How do I install the Audit Skills skill?

Run npx skillmds@latest add ronniepinnell/audit-skills in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Audit Skills safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Audit Skills?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Audit Skills free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Audit Skills?

ronniepinnell (@ronniepinnell) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Audit Skills by ronniepinnell — Agent Skill

Name: Audit Skills
Author: ronniepinnell

Audit Skills

Efficiency audit of a skill library on four measured axes — token cost, ceremony/duplication bloat, trigger-description precision, and overlap clusters — plus usage axes when invocation telemetry exists. Every claim carries a number; findings land as diffs and budgets, not advice. Use before tightening skills, after adding many, or as a periodic library health check.

ronniepinnell Updated Sep 17, 2026

File contents

/audit-skills — A Number or It Didn't Happen

Skills are input tokens paid on every invocation. "This skill feels bloated" is a claim; this skill replaces it with measurements — and marks what it cannot measure UNVERIFIED instead of guessing.

Invocation

/audit-skills                       # audit the high-traffic set
/audit-skills --all                 # every skill in the library

Procedure

Measure (python3 scripts/audit_skills.py --json out.json — works on any skills/<name>/SKILL.md tree): per skill, token estimate, bloat % (preamble + cross-skill duplicated boilerplate), trigger-description flags, and overlap clusters (shingle Jaccard, no heavy deps).
Fold in usage only if real telemetry exists (an invocation log). Absent that, dead-weight and trigger-precision are reported UNVERIFIED — fire counts are never invented.
Classify before cutting — the heuristics lie sometimes: dense procedure (big, low ceremony) → keep; deliberate shared blocks → keep; extractable payload (verbatim templates, catalogs) → move to references/ with a read-on-demand stub; true overlap → merge.
Bank the result as a budget: record each audited skill's size + ~10% headroom and wire a lint check that fails on re-bloat. Prove the gate: append filler, watch it fail, revert.
Refute your own edit — measure the before/after token delta and run the library's full check suite before claiming "tighter, behavior unchanged". End with ONE verdict line, logged to the ledger:

AUDIT: skills | <n> in scope | <before>→<after> tok | tightened: <list> | UNVERIFIED: <axes or none>

Rules

Size alone is not bloat; dense forced procedure is the product.
"Dead" requires a fire count; "overlapping" requires a similarity score AND a human-verified same-job read.
UNVERIFIED axes stay UNVERIFIED until telemetry exists. Re-run then.

Composes with

/refute — step 5 is a forced refutation of the audit's own claim.
/gate — the per-skill budget is a standing gate.
/verdict — AUDIT: lines land in the ledger; the next audit diffs against the last.

---name: audit-skillsdescription: Efficiency audit of a skill library on four measured axes — token cost, ceremony/duplication bloat, trigger-description precision, and overlap clusters — plus usage axes when invocation telemetry exists. Every claim carries a number; findings land as diffs and budgets, not advice. Use before tightening skills, after adding many, or as a periodic library health check.---# /audit-skills — A Number or It Didn't HappenSkills are input tokens paid on every invocation. "This skill feels bloated"is a claim; this skill replaces it with measurements — and marks what itcannot measure UNVERIFIED instead of guessing.## Invocation```/audit-skills # audit the high-traffic set/audit-skills --all # every skill in the library```## Procedure1. **Measure** (`python3 scripts/audit_skills.py --json out.json` — works on any `skills/<name>/SKILL.md` tree): per skill, token estimate, bloat % (preamble + cross-skill duplicated boilerplate), trigger-description flags, and overlap clusters (shingle Jaccard, no heavy deps).2. **Fold in usage only if real telemetry exists** (an invocation log). Absent that, dead-weight and trigger-precision are reported **UNVERIFIED** — fire counts are never invented.3. **Classify before cutting** — the heuristics lie sometimes: dense procedure (big, low ceremony) → keep; deliberate shared blocks → keep; extractable payload (verbatim templates, catalogs) → move to `references/` with a read-on-demand stub; true overlap → merge.4. **Bank the result as a budget**: record each audited skill's size + ~10% headroom and wire a lint check that fails on re-bloat. Prove the gate: append filler, watch it fail, revert.5. **Refute your own edit** — measure the before/after token delta and run the library's full check suite before claiming "tighter, behavior unchanged". End with ONE verdict line, logged to the ledger:```AUDIT: skills | <n> in scope | <before>→<after> tok | tightened: <list> | UNVERIFIED: <axes or none>```## Rules- Size alone is not bloat; dense forced procedure is the product.- "Dead" requires a fire count; "overlapping" requires a similarity score AND a human-verified same-job read.- UNVERIFIED axes stay UNVERIFIED until telemetry exists. Re-run then.## Composes with- `/refute` — step 5 is a forced refutation of the audit's own claim.- `/gate` — the per-skill budget is a standing gate.- `/verdict` — `AUDIT:` lines land in the ledger; the next audit diffs against the last.

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