drop another .md file to compare - side-by-side diff against inbound

inbound

Spots and sorts incoming messages so the right ones reach you.

description: "Triggers on prompt mention of 'inbound'."

personal 2 files 10 recent evals

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What it does for you

Spots and sorts incoming messages so the right ones reach you.

What it produces

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

loading…

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

actorExported functions in state/lib/inbound.ts.

auditorNone wired yet - eval is manual (Robert review).

eval modeshape

categoryChannels

stages2

dependssweep

what's inside - the parts that make up a skill 3/4 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/inbound/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/inbound.ts present

code the skill can run

Reusable code this skill can call when it needs to.

Scripts

state/bin/inbound/ not present

helper scripts

Optional. Added when a skill has a few commands to run.

Loader

state/skills/inbound/AGENTS.md present

what the AI loads on the fly

Loaded automatically the moment this skill is needed. Kept short on purpose.

how it's graded - what counts as a good run 4 criteria · 3 deterministic · 1 judge

Each row is one thing a good run has to get right. deterministic means a quick check decides, pass or fail. judge means the AI reads the result and rates it. Grading each piece on its own (instead of one overall score) shows exactly where a run fell short, so the fix is obvious.

name

kind

check

calls_all_required_functions

deterministic

The execution log includes calls to checkSlackJoins(), checkEmail(), and classifyInbound().

inputs_are_strings

deterministic

Inputs checkSlackJoins_input, checkEmail_input, and classifyInbound_input are all provided as strings.

creates_log_row

deterministic

A new row is appended to state/log/pending-eval.ndjson after skill execution.

inbound_classification_valid

judge

The output of classifyInbound() aligns with expected inbound classification based on provided inputs.

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

present

Exported functions in state/lib/inbound.ts. the worker

Does the actual work. Whatever it produces is what gets checked next.

checks the work The reviewer

present

None wired yet - eval is manual (Robert review). the checker

A separate checker grades the work, so the part that made it can't approve its own work.

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/pending-eval.ndjson pending runs

Every run is written down here, then reviewed by hand each week.

Critical rules the things this skill must not get wrong

No must-not-break rules called out for this skill. Anything important lives in the writeup below.

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.

Loading feedback rows…

how the work flows- who makes it, who checks it

inputs sweep

actor Exported functions in state/lib/inbound.ts.

1 generator

invoke

actor = Exported functions in state/lib/inbound.ts.

import functions from `state/lib/inbound.ts` (`checkSlackJoins`, `checkEmail`, `classifyInbound`)

+ eval for this step

auditor None wired yet - eval is manual (Robert review).

2 data

eval log

`state/log/pending-eval.ndjson` (manual review until shape gate added)

+ eval for this step

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

inbound

Inbound detection and classification.

Ported from kernel snappy-inbound in Phase 0.5. See state/lib/inbound.ts for the full API surface.

Steps

checkSlackJoins() - see state/lib/inbound.ts
checkEmail() - see state/lib/inbound.ts
classifyInbound() - see state/lib/inbound.ts

Eval

Actor: the exported functions in state/lib/inbound.ts. Auditor: none wired yet - eval is manual (Robert review). File a state/log/pending-eval.ndjson row on each run.

Score convention:

Outcome	Score
Pass on first try	1.0
Failed first, auto-fix applied, re-check passed	0.5
Still failing or unrecoverable	0.0

Gotchas

via the Phase 0.5 driver. Only these rewrites were applied: already in state/lib/)

realpathSync(process.argv[1]) CLI guard wrapped in try/catch

See the kernel SKILL.md for the original long-form guidance if you need it

(read-only reference at the kernel path above).

Graduation

This skill is prose. Graduate by defining a deterministic auditor and flipping eval: auto.

Rubric

criteria:
  - name: calls_all_required_functions
    kind: deterministic
    check: "The execution log includes calls to checkSlackJoins(), checkEmail(), and classifyInbound()."
  - name: inputs_are_strings
    kind: deterministic
    check: "Inputs checkSlackJoins_input, checkEmail_input, and classifyInbound_input are all provided as strings."
  - name: creates_log_row
    kind: deterministic
    check: "A new row is appended to state/log/pending-eval.ndjson after skill execution."
  - name: inbound_classification_valid
    kind: judge
    check: "The output of classifyInbound() aligns with expected inbound classification based on provided inputs."

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

inbound - loader

Per-turn rules for the inbound skill. Full reference: state/skills/inbound/SKILL.md. Do not skip these.

Critical Rules

_(no failures recorded yet - this skill is a Phase 0.5 mechanical port from snappy-inbound with no hard-won rules. Read state/skills/inbound/SKILL.md and state/lib/inbound.ts before invoking. Eval is manual until an auditor is wired.)_

Commands

| ui dashboard | state/skills/inbound/resources/ui.openui | |invoke: import functions from state/lib/inbound.ts (checkSlackJoins, checkEmail, classifyInbound) |eval log: state/log/pending-eval.ndjson (manual review until shape gate added)

OpenUI Resource

Skill-owned OpenUI Lang resource: state/skills/inbound/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.

