cat)
printf 'Today is %s (UTC).\\n\\n%s\\n' "$TODAY" "$INPUT"
`;

export function installDateHook(): { installed: boolean; hookPath: string } {
  mkdirSync(join(homedir(), ".claude/hooks"), { recursive: true });
  writeFileSync(HOOK_PATH, HOOK_SCRIPT);
  chmodSync(HOOK_PATH, 0o755);

  const settings = existsSync(SETTINGS_PATH)
    ? JSON.parse(readFileSync(SETTINGS_PATH, "utf8"))
    : {};
  settings.hooks = settings.hooks || {};
  settings.hooks.UserPromptSubmit = settings.hooks.UserPromptSubmit || [];
  const already = settings.hooks.UserPromptSubmit.some(
    (h: any) => h.command === HOOK_PATH,
  );
  if (!already) {
    settings.hooks.UserPromptSubmit.push({ matcher: "*", command: HOOK_PATH });
  }
  writeFileSync(SETTINGS_PATH, JSON.stringify(settings, null, 2));
  return { installed: !already, hookPath: HOOK_PATH };
}

if (import.meta.url === `file://${process.argv[1]}`) {
  console.log(installDateHook());
}
```

**`date-injector/SKILL.md`**:

```markdown
---
name: date-injector
description: Install a UserPromptSubmit hook that prepends today's ISO date to every prompt. Use when the user says "install the date hook", "the agent does not know what day it is", or "add a prompt-time date injector".
---

# date-injector

One hook, idempotent installer, no uninstall step required. Stops the agent
from guessing the current date.
```

## Try it

```bash
npx tsx ~/.claude/skills/date-injector/api.ts
```

Then start a fresh Claude Code session and ask "what day is it". The response should name today's date correctly. The visible failure mode is the agent still answering with a wrong or hedged date, which means the hook is not wired - run `listHooks()` from `snappy-hooks` to confirm the entry landed.

## What this taught you about the shape

The shape is "small shell script at a hook point, wired once, invisible after." Hooks are how you add ambient context or enforcement to every agent session without touching any skill. You will see the same shape in `snappy-sensors` (ambient context injected via the always-inject list) and in the `PostToolUse` regen queue that `snappy-skill` relies on to rebuild loaders after edits.

## Before the next lesson

Next you will build a skill that searches the public catalog so the agent reaches for existing work before writing new work.

---
title: "Search the public catalog before you build"
course: Build with Snappy
tool: find-skills
slug: 07-find-skills
status: draft
source_files:
  - /Users/robertboulos/.claude/skills/find-skills/SKILL.md
source_quotes:
  - speaker: find-skills
    date: 2026-04-14
    quote: "The Skills CLI (npx skills) is the package manager for the open agent skills ecosystem."
  - speaker: find-skills
    date: 2026-04-14
    quote: "npx skills find [query] - Search for skills interactively or by keyword"
draft_date: 2026-04-14
q_audit: Q1-Q8: pass
q_audited_at: 2026-04-14T00:00:00Z
q_revision: 2
image_brief: |
  Layer 1 (template lock): cream-paper 1:1 ref image as the hard style lock.
  Layer 2 (subject): Flat 2D diagram of a magnifying glass shape drawn as pure linework hovering over a stack of labeled index cards, with an arrow pointing right to a small list of rows labeled NAME, DESC, INSTALL. Fine-tip Micron pen, black ink on warm cream paper.
  Layer 3 (material): fine-tip Micron pen, architectural linework, consistent stroke weight, clean single-stroke outlines.
  Layer 4 (composition): illustration centered in upper two-thirds, generous whitespace, title in lower third.
  Layer 5 (typography): lower third, clean serif, title "The Catalog".
  Layer 6 (constraints): black ink on warm cream paper, no color, no photographic realism, no gray fills.
image_url: https://robert-storage.tor1.digitaloceanspaces.com/skool/2026-04-14-build-with-snappy-07-find-skills.png
image_cdn_url: https://robert-storage.tor1.digitaloceanspaces.com/skool/2026-04-14-build-with-snappy-07-find-skills.png
image_generated_at: 2026-04-14T00:00:00Z
image_status: complete
skool_url: https://www.skool.com/snappy/classroom/bab00d86?md=fb09e3c4667745518f849c3593a025ab
---

The worst way to build a skill is to build one that already exists. Somebody in the open catalog has probably already solved your problem, and their version has been used by other people and debugged in situations yours has not seen yet. If the agent does not reach for the catalog before reaching for a blank editor, you end up with a drawer full of near-duplicates that each do one thing slightly wrong. This lesson teaches you to wrap the catalog search as its own skill.

## The shape

A typed function over a search API. Take a query string, hit the catalog endpoint, return a small array of results with name, description, install command, and a permalink. No caching, no retry logic, no opinion about what to do with the results. The consumer (you, or the next agent) decides whether to install one.

## The reference

`find-skills` is the canonical implementation. It wraps the Skills CLI (`npx skills`) and teaches the agent the decision loop: recognize an intent, translate to a query, search, present, offer to install.

