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cockpit-audit

Checks each screen of your dashboard against a quality checklist before it ships.
personal 2 files 7 recent evals

What it does for you

Checks each screen of your dashboard against a quality checklist before it ships.

What it produces

A recent result, so you can see the kind of work it returns.

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How to get it

These run inside the Snappy workspace. Want this working in your business? I set skills like this up with you, in one focused week.

Work with me
For developers how this skill is built, graded, and how it runs

at a glance- the short version

eval modeshape
categoryQuality

what's inside - the parts that make up a skill 2/4 present

A skill is just a few plain-text files. Only the main one is required. The rest are optional, added as the work needs them. This is what the skill is made of; how it runs is just below.

The skill
state/skills/cockpit-audit/SKILL.md present
the skill itself, in plain text
The main file. It says what the skill is and lays out the steps in plain English.
Code
state/lib/cockpit-audit.ts not present
code the skill can run
Optional. Many skills are just words and need no code at all.
Scripts
state/bin/cockpit-audit/ not present
helper scripts
Optional. Added when a skill has a few commands to run.
Loader
state/skills/cockpit-audit/AGENTS.md present
what the AI loads on the fly
Loaded automatically the moment this skill is needed. Kept short on purpose.

how it runs - the shared frame every skill uses 3/5 present

Every skill runs the same way. One part does the work, a separate part checks it, and a short loader hands the AI exactly what it needs for the job. Anything this skill doesn't use shows a one-line note saying why, on purpose, not by accident.

makes the work The worker
not present

No work step here. This is probably a skill that reads or coordinates, not one that produces something.

checks the work The reviewer
inferred
shape gate an automatic check
The check is an automatic pass or fail on the shape of the result, run separately from the work itself.
frame
learns Self-correction
present
fixes itself learns from gaps
When a run hits a gap, the skill gets edited on the spot [FIXED] or queued for a bigger rewrite [LOGGED], so it keeps getting better.
tidies up Background fixes
present
queued for rewrite runs in the background
Bigger fixes that can't be made on the spot get queued and rewritten in the background later.
remembers Run history
present
state/log/evals.ndjson shape runs
Every run is written down here, so the next time this skill is used it already knows how the last runs went.
Critical rules the things this skill must not get wrong
  1. 4a (CRITICAL — Krisp pattern 2026-04-26): Curl response keys MUST match Swift Codable model CodingKeys exactly. Server returns meeting_id but model expects id → silent decode failure → blank UI. Required: list curl keys, list CodingKeys, verify 1:1 map.

what it has learned - fixes written back in over time sample

When a run hits something this skill didn't handle, the fix gets written back into the skill so it doesn't happen again. FIXED means it was corrected on the spot. LOGGED means it's queued for a bigger rewrite. Either way, the skill gets a little better and never makes the same mistake twice.

  1. Loading feedback rows…

SKILL.md- the skill, written out in plain English

cockpit-audit

Per-page audit checklist for snappy-shell pages. The pattern emerged from repeated misses in one session: refresh buttons that didn't refresh, "drain queue" buttons that didn't drain, "click to copy" docs without click handlers, page subtitles claiming 0 pending while a stale cache hid 11 items behind it.

Robert (2026-04-26): "why do i have to explain the obvious for everything it is like you need a checklist for every single page some kind of checklist that enforces everything."

The full checklist + dogfood-verify protocol lives in this skill's AGENTS.md loader. Trigger keywords: cockpit, audit, snappy-shell.

When to run

  • Before writing any git commit message that says "shipped" / "works" / "fixed" against a snappy-shell page
  • After adding a new button, endpoint, or data surface
  • When auditing an existing page for missed sophistication (the "hidden layer" pattern)

Steps

  1. Open the page in the running app OR read the screen file end-to-end
  2. Walk the 10 points in AGENTS.md
  3. For each button: curl the underlying endpoint, observe the side effect, then trigger via UI
  4. For each data surface: check the server endpoint returns more than the page consumes (richer-endpoint trap)
  5. If any point fails, fix it before commit. If a point can't be verified from this shell (SSH/no Screen Recording), say so in the commit message
  6. Log the audit in state/log/agents-md-feedback.log

Eval

Shape. This skill is prose-only; the eval is whether running it actually catches the kind of issues it was created to catch. Manual audit log lives in feedback log.

Failure modes that motivate this skill

  • Krisp refresh button fired krisp-inbox skill, which only READS the cache, doesn't refresh it. Cache stayed 6 days stale.
  • Regen drain button fired pid-regen skill, which scans evals.ndjson, NOT regen-pending.txt. Queue stayed at 11 entries.
  • Memory page rendered raw journal only, hid the distilled observations layer that /memory/state already exposed.

All three would have been caught by point #4 (verified buttons) or #2 (layered visibility).

AGENTS.md- what the AI loads when this skill comes up

cockpit-audit - loader

Per-turn rules for auditing a snappy-shell page before declaring it "shipped." Robert (2026-04-26): "you need a checklist for every single page some kind of checklist that enforces everything."