Known Pitfalls

Skill is eval: shape in frontmatter but auditor is "none wired yet - manual" - every run must eval.pending() row, do not claim auto-pass

Self-Test

An agent reading this should correctly:

[ ] Log every run to state/log/pending-eval.ndjson rather than evals.ndjson?
[ ] Resist drafting a deterministic auditor without first running the function on real data twice?
[ ] Find the API surface in state/lib/inbound.ts?

Self-report

If this loader fell short, append a line:

echo "[$(date -u +%FT%TZ)] inbound: <what was missing>" >> state/log/loader-feedback.log

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)] inbound: <what was missing or fixed> [FIXED|LOGGED]" >> 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.

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.

api.ts- the code it can call

#!/usr/bin/env npx tsx
/**
 * snappy-inbound/api.ts -- Inbound detection and classification.
 *
 * Usage:
 *   npx tsx api.ts slack-joins            # check #all-snappy for join events
 *   npx tsx api.ts classify "I want to learn about AI agents for my business"
 *
 * Or import as module:
 *   import { checkSlackJoins, classifyInbound } from "./inbound.ts";
 */

import { readMessages } from "./slack.ts";
import { env } from "./env.ts";
import { realpathSync } from "fs";

const ALL_SNAPPY_CHANNEL = "C09DD2D0S07";

/** Check #all-snappy for recent join events. Returns messages with join subtype. */
export async function checkSlackJoins(limit = 50) {
  const data = await readMessages(ALL_SNAPPY_CHANNEL, limit);
  if (!data.messages) return [];
  return data.messages.filter(
    (m: any) => m.subtype === "channel_join" || m.subtype === "group_join"
  );
}

/**
 * Check email for new subscribers.
 * Placeholder -- ActiveCampaign is NOT in use. Email goes through Xano/Gmail.
 * When implementing, use Xano contacts API or Loops.so for subscriber list polling.
 */
export async function checkEmail(): Promise<{ status: string; info: string }> {
  return {
    status: "not_implemented",
    info: "Subscriber polling not yet implemented. Use Xano contacts API (GET api:PB9UH7b9/contacts) or Loops.so. ActiveCampaign is NOT in use.",
  };
}

type InboundType = "lead" | "support" | "spam" | "partnership" | "unknown";

/** Classify inbound text into a type. Simple keyword-based -- upgrade to LLM call if needed. */
export function classifyInbound(text: string): { type: InboundType; confidence: number } {
  const lower = text.toLowerCase();

  const spamSignals = ["unsubscribe", "casino", "crypto airdrop", "free money", "click here now"];
  if (spamSignals.some((s) => lower.includes(s))) {
    return { type: "spam", confidence: 0.9 };
  }

  const partnerSignals = ["partnership", "collaborate", "joint venture", "co-market", "affiliate"];
  if (partnerSignals.some((s) => lower.includes(s))) {
    return { type: "partnership", confidence: 0.7 };
  }

  const supportSignals = ["help", "issue", "broken", "not working", "bug", "error", "can't access"];
  if (supportSignals.some((s) => lower.includes(s))) {
    return { type: "support", confidence: 0.7 };
  }

  const leadSignals = [
    "interested", "pricing", "how much", "learn more", "demo",
    "business", "agency", "consulting", "ai agent", "automation",
  ];
  if (leadSignals.some((s) => lower.includes(s))) {
    return { type: "lead", confidence: 0.7 };
  }

  return { type: "unknown", confidence: 0.3 };
}

// --- CLI ---

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

    switch (cmd) {
      case "slack-joins": {
        const joins = await checkSlackJoins();
        if (joins.length === 0) {
          console.log("No recent join events.");
        } else {
          for (const j of joins) {
            const ts = new Date(Number(j.ts) * 1000).toISOString().slice(0, 16);
            console.log(`${ts}\t${j.user}\tjoined`);
          }
        }
        break;
      }
      case "classify": {
        const text = args.join(" ");
        if (!text) { console.error("Usage: api.ts classify <text>"); process.exit(1); }
        const result = classifyInbound(text);
        console.log(JSON.stringify(result, null, 2));
        break;
      }
      default:
        console.log("Usage: npx tsx api.ts [slack-joins|classify] ...");
    }
  })();
}

scripts- helper scripts it can run

prose-only skill - 1 inline code block live in SKILL.md above (no state/bin/ sidecar yet).

how we check it- the checks, plus the last 10 runs

rubric shape schema-shape check (no inline rubric)

recent mean 1.00 · 10 runs actor/auditor: unverifiable

deps sweep

timestamp	verb	score	primary_issue	artifact
2026-04-25 04:11Z	-	1.00	-	-
2026-04-21 15:58Z	-	1.00	-	-
2026-04-21 15:56Z	-	1.00	-	-
2026-04-21 03:53Z	-	1.00	-	-
2026-04-25 04:11Z	-	1.00	-	-
2026-04-21 15:58Z	-	1.00	-	-
2026-04-21 15:56Z	-	1.00	-	-
2026-04-21 03:53Z	-	1.00	-	-
2026-04-25 04:11Z	-	1.00	-	-
2026-04-21 15:58Z	-	1.00	-	-