```bash
npx snappy-skills install find-skills
```

Read its SKILL.md and note the four CLI commands. Your wrapper is going to make one of them, `find`, callable from code instead of only from a terminal.

## Build your own

You are going to write `catalog-search`, a skill with one typed function that wraps `npx skills find` and returns a parsed array.

**`catalog-search/AGENTS.md`**:

```markdown
---
name: catalog-search
role: Search the public skills catalog by keyword
loaded-by: preload-skill-context hook
---

When the user asks "is there a skill for X" or "find a skill that does Y",
call `searchCatalog(query)` from `./api.ts`. The function returns an array of
{ name, repo, url }. Present the top few to the user; do not install without
asking.
```

**`catalog-search/api.ts`**:

```typescript
import { execSync } from "node:child_process";

export interface CatalogHit {
  name: string;
  repo: string;
  url: string;
}

export function searchCatalog(query: string): CatalogHit[] {
  const out = execSync(`npx --yes skills find ${JSON.stringify(query)}`, {
    encoding: "utf8",
  });
  const hits: CatalogHit[] = [];
  // Parse lines like: "vercel-labs/agent-skills@vercel-react-best-practices"
  for (const line of out.split("\n")) {
    const m = line.trim().match(/^([\w.-]+\/[\w.-]+)@([\w.-]+)$/);
    if (m) {
      hits.push({
        name: m[2],
        repo: m[1],
        url: `https://skills.sh/${m[1]}/${m[2]}`,
      });
    }
  }
  return hits;
}

if (import.meta.url === `file://${process.argv[1]}`) {
  const q = process.argv.slice(2).join(" ");
  for (const h of searchCatalog(q)) {
    console.log(`${h.name}  (${h.repo})  ${h.url}`);
  }
}
```

**`catalog-search/SKILL.md`**:

```markdown
---
name: catalog-search
description: Search the public skills catalog by keyword and return parsed results. Use when the user says "is there a skill for X", "find a skill that does Y", or "search the catalog for Z". Returns name, repo, and skills.sh URL per hit.
---

# catalog-search

Typed wrapper over `npx skills find`. Returns structured results instead of
raw CLI output.
```

## Try it

```bash
npx tsx ~/.claude/skills/catalog-search/api.ts "pr review"
```

You should see one line per hit with name, repo, and URL. The visible failure mode is an empty array when the catalog is up but the query matched nothing - widen the query (`testing` not `jest`) and try again.

## What this taught you about the shape

The shape is "typed function over an existing CLI, structured output." You wrap a tool the terminal already knows how to run and make it callable from code with a predictable return type. You will see the same shape in `snappy-github` (typed wrapper over the `gh` CLI) and in `snappy-ffmpeg` (typed wrapper over `ffmpeg` with parsed output).

## Before the next lesson

Next you will build a skill that guards against the most common install failure: a skill that lands in the wrong directory and is silently invisible.

---
title: "Verify an install landed in the right scope"
course: Build with Snappy
tool: skills-install
slug: 08-skills-install
status: draft
source_files:
  - /Users/robertboulos/.claude/skills/skills-install/SKILL.md
source_quotes:
  - speaker: skills-install
    date: 2026-04-14
    quote: "the scope depends on the directory you were in when you started the Claude Code session that did the installing."
  - speaker: skills-install
    date: 2026-04-14
    quote: "A skill that is in the wrong scope looks identical to a skill that does not exist at all"
draft_date: 2026-04-14
q_audit: Q1-Q8: pass
q_audited_at: 2026-04-14T00:00:00Z
q_revision: 2
image_brief: |
  Layer 1 (template lock): cream-paper 1:1 ref image as the hard style lock.
  Layer 2 (subject): Flat 2D diagram of a labeled package on top, an arrow pointing down into a branching tree with two paths labeled GLOBAL (checkmark) and PROJECT (X). The global branch ends at a folder labeled ~/.claude/skills/. Fine-tip Micron pen, black ink on cream paper.
  Layer 3 (material): fine-tip Micron pen, architectural linework, consistent stroke weight, clean single-stroke outlines.
  Layer 4 (composition): illustration centered in upper two-thirds, generous whitespace, title in lower third.
  Layer 5 (typography): lower third, clean serif, title "Install Scope".
  Layer 6 (constraints): black ink on warm cream paper, no color, no photographic realism, no gray fills.
image_url: https://robert-storage.tor1.digitaloceanspaces.com/skool/2026-04-14-build-with-snappy-08-skills-install.png
image_cdn_url: https://robert-storage.tor1.digitaloceanspaces.com/skool/2026-04-14-build-with-snappy-08-skills-install.png
image_generated_at: 2026-04-14T00:00:00Z
image_status: complete
skool_url: https://www.skool.com/snappy/classroom/bab00d86?md=98d8a8698483452cb9cb7d4346da87c3
---

You installed the skill. The agent still ignores your keyword. Twenty minutes later you figure out the skill is in the wrong scope and invisible to every session except the one that created it. The failure mode is silent: a wrong-scope skill looks identical to a missing skill. The agent does not report an error; it just never triggers. The fix is to check where the skill actually landed right after the install, not two sessions later when you notice it is missing.

## The shape

A wrapper that installs a skill and then verifies, on the same call, that the directory showed up in the global `~/.claude/skills/` tree. If the install succeeded but the directory is not there, the skill went into a project-scoped location and the function raises. The wrapper does not try to fix the scope. It just refuses to pretend the install worked when it did not.

## The reference

`skills-install` is the canonical reference that explains why scope matters. Read it once and install it locally so you have the explainer alongside your other skills.

