agentleFS
innovationenginesinnovationengines/skills-repo/skill-audit/SKILL.md

skill-audit

Skill0 starsChanged 2 months agoMITView on GitHub

Review the user's installed Claude Skills for overlap, dead weight, vague triggers, and gaps, then deliver a keep/merge/fix/delete action plan. Use whenever the user types /skill-audit, says their skills overlap or fire at the wrong times, asks which skills to keep or delete, wants their skill library cleaned up or organized, or asks "which of my skills are actually useful". Also use after a user has installed a batch of new skills and wants to know how they fit together.

58 other projects use a version of this.

Adopted by

Most recently taken up by

Used with

Projects with it also use root-cause, reflect and 6 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • Ingramradical235Skill

    root-cause

    Structured root cause analysis using WHERE (MECE decomposition) then WHY (causal structure diagrams) pipeline. Includes coin-flip detection, logical-leap detection, and vicious cycle intervention. Use when buffer enters YELLOW/RED, when metrics stall, or when Actions execute but don't produce results.

    Also in 3 projects that use this

  • Microsoft AzureSkill

    reflect

    Learning capture system that extracts HIGH/MED/LOW confidence patterns from conversations to prevent repeating mistakes. Use after user corrections ("no", "wrong"), praise ("perfect", "exactly"), or when discovering edge cases. Complements .squad/agents/{agent}/history.md and .squad/decisions.md.

    Also in 5 projects that use this

  • sunnyclinches44Skill

    explain

    Explains code using real-world analogies and ASCII diagrams

    Also in 4 projects that use this

  • eherrerosjSkill

    humanize

    Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive 'Signs of AI writing' guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases. Triggers on: 'humanize', 'make this sound human', 'remove AI writing', 'de-AI', 'sound more natural', 'less robotic', 'too AI-sounding', or when reviewing text that reads like AI output.

    Also in 3 projects that use this

  • MicrosoftSkill

    report

    Turn an exploration (threads, findings, charts) into a single Markdown report — note, blog post, executive summary, KPI dashboard, slide brief, or multi-section analytical report, with embedded charts.

    Also in 3 projects that use this

  • SentrySkill

    review

    Fixture review skill.

    Also in 5 projects that use this

The file

We split this skill into the rules it gives an agent

5 rules. 0 are common, 4 are distinctive, and 1 is very specific to this project.

  • 0

    Common

    Most of the 51 edited versions we read keep these.

  • 4

    Distinctive

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  • could be automatic: a setting could enforce it
  1. Very specific:

    If the user can share the actual SKILL.md files, audit those — that enables the deepest checks.

    line 15
  2. Distinctive:

    Never invent skills the user didn't confirm having.

    line 17
  3. Distinctive:

    ALWAYS use this exact structure:

    line 33
  4. Distinctive:

    A healthy library is a valid result: If the collection is clean, say so, fix the one or two weakest descriptions, and stop.

    line 59
  5. Distinctive:

    Merges must be concrete: Never just say "merge A and B" — state the merged skill's name, its one-line job, and which instructions survive …

    line 60

“Common” is measured: kept by at least 6 in 10 of the 51 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Skill Audit

A skill library degrades the same way a prompt library does: things pile up, two skills quietly do the same job, and descriptions get vague enough that nothing triggers reliably. This skill audits the collection and produces a ruthless, actionable cleanup plan.

Step 1 — Get the inventory

Work from the best available source, in this order:

  1. The skills visible in the current environment (if you can see an installed skills list, use it).
  2. Otherwise, ask the user to paste their list from Settings → Capabilities → Skills — names are enough to start; descriptions make the audit much better.
  3. If the user can share the actual SKILL.md files, audit those — that enables the deepest checks.

Never invent skills the user didn't confirm having.

Step 2 — Map each skill to its job

For every skill, write one line: the job it's hired for, in the form "when [situation], produce [output]". If you cannot write that line from the skill's name and description, that is itself a finding — the skill has a triggering problem.

Step 3 — Run the five checks

  1. Overlap. Two or more skills hired for the same job (e.g. a /humanizer and an /anti-ai that both strip AI tells). Overlapping skills split triggering unpredictably — Claude may pick either one. Verdict: merge into one, or sharpen each description until the boundary is unambiguous (e.g. one runs on drafts, the other runs during prompt-writing).
  2. Vague triggers. Descriptions like "helps with productivity" or "improves writing" — categories, not situations. These undertrigger (Claude skips them) or overtrigger (they fire on everything). Verdict: rewrite the description to name the exact user phrases and situations that should fire it.
  3. Dead weight. Skills that duplicate what Claude does well with a one-line prompt (e.g. a skill that just says "summarize this"). A skill earns its slot by encoding a procedure, format, or standard Claude wouldn't apply by default. Verdict: delete, and note the one-line prompt that replaces it.
  4. Scope creep. One skill doing three jobs ("writes posts, replies to comments, and plans content"). Multi-job skills trigger at the wrong times and dilute their instructions. Verdict: split, and name the resulting skills.
  5. Gaps. Look at the user's actual recurring workflow (ask one question about it if unclear) and name at most 2-3 missing skills that would compound with the existing ones. Do not pad this list — a gap is only real if the user repeats that task weekly.

Step 4 — Deliver the audit

ALWAYS use this exact structure:

## Inventory
[N] skills audited.

## Findings
| Skill | Job it's hired for | Verdict | Why |
|-------|--------------------|---------|-----|
| /name | when X, produce Y  | ✅ Keep / 🔀 Merge / 🔧 Fix description / 🗑️ Delete / ✂️ Split | one line |

## Action plan (do in this order)
1. [Highest-impact action first — usually the merge or the worst description fix]
2. ...

## Rewritten descriptions
[For every 🔧: the ready-to-paste replacement description,
naming concrete trigger situations and user phrases.]

## Gaps worth filling (max 3)
- /suggested-name — [the recurring job it would do]

Judgment calls

  • Be conservative with 🗑️. Deleting a skill the user loves destroys trust in the audit. If usage is unknown, mark it "🗑️ Delete (confirm: when did you last use this?)" instead of asserting.
  • A healthy library is a valid result. If the collection is clean, say so, fix the one or two weakest descriptions, and stop. An audit that always finds problems is a horoscope.
  • Merges must be concrete. Never just say "merge A and B" — state the merged skill's name, its one-line job, and which instructions survive from each parent.
  • Respect the library's purpose. A content creator's library and a developer's library have different "dead weight" thresholds. Judge each skill against the user's actual work, not an abstract ideal.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.