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repo-heat

Shows which of your skills are fresh and which are going stale.
description: "Triggers on prompt mention of 'repo-heat'."
personal 2 files 10 recent evals

What it does for you

Shows which of your skills are fresh and which are going stale.

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

actorBucket pass.
auditorRe-sum the buckets and confirm `hot + warm + cold === total
eval modeauto
categoryOps
stages2

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/repo-heat.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/repo-heat.ts not present
code the skill can run
Optional. Many skills are just words and need no code at all.
Scripts
state/bin/repo-heat/ not present
helper scripts
Optional. Added when a skill has a few commands to run.
Loader
state/skills/repo-heat.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 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
Bucket pass. the worker
Does the actual work. Whatever it produces is what gets checked next.
checks the work The reviewer
present
Re-sum the buckets and confirm `hot + warm + cold === total 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/evals.ndjson unknown 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. A freshly-generated .agents.md loader will show as "hot" even if the

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…

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

actor Bucket pass.
1 generator
invoke
actor = Bucket pass.
see `state/skills/repo-heat.md` Steps section
auditor Re-sum the buckets and confirm `hot + warm + cold === total
2 data
eval log
`state/log/evals.ndjson` (skill: "repo-heat")

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

Backed by: git log + filesystem mtime. No lib needed.

repo-heat

Reads the last-commit date for each state/skills/*.md file (skipping .agents.md and _template.md). Buckets them:

  • hot - touched in the last 7 days
  • warm - 7-30 days
  • cold - >30 days

Emits a report at state/log/repo-heat/<date>.md with bucket counts and the full cold list. The cold list is the signal: skills that haven't moved in a month are either done, dormant, or forgotten.

Steps

  1. List every state/skills/*.md (skip .agents.md, skip _template.md).
  2. For each, run git log -1 --format=%cI -- <file> to get last-commit ISO

date. If the file is untracked, fall back to filesystem mtime.

  1. Bucket by age: hot <7d, warm 7-30d, cold >30d. Sort the cold list by

oldest first.

  1. Write state/log/repo-heat/<date>.md: header with counts, then the

cold list as a table | slug | last_touch | age_days |.

Eval

Actor: the bucket pass. Auditor: re-sum the buckets and confirm hot + warm + cold === total skills scanned (no row lost).

const total = hot.length + warm.length + cold.length;
const conserved = total === scanned;

score("repo-heat", run_id, {
  score: conserved ? 1.0 : 0.0,
  hot_count: hot.length,
  warm_count: warm.length,
  cold_count: cold.length,
  scanned,
  primary_issue: conserved ? null : "bucket-count-mismatch",
});

Gotchas

  • A freshly-generated .agents.md loader will show as "hot" even if the

underlying skill hasn't moved - filter it out before scanning, or the report inflates hot count with noise.

  • git log on a file that was renamed returns the rename commit, not the

original creation. That's fine for heat purposes - rename is a touch.

  • If state/skills/ has files other than *.md (e.g. .bak), skip

them. Only the .md skill sheets count.

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

repo-heat - loader

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

Critical Rules

  • A freshly-generated .agents.md loader will show as "hot" even if the

Commands

|invoke: see state/skills/repo-heat.md Steps section |eval log: state/log/evals.ndjson (skill: "repo-heat")

Self-Test

An agent reading this should correctly:

  1. [ ] Know which lib/bin artifact backs this skill (or that it is prose-only)
  2. [ ] Know what to write to state/log/evals.ndjson after invoking
  3. [ ] Know the eval mode (auto / shape / manual) from the .md frontmatter

Self-report

If this loader fell short, append a line:

echo "[$(date -u +%FT%TZ)] repo-heat: <what was missing>" >> ~/.claude/logs/snappy-os-loader-feedback.log

<!-- 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)] <skill-name>: <what was missing or fixed> [FIXED|LOGGED]" >> state/log/agents-md-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

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

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 auto no rubric declared
recent mean 1.00 · 10 runs actor/auditor: unverifiable
deps none declared
timestamp verb score primary_issue artifact
2026-04-26 23:47Z - 1.00 - -
2026-04-25 04:11Z - 1.00 - -
2026-04-24 06:26Z - 1.00 - -
2026-04-26 23:47Z - 1.00 - -
2026-04-25 04:11Z - 1.00 - -
2026-04-24 06:26Z - 1.00 - -
2026-04-26 23:47Z - 1.00 - -
2026-04-25 04:11Z - 1.00 - -
2026-04-24 06:26Z - 1.00 - -
2026-04-26 23:47Z - 1.00 - -