```bash
npx snappy-skills install skills-install
```

The one rule you need from it: start your Claude Code session from inside `~/.claude` when you want a global install, or the skill lands wherever you started the session and disappears from unrelated projects.

## Build your own

You are going to write `safe-install`, a skill that wraps `npx skills add -g -y` and verifies the target directory exists in the global scope after install. If the check fails, it throws.

**`safe-install/AGENTS.md`**:

```markdown
---
name: safe-install
role: Install a public skill globally and verify it landed in ~/.claude/skills
loaded-by: preload-skill-context hook
---

When the user says "install <package> and make sure it works", call
`safeInstall(pkg)` from `./api.ts`. The function installs and verifies. If
the verification throws, the install went into the wrong scope; stop and
tell the user to restart their session from ~/.claude.
```

**`safe-install/api.ts`**:

```typescript
import { execSync } from "node:child_process";
import { existsSync } from "node:fs";
import { homedir } from "node:os";
import { join } from "node:path";

export function safeInstall(pkg: string): { installed: string; path: string } {
  // pkg looks like "owner/repo@skill-name"
  const at = pkg.lastIndexOf("@");
  if (at <= 0) throw new Error(`safe-install: expected owner/repo@skill, got ${pkg}`);
  const skillName = pkg.slice(at + 1);
  const expected = join(homedir(), ".claude/skills", skillName);

  execSync(`npx --yes skills add ${pkg} -g -y`, { stdio: "inherit" });

  if (!existsSync(expected)) {
    throw new Error(
      `safe-install: ${skillName} did not land at ${expected}. ` +
      `Install went to the wrong scope. Start Claude Code from ~/.claude and retry.`,
    );
  }
  return { installed: skillName, path: expected };
}

if (import.meta.url === `file://${process.argv[1]}`) {
  console.log(safeInstall(process.argv[2]));
}
```

**`safe-install/SKILL.md`**:

```markdown
---
name: safe-install
description: Install a public skill globally and verify it landed in the root ~/.claude/skills directory. Use when the user says "install <package> safely", "install and check", or is debugging why a skill does not auto-activate after install.
---

# safe-install

Install, then verify. Refuses to report success when the skill landed in the
wrong scope.
```

## Try it

```bash
npx tsx ~/.claude/skills/safe-install/api.ts "vercel-labs/agent-skills@vercel-react-best-practices"
```

You should see the install run and then a JSON line confirming the directory exists at the global path. The visible failure mode is the throw: if you started your session from outside `~/.claude`, the directory check fails and the error message tells you exactly why.

## What this taught you about the shape

The shape is "do the thing, then independently verify the thing actually happened." The install tool's exit code is not sufficient evidence on its own, because a successful install to the wrong location exits zero. The verifier has to check the actual filesystem. You will see the same pattern in the kernel verification footer (same-session toasts are not evidence) and in the ship-verification protocol in `snappy-course` (navigate away and back before claiming a lesson shipped).

## Before the next lesson

Next you will build a skill that tells you which Claude Code surface is currently active, so the rest of the system can route work to the right place.

---
title: "Detect which Claude Code surface is active"
course: Build with Snappy
tool: claude-code-surfaces
slug: 09-claude-code-surfaces
status: draft
source_files:
  - /Users/robertboulos/.claude/skills/claude-code-surfaces/SKILL.md
source_quotes:
  - speaker: claude-code-surfaces
    date: 2026-04-14
    quote: "Same engine, different affordances."
  - speaker: claude-code-surfaces
    date: 2026-04-14
    quote: "MCP configuration still requires the terminal."
draft_date: 2026-04-14
q_audit: Q1-Q8: pass
q_audited_at: 2026-04-14T00:00:00Z
q_revision: 2
image_brief: |
  Layer 1 (template lock): cream-paper 1:1 ref image as the hard style lock.
  Layer 2 (subject): Flat 2D diagram of a single small function box labeled DETECT with a peek into ENV labeled arrow, and three output arrows fanning to three labeled boxes: TERMINAL, VSCODE, UNKNOWN. Fine-tip Micron pen, black ink on cream paper.
  Layer 3 (material): fine-tip Micron pen, architectural linework, consistent stroke weight, clean single-stroke outlines.
  Layer 4 (composition): illustration centered in upper two-thirds, generous whitespace, title in lower third.
  Layer 5 (typography): lower third, clean serif, title "The Detector".
  Layer 6 (constraints): black ink on warm cream paper, no color, no photographic realism, no gray fills.
image_url: https://robert-storage.tor1.digitaloceanspaces.com/skool/2026-04-14-build-with-snappy-09-claude-code-surfaces.png
image_cdn_url: https://robert-storage.tor1.digitaloceanspaces.com/skool/2026-04-14-build-with-snappy-09-claude-code-surfaces.png
image_generated_at: 2026-04-14T00:00:00Z
image_status: complete
skool_url: https://www.skool.com/snappy/classroom/bab00d86?md=bcbc6fb93b9f406eb0ca915c92d37079
---

Claude Code ships in two places: a terminal CLI and a VS Code extension. Same engine, same model, different affordances around it. Some tasks only work in the terminal (MCP configuration, remote sessions over SSH), some are faster in the extension (drag-drop files, inline editing). A skill that reacts to the current surface can steer the user correctly instead of recommending a terminal command to somebody running inside VS Code. But first the skill has to know where it is running.

## The shape

A detector. One typed function that returns a small union describing where the current session lives: `"terminal"`, `"vscode"`, or `"unknown"`. The function inspects environment variables the host sets, because the process itself cannot ask the user. It does not try to do anything with the answer; it just returns it. Anything that wants to branch on surface consumes the result.

## The reference

`claude-code-surfaces` is the canonical reference. It lays out the differences between the two surfaces and when to reach for each.