Critical Rules

  1. Data freshness - Cached or live? If cached, subtitle shows age ("synced 6h ago"). Refresh button hits source-of-truth (not a downstream skill re-reading cache). Verify: curl endpoint pre-click, check cache file mtime post-click.
  2. Layered visibility - Pages surface ALL data stages (raw→derived→archive), not just the one the endpoint returned. Server may expose richer endpoints than the page currently reads.
  3. Button coverage - Every documented action is a button on the page. No "run this bash command" workarounds.
  4. Verified buttons (CRITICAL) - E2E test before commit: curl endpoint → observe state change → click button → confirm same change lands. Never ship an untested button.
  • 4a (CRITICAL - Krisp pattern 2026-04-26): Curl response keys MUST match Swift Codable model CodingKeys exactly. Server returns meeting_id but model expects id → silent decode failure → blank UI. Required: list curl keys, list CodingKeys, verify 1:1 map.
  1. Honest staleness - Subtitle never lies. "12 items · synced 6 days ago" (not "12 items").
  2. Actionable empty states - Button, not CLI. "no X yet - [seed]" button (not "run npx tsx ...").
  3. Errors + retry - Every ErrorPlaceholder has a retry callback.
  4. Click intent matching - If doc says "click row to copy," clicking does copy.
  5. Live-listen - If SSE data event exists for page, observe AppState.liveEvents and refetch on change.
  6. No cockpit exits - Every documented operation has an in-app button.

Commands

| ui dashboard | state/skills/cockpit-audit/resources/ui.openui |

ActionCommand
Audit a pageOpen snappy-shell page in /Applications/SnappyShell.app; walk points 1-10 manually; curl http://localhost:3147/<endpoint> for each button
Verify curl response`curl -s http://localhost:3147/<endpoint> \jq 'keys'; cross-check against Sources/SnappyCore/Models/X.swift CodingKeys` block
Test server endpointcurl -s -o /dev/null -w "%{http_code}\n" http://localhost:3147/<endpoint> (expect 200)
Write eval rowscore("cockpit-audit", run_id, { score: 0.5 or 1.0, notes: "points 1,3,4,7 passed; 2 missing" })
Check past auditsgrep cockpit-audit state/log/evals.ndjson

Self-Test

  1. [ ] Run curl on every button's endpoint before trusting it works
  2. [ ] Verify curl JSON keys match Swift CodingKeys (not just HTTP 200)
  3. [ ] Check all data-backing stages are visible, not just one
  4. [ ] Refuse to commit without ticking points 1,3,4,6,7,10

<!-- kernel-ok: historical attribution for the PID-loader pattern. -->

Self-correcting loader (PID feedback)

This is the rule that makes loaders learn. Ported from snappy-kernel, where it demonstrably worked: the snappy-dom-cartographer skill mapped Skool admin UI once, every later ship-agent inherited the full map for free.

Before you finish, do two things:

1. Fix gaps (P - proportional). MANDATORY. If this loader didn't cover your case - if you had to read another file, run an undocumented command, work around a wrong selector, or discover a quirk - you MUST attempt an Edit to this AGENTS.md before you log.

  • Default action: edit this file. Surgical. Add the missing line, table

row, or code example where it belongs. No restructuring.

  • Only LOGGED is allowed when: the fix needs >10 lines, spans multiple

files, or requires a structural rewrite. In that case the state/regen/drain.sh queue picks it up asynchronously.

  • "I didn't have time" / "it's minor" / "the next agent will figure it out"

are NOT valid reasons. Minor gaps compound - that's the whole point of this loop.

  • Concrete example of a valid P-fix: discover agent-browser screenshot

takes path positionally not as --output <path>. One line in a table. Edit it in. Do not just log it.

  • The goal: the next agent never has to leave the loader.

2. Log the result.

echo "[$(date -u +%FT%TZ)] cockpit-audit: <what was missing or fixed> [FIXED|LOGGED] action_kind=<kind>" >> state/log/loader-feedback.log
  • FIXED = you patched this loader inline (P-fix).
  • LOGGED = too large for inline; the PostToolUse enqueue + Stop-hook drain

will rewrite the loader from scratch on next session-end.

  • action_kind: shape-ok | skill-ran | loader-rewritten | pattern-elevated

Do not skip this. Every agent run must leave the system better than it found it. The loader is the setpoint; you are the sensor; the gap is the error signal; closing the gap is the correction.

OpenUI Resource

  • Skill-owned OpenUI Lang resource: state/skills/cockpit-audit/resources/ui.openui. Read it before rendering or editing this skill's generated component surface.
  • Treat this resource as a first-class artifact of the skill, not a generic chat response. Improve it when the skill's user-facing output needs to become richer.
  • System resources compose OpenUI primitives and inherit SnappyChat tokens. Use ui_contract: branded in SKILL.md only for deliberate platform or client visuals.

api.ts- the code it can call

⚠ no api.ts - this skill has no typed action surface

scripts- helper scripts it can run

prose-only skill - no sidecar under state/bin/ yet. Steps, if any, are described in SKILL.md.

how we check it- the checks, plus the last 7 runs

rubric shape schema-shape check (no inline rubric)
recent mean 0.95 · 7 runs actor/auditor: unverifiable
deps none declared
timestamp verb score primary_issue artifact
2026-04-27 13:11Z - 0.88 - -
2026-04-26 23:44Z - 1.00 - -
2026-04-27 13:11Z - 0.88 - -
2026-04-26 23:44Z - 1.00 - -
2026-04-27 13:11Z - 0.88 - -
2026-04-26 23:44Z - 1.00 - -
2026-04-26 23:44Z - 1.00 - -