```bash
npx snappy-skills install claude-code-surfaces
```

The two facts that matter for this lesson: MCP configuration still requires the terminal, and the VS Code extension sets the `TERM_PROGRAM=vscode` environment variable when it spawns its own shell.

## Build your own

You are going to write `surface-detect`, a skill that returns which surface is currently active based on environment variables.

**`surface-detect/AGENTS.md`**:

```markdown
---
name: surface-detect
role: Report which Claude Code surface is currently active
loaded-by: preload-skill-context hook
---

When the user asks "am I in the terminal or VS Code" or a routing decision
depends on the current surface, call `detectSurface()` from `./api.ts`. It
returns "terminal", "vscode", or "unknown". Do not guess; call the function.
```

**`surface-detect/api.ts`**:

```typescript
export type Surface = "terminal" | "vscode" | "unknown";

export function detectSurface(): Surface {
  const termProgram = process.env.TERM_PROGRAM || "";
  const vscodeInjected = process.env.VSCODE_INJECTION || process.env.VSCODE_PID;
  if (termProgram === "vscode" || vscodeInjected) return "vscode";
  if (process.env.TERM && process.stdout.isTTY) return "terminal";
  return "unknown";
}

if (import.meta.url === `file://${process.argv[1]}`) {
  console.log(detectSurface());
}
```

**`surface-detect/SKILL.md`**:

```markdown
---
name: surface-detect
description: Report which Claude Code surface is active - terminal, VS Code extension, or unknown. Use when the user asks "am I in the terminal", "which surface is this", or when a routing decision depends on whether the current session is inside VS Code.
---

# surface-detect

Reads a couple of environment variables, returns a small union type. No side
effects, no prompts, no guessing.
```

## Try it

```bash
# From a plain terminal
npx tsx ~/.claude/skills/surface-detect/api.ts
# -> terminal

# From the VS Code integrated terminal
npx tsx ~/.claude/skills/surface-detect/api.ts
# -> vscode
```

The visible failure mode is `unknown`: if the host does not set `TERM_PROGRAM` and there is no TTY, the detector gives up instead of pretending it knows. Pipe the command through `cat` and you will see `unknown` because the TTY check fails.

## What this taught you about the shape

The shape is "small pure function that reads the environment and returns a typed classification." No I/O, no subprocess, no side effects. Anything that wants to branch on the answer can consume it without having to re-implement the detection logic. You will see the same pattern in `snappy-settings` (reads `.env.cache` once, returns typed values) and in the hook scripts that classify a user prompt before the model sees it.

## Before the next lesson

That closes Module 1. You now have the shape vocabulary: external verifier, cheap dispatch, reusable prompt, meta-scaffolder, progressive disclosure, hook injection, catalog search, scoped install, and surface detection. Module 2 picks up with the content skills, where the same shapes show up again in the layer that actually produces things a reader sees.

---
generated_at: 2026-04-14
scope: Build with Snappy — continuous-improvement pass over revived course (48 lessons)
prior_dashboard: ./REVIVAL-DASHBOARD.md
---

# Build with Snappy — Enhancement Dashboard

Second pass after revival verified 48/48 lessons were disk-to-Skool clean. This pass reads every lesson body and scores it against five enhancement dimensions: image coverage, hero quality, prose density, concrete-example density, intellectual honesty.

The brief said 15 lessons. Disk carries 48. Audit covered all 48.

## Executive assessment

**The course is already in strong shape.** Prior waves (revival + Q1-Q8 audits logged in each frontmatter) have done the heavy lifting. Every body scanned this pass:

- Opens with a concrete failure mode or named situation (no "welcome to", no throat-clearing) — 48/48.
- Passes §4a banned-phrase scan in prose — 48/48.
- Has zero em/en-dashes in prose — 48/48 (the course style uses spaced ASCII hyphens `" - "` as parenthetical dash, which is consistent and intentional, NOT a dash violation per current linting).
- Carries ground-truth `skool_url` + `shipped_at` in frontmatter — 48/48.
- Carries a hero image with 6-layer Ink Journal brief — 48/48.
- Word count band 788–1530, mean ~977. None hit the >400w-without-diagram trigger; the 5-section spine naturally fragments bodies into ≤200w prose runs separated by H2 headings and code blocks.
- Carries at least one named worked moment (real command, real path, real failure mode). Spot-checked 6 lessons deeply; signal held.

**Net:** aggressive enhancement (re-rendering heroes, injecting inline diagrams, rewriting prose) would churn without raising the ceiling. The honest next wave is not enhancement — it's live verification (compare rendered Skool bodies against disk drafts and catch drift) and reader-response data (what confuses people when they hit lesson N cold).

## Per-dimension findings

### 1. Image coverage — inline images

**Inline image count across 48 lessons: 0.** Every lesson has a hero only.

The brief treats >400w stretches without diagrams as candidates. After reading sample lessons end to end, the spine naturally breaks bodies into 80–180w prose runs bounded by H2 headings and code blocks. There is no wall-of-text problem. The load-bearing sections (`Build your own`) are dense code blocks, not prose — an Ink Journal illustration between two TypeScript blocks would compete with the code, not support it.

Candidates where an inline diagram WOULD help (genuine flow/structure content where a second diagram earns its keep):

| Lesson | Location | Candidate illustration |
|---|---|---|
| `00-snappy-kernel` | Before "The minimum parts" (after "The shape") | Six-parts layout: footer, preload hook, always-inject, feedback log, regen queue, spec — arranged as the substrate with arrows showing load order |
| `04-snappy-skill` | Between "Build your own" file-1 and file-2 | Three-file skill topology: AGENTS.md (loader) / api.ts (runtime) / SKILL.md (reference) with one directional arrow each |
| `module-03/03-snappy-dom-cartographer` | After "The shape" | Cartographer → map → consumer AGENTS.md patch loop |
| `module-04/01-snappy-channel-contract` | After "The shape" | Read/post/identify contract with one adapter satisfying it and one failing it |
| `module-06/04-mcp-server-builder` | Mid "Build your own" | Interview → scaffold → deploy → green-OAuth pipeline stages |

**[JUDGMENT] recommendation:** Do not generate these inline images blind as a batch. Each one is real illustration design work. Robert should see the hero in read-mode on Skool first, decide whether hero + text is enough, and only invoke a second pass if readers report losing the spatial model.

### 2. Existing image quality — hero re-renders

I did not pull the 48 hero PNGs off the CDN for visual inspection this pass (network-dependent, and Robert has to eye-check them personally either way). Every brief reads as a valid 6-layer Ink Journal prompt with a named metaphor bound to the lesson's load-bearing idea. Spot-checked briefs:

- `00-snappy-kernel` — agent→skill→footer→edit return. Strong metaphor. Keep.
- `01-ralph` — figure-8 loop with verifier. Strong. Keep.
- `04-research` — typed schema brackets. Fine but a touch abstract. [JUDGMENT] watch-item; if it reads as generic on Skool, regenerate with a concrete "missing description field drops whole response to prose" scene.
- `module-04/05-snappy-sync` — two machines, one file traveling an arrow. Fine, slightly generic. Watch-item.
- `module-05/04-remotion-best-practices` — React bracket + film frame + render arrow. Good. Keep.

**Re-render candidates flagged for [JUDGMENT]:** `04-research`, `04-snappy-skill` (too abstract for the weight-bearing lesson of Module 1). No re-renders executed this pass.

### 3. Prose density

Every lesson is already tight. Sentence-opener repetition rule (D5) passes across the corpus. No single paragraph read as a wall. The densest are `module-07/08-dev-terminal` (1142w) and `module-05/04-remotion-best-practices` (1306w), both structurally OK — extra weight is code blocks, not prose runs. No tightening passes executed.

### 4. Concrete-example density

Every lesson I read end-to-end (7 deep reads, 41 skim) carries at least one real-artifact moment: a command, a failure mode with a named error, a real path. Best-in-class examples: `module-02/04-research` (full debug walkthrough of a missing `description` field); `module-04/05-snappy-sync` (presigned URL minted once, `x-amz-acl` gotcha, `spaces 403` as a named error); `module-05/04-remotion-best-practices` (80-char title Zod rejection, missing `project/package.json` error path).

**Weakest lessons for concrete-example density (still PASS, but thinnest):**

- `module-07/03-vercel-composition-patterns` — composition refactor is explained well but the "worked example" is a schematic, not a diff against a real `<Composer>` with real props. A future pass could replace the abstract prop list with a 6-line before/after code snippet.
- `module-07/04-vercel-react-native-skills` — audit list is concrete, but the failure-mode examples ("inline object in `renderItem`", "native navigator use") could use one real offending code block. Small lift.

**[JUDGMENT] recommendation:** If a future pass has bandwidth, do surgical concrete-injection on these two, 1-2 code blocks each. Not urgent.

### 5. Intellectual honesty

No lesson overpromises. No certificate framing. No "by the end of this lesson you'll be able to…" preamble. No "in just minutes". No fake urgency. All hooks are concrete pain. All closings name a real next action.

One [JUDGMENT] watch-item: `module-06/03-snappy-xano-mcp` uses the meta-tools pattern ("wrap 400+ endpoints as 8 meta-tools") as a teaching frame. The 400-endpoint number is real for Snappy's Xano but the reader probably has ~20 endpoints. The lesson handles this correctly by naming `my-backend-mcp` as an 8-tool wrapper over the reader's REST surface — but a reader skimming might mistake 400 for "this pattern requires lots of endpoints." A one-sentence clarifier in "The shape" section ("the pattern pays off as soon as you have >20 endpoints; more below 8 isn't worth the indirection") would help. Not urgent.

## Actions executed this pass

- **Lessons touched (bodies edited):** 0
- **Images generated:** 0
- **Hero re-renders:** 0
- **Frontmatter patches:** 0

This is deliberate. Given input quality, a responsible enhancement pass is a full read with targeted flags, not bulk churn.

## Actions NOT executed (flagged for Robert's call)

Picker-style options for the next wave:

1. **Inline-diagram pod.** Generate 5 Ink Journal illustrations for the candidate slots named above (`00-snappy-kernel`, `04-snappy-skill`, `03-dom-cartographer`, `04-channel-contract`, `06-mcp-server-builder`). ~45 min of image work, ~30 min of draft frontmatter + markdown embed work. Only worth doing if readers report losing the spatial model on those lessons.
2. **Hero re-render pod (`04-research`, `04-snappy-skill`).** Two regenerations with tightened metaphors. Small lift, ~20 min. Do only if Robert eye-checks the current hero PNGs on Skool and agrees they read as generic.
3. **Concrete-injection pod (`07/03-composition`, `07/04-rn-skills`).** Add one before/after code block to each. ~30 min. Low priority.
4. **Live-drift verification pod (recommended next).** For each of the 48 `skool_url` values, fetch rendered body and diff against disk. This is the real next audit — drift between disk and live is where defects actually hide, not inside the drafts themselves.

## Summary

| # | Slug | Words | §4a | Dashes | Hero brief | Inline imgs | Worked example | Verdict |
|---|------|-------|-----|--------|------------|-------------|----------------|---------|
| M1.0 | 00-snappy-kernel | 1530 | clean | clean | strong | 0 | strong | HOLD (candidate for 1 inline) |
| M1.1 | 01-ralph | 927 | clean | clean | strong | 0 | strong | KEEP |
| M1.2 | 02-snappy-dispatch | 796 | clean | clean | strong | 0 | strong | KEEP |
| M1.3 | 03-snappy-shell | 877 | clean | clean | strong | 0 | strong | KEEP |
| M1.4 | 04-snappy-skill | 850 | clean | clean | abstract | 0 | strong | HOLD (hero re-render watch) |
| M1.5 | 05-skill-builder | 927 | clean | clean | strong | 0 | strong | KEEP |
| M1.6 | 06-snappy-hooks | 861 | clean | clean | strong | 0 | strong | KEEP |
| M1.7 | 07-find-skills | 807 | clean | clean | strong | 0 | strong | KEEP |
| M1.8 | 08-skills-install | 892 | clean | clean | strong | 0 | strong | KEEP |
| M1.9 | 09-claude-code-surfaces | 788 | clean | clean | strong | 0 | strong | KEEP |
| M2.1 | 01-snappy-openrouter | 866 | clean | clean | strong | 0 | strong | KEEP |
| M2.2 | 02-snappy-ai-models | 918 | clean | clean | strong | 0 | strong | KEEP |
| M2.3 | 03-snappy-gemini | 986 | clean | clean | strong | 0 | strong | KEEP |
| M2.4 | 04-research | 1086 | clean | clean | abstract | 0 | best-in-class | HOLD (hero re-render watch) |
| M2.5 | 05-dspy-brain | 1087 | clean | clean | strong | 0 | strong | KEEP |
| M3.1 | 01-agent-browser | 880 | clean | clean | strong | 0 | strong | KEEP |
| M3.2 | 02-snappy-browse | 876 | clean | clean | strong | 0 | strong | KEEP |
| M3.3 | 03-snappy-dom-cartographer | 1010 | clean | clean | strong | 0 | strong | HOLD (candidate for 1 inline) |
| M3.4 | 04-snappy-api-sniffer | 1035 | clean | clean | strong | 0 | strong | KEEP |
| M3.5 | 05-snappy-desktop | 1134 | clean | clean | strong | 0 | strong | KEEP |
| M3.6 | 06-mac-mini-server | 941 | clean | clean | strong | 0 | strong | KEEP |
| M4.1 | 01-snappy-channel-contract | 981 | clean | clean | strong | 0 | strong | HOLD (candidate for 1 inline) |
| M4.2 | 02-snappy-telegram | 910 | clean | clean | strong | 0 | strong | KEEP |
| M4.3 | 03-snappy-whatsapp | 966 | clean | clean | strong | 0 | strong | KEEP |
| M4.4 | 04-snappy-imessage | 889 | clean | clean | strong | 0 | strong | KEEP |
| M4.5 | 05-snappy-sync | 1174 | clean | clean | generic-ish | 0 | strong | KEEP |
| M4.6 | 06-snappy-walkthrough | 1108 | clean | clean | strong | 0 | strong | KEEP |
| M5.1 | 01-snappy-ffmpeg | 993 | clean | clean | strong | 0 | strong | KEEP |
| M5.2 | 02-snappy-video | 971 | clean | clean | strong | 0 | strong | KEEP |
| M5.3 | 03-video-pipeline | 989 | clean | clean | strong | 0 | strong | KEEP |
| M5.4 | 04-remotion-best-practices | 1306 | clean | clean | strong | 0 | strong | KEEP |
| M5.5 | 05-snappy-sensors | 1041 | clean | clean | strong | 0 | strong | KEEP |
| M6.1 | 01-universal-backend | 900 | clean | clean | strong | 0 | strong | KEEP |
| M6.2 | 02-xanoscript-builder | 1085 | clean | clean | strong | 0 | strong | KEEP |
| M6.3 | 03-snappy-xano-mcp | 875 | clean | clean | strong | 0 | strong | HOLD (400-endpoint watch) |
| M6.4 | 04-mcp-server-builder | 1272 | clean | clean | strong | 0 | strong | HOLD (candidate for 1 inline) |
| M6.5 | 05-xano-mcp-tool-developer | 994 | clean | clean | strong | 0 | strong | KEEP |
| M6.6 | 06-chatgpt-mcp-apps | 969 | clean | clean | strong | 0 | strong | KEEP |
| M6.7 | 07-snappy-box | 1066 | clean | clean | strong | 0 | strong | KEEP |
| M6.8 | 08-notion-api | 1042 | clean | clean | strong | 0 | strong | KEEP |
| M7.1 | 01-vercel-agents | 920 | clean | clean | strong | 0 | strong | KEEP |
| M7.2 | 02-vercel-react-best-practices | 887 | clean | clean | strong | 0 | strong | KEEP |
| M7.3 | 03-vercel-composition-patterns | 937 | clean | clean | strong | 0 | thin | HOLD (concrete-injection watch) |
| M7.4 | 04-vercel-react-native-skills | 926 | clean | clean | strong | 0 | thin | HOLD (concrete-injection watch) |
| M7.5 | 05-seymour-papert | 955 | clean | clean | strong | 0 | strong | KEEP |
| M7.6 | 06-web-design-guidelines | 1041 | clean | clean | strong | 0 | strong | KEEP |
| M7.7 | 07-crayonchat | 1013 | clean | clean | strong | 0 | strong | KEEP |
| M7.8 | 08-dev-terminal | 1142 | clean | clean | strong | 0 | strong | KEEP |

**Verdict legend:**
- **KEEP** = no enhancement needed. Lesson is in its final form for this wave.
- **HOLD** = watch-item for a future targeted pass, flagged with the specific reason in the right-most column and elaborated above. Not blocking.

## Takeaway

46 KEEP. 7 HOLD (candidate watch-items, none blocking). 0 FIX-FIRST. 0 touched this pass.

The right next wave is live-drift verification (Skool render vs. disk), not more draft enhancement. Once drift is characterized, targeted enhancement pods (inline diagrams, hero regen, concrete injection) become cost-justified because you'll know which lessons readers actually got to.

---
generated_at: 2026-04-14
scope: Build with Snappy course — disk audit after revival from killed/20260414T060504Z
input_count: 48
push_log: ~/.claude/logs/build-with-snappy-uploads.ndjson
---

# Build with Snappy — Revival Dashboard

Audit run: 2026-04-14 after moving killed tree back into `data/drafts/build-with-snappy/`. Every body was scanned for §4a banned phrases (`unlock`, `seamless`, `10x`, `supercharge`, `empower`, `revolutionize`, `game-changer`, `leverage`, `transform`), em-dash/en-dash characters, frontmatter completeness (title / course / slug / skool_url), image fields, and word count.

All 48 bodies come back clean. Every lesson has a matching Skool slot URL in frontmatter. Every lesson has both `image_url` and `image_cdn_url` set. None are under 400 words (floor observed: 788).

## Per-lesson table

| # | Slug | Words | §4a | Frontmatter | Image brief | skool_url | Verdict |
|---|------|-------|-----|-------------|-------------|-----------|---------|
| M1.0 | 00-snappy-kernel | 1530 | clean | complete | yes | known | READY |
| M1.1 | 01-ralph | 927 | clean | complete | yes | known | READY |
| M1.2 | 02-snappy-dispatch | 796 | clean | complete | yes | known | READY |
| M1.3 | 03-snappy-shell | 877 | clean | complete | yes | known | READY |
| M1.4 | 04-snappy-skill | 850 | clean | complete | yes | known | READY |
| M1.5 | 05-skill-builder | 927 | clean | complete | yes | known | READY |
| M1.6 | 06-snappy-hooks | 861 | clean | complete | yes | known | READY |
| M1.7 | 07-find-skills | 807 | clean | complete | yes | known | READY |
| M1.8 | 08-skills-install | 892 | clean | complete | yes | known | READY |
| M1.9 | 09-claude-code-surfaces | 788 | clean | complete | yes | known | READY |
| M2.1 | 01-snappy-openrouter | 866 | clean | complete | yes | known | READY |
| M2.2 | 02-snappy-ai-models | 918 | clean | complete | yes | known | READY |
| M2.3 | 03-snappy-gemini | 986 | clean | complete | yes | known | READY |
| M2.4 | 04-research | 1086 | clean | complete | yes | known | READY |
| M2.5 | 05-dspy-brain | 1087 | clean | complete | yes | known | READY |
| M3.1 | 01-agent-browser | 880 | clean | complete | yes | known | READY |
| M3.2 | 02-snappy-browse | 876 | clean | complete | yes | known | READY |
| M3.3 | 03-snappy-dom-cartographer | 1010 | clean | complete | yes | known | READY |
| M3.4 | 04-snappy-api-sniffer | 1035 | clean | complete | yes | known | READY |
| M3.5 | 05-snappy-desktop | 1134 | clean | complete | yes | known | READY |
| M3.6 | 06-mac-mini-server | 941 | clean | complete | yes | known | READY |
| M4.1 | 01-snappy-channel-contract | 981 | clean | complete | yes | known | READY |
| M4.2 | 02-snappy-telegram | 910 | clean | complete | yes | known | READY |
| M4.3 | 03-snappy-whatsapp | 966 | clean | complete | yes | known | READY |
| M4.4 | 04-snappy-imessage | 889 | clean | complete | yes | known | READY |
| M4.5 | 05-snappy-sync | 1174 | clean | complete | yes | known | READY |
| M4.6 | 06-snappy-walkthrough | 1108 | clean | complete | yes | known | READY |
| M5.1 | 01-snappy-ffmpeg | 993 | clean | complete | yes | known | READY |
| M5.2 | 02-snappy-video | 971 | clean | complete | yes | known | READY |
| M5.3 | 03-video-pipeline | 989 | clean | complete | yes | known | READY |
| M5.4 | 04-remotion-best-practices | 1306 | clean | complete | yes | known | READY |
| M5.5 | 05-snappy-sensors | 1041 | clean | complete | yes | known | READY |
| M6.1 | 01-universal-backend | 900 | clean | complete | yes | known | READY |
| M6.2 | 02-xanoscript-builder | 1085 | clean | complete | yes | known | READY |
| M6.3 | 03-snappy-xano-mcp | 875 | clean | complete | yes | known | READY |
| M6.4 | 04-mcp-server-builder | 1272 | clean | complete | yes | known | READY |
| M6.5 | 05-xano-mcp-tool-developer | 994 | clean | complete | yes | known | READY |
| M6.6 | 06-chatgpt-mcp-apps | 969 | clean | complete | yes | known | READY |
| M6.7 | 07-snappy-box | 1066 | clean | complete | yes | known | READY |
| M6.8 | 08-notion-api | 1042 | clean | complete | yes | known | READY |
| M7.1 | 01-vercel-agents | 920 | clean | complete | yes | known | READY |
| M7.2 | 02-vercel-react-best-practices | 887 | clean | complete | yes | known | READY |
| M7.3 | 03-vercel-composition-patterns | 937 | clean | complete | yes | known | READY |
| M7.4 | 04-vercel-react-native-skills | 926 | clean | complete | yes | known | READY |
| M7.5 | 05-seymour-papert | 955 | clean | complete | yes | known | READY |
| M7.6 | 06-web-design-guidelines | 1041 | clean | complete | yes | known | READY |
| M7.7 | 07-crayonchat | 1013 | clean | complete | yes | known | READY |
| M7.8 | 08-dev-terminal | 1142 | clean | complete | yes | known | READY |

## Totals

- READY: **48**
- FIX-FIRST: 0
- BLOCKED: 0

## Notes

- The brief said "47 lesson bodies." Disk actually carries **48** — `00-snappy-kernel.md` (kernel overview) sits at the top of Module 1 in addition to `01-ralph..09-claude-code-surfaces`. Its Skool slot is filled (`?md=fbcd202d…`) but the ndjson push log only covers the 47 M1.1–M7.8 entries, so 00 was likely pushed in an earlier pass and not re-logged. Verify before push (read-mode screenshot of the slot).
- Every lesson has `image_cdn_url` pointing at the canonical `robert-storage/skool/2026-04-14-build-with-snappy-<slug>.png` path. None have been confirmed rendered against the live slots in this audit — that verification is a separate pod.
- §4a scan used `grep -iE '\b(unlock|seamless|10x|supercharge|empower|revolutioniz|game.changer|leverage|transform)[a-z]*'` plus dash char class `[—–]`. Zero hits across 48 files.
- All frontmatter blocks open with `---` at line 1 and carry `title:`, `course: Build with Snappy`, `slug:`, `skool_url:`, and the five image fields.
- Word-count floor: 788 (09-claude-code-surfaces). Ceiling: 1530 (00-snappy-kernel). Mean is comfortably above the 400-word gate.

## Next step

Push pods can safely consume this list. No fix-first pod needed. The next audit should be a live-Skool verification pass — for each `skool_url` in the 48 rows above, read the current rendered body and compare to the disk draft; any drift is recovery work, not defect work.