# Audit Skills

> Audit installed skills for redundancy with built-in model knowledge. Use when asked to "audit skills", "review my skills", "check skill redundancy", "which skills are useful", "clean up skills", or "are my skills teaching you anything new".

- Skill: `pvillega/audit-skills` (Agent Skill)
- Install (CLI): `npx skillmds@latest add pvillega/audit-skills`
- Raw SKILL.md: https://api.skillmd.com/api/skills/pvillega/audit-skills/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: pvillega (https://skillmd.com/u/pvillega)
- Updated: 2026-09-10
- Page: https://skillmd.com/skills/pvillega/audit-skills

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# Audit Skills

Review all installed skills and assess whether each teaches something the model does not already know from training.

## Step 1: Collect skills from all sources

Gather skill directories from these three locations:

1. **Project skills**: `.claude/skills/` in the current working directory
2. **Global skills**: `~/.claude/skills/` (resolve symlinks to find actual SKILL.md files)
3. **Plugin skills**: `~/.claude/plugins/cache/*/skills/` (all installed plugins)

For each location, list all directories containing a `SKILL.md` file. Record the source location alongside each skill name.

## Step 2: Read and classify each skill

For each skill found, read its `SKILL.md` (frontmatter + body, skip reference files for now).

Evaluate each skill on **two independent axes**, then classify:

### Axis 1: Knowledge — Does it teach something the model doesn't know?

Examples of novel knowledge: project conventions, repo-specific patterns, uncommon domain expertise, curated decision logic, specialized tooling (e.g., jscpd for duplication detection).

### Axis 2: Process discipline — Does it enforce a workflow the model wouldn't follow on its own?

This is the axis most commonly misjudged. A skill can be "redundant" on knowledge but **critical** on process. Ask these questions:

| Process signal | What to look for |
|---|---|
| **Hard stops / approval gates** | Does it force the model to pause and wait for user input between phases? (Models batch by default) |
| **Measurement-before-action** | Does it require baselines, profiling, or evidence before changes? (Models skip measurement by default) |
| **Scope constraints** | Does it limit how much the model does per cycle? (One transformation per commit, 80/20 rule, stop at diminishing returns) |
| **Anti-pattern tables** | Does it list specific rationalizations to watch for? ("Bottleneck is obvious", "I'll batch these", "Too late to measure") |
| **Tool-enforced coverage** | Does it use external tools (jscpd, k6, linters) that catch things invisible to manual review? |
| **Push-back instructions** | Does it tell the model to refuse or challenge the user's request in specific situations? |

**Key insight: "I know how to do X" ≠ "I will reliably follow the disciplined process for X." Knowledge and discipline are independent axes.**

### Classification matrix

| Category | Knowledge | Process | Criteria |
|----------|-----------|---------|----------|
| **Redundant** | Known | No discipline added | Teaches only general knowledge the model already has, with no workflow enforcement |
| **Valuable — process discipline** | Known or unknown | Enforces workflow | Constrains model behavior: hard stops, measurement gates, scope limits, anti-pattern guards, or external tool requirements |
| **Valuable — project-specific** | Unknown | Either | Encodes project conventions, team workflows, or repo-specific patterns the model cannot know |
| **Valuable — novel knowledge** | Unknown | Either | Provides specialized domain expertise, uncommon techniques, or curated decision logic |
| **Partially redundant** | Mixed | Mixed | Some sections are valuable (on either axis), others repeat known information without adding discipline |

## Step 3: Report findings

Present results in this format:

### Redundant skills
For each: skill name, source location, one-line reason why it duplicates built-in knowledge **and** adds no process discipline.

### Partially redundant skills
For each: skill name, source location, which sections are valuable vs redundant, noting both axes.

### Valuable skills (group by primary value type)

**Process discipline:** skill name, source location, what workflow it enforces that the model wouldn't follow unprompted.

**Project-specific:** skill name, source location, what project knowledge it encodes.

**Novel knowledge:** skill name, source location, what specialized expertise it provides.

### Summary
- Total skills scanned
- Count per category
- Recommended actions (remove, trim, keep)

## Common misclassification trap

**Do NOT classify a skill as "redundant" just because you know the domain.** The question is not "do I know how to refactor/optimize/detect duplication?" — the question is "will I reliably follow this specific process without the skill enforcing it?"

Examples of skills that look redundant but aren't:
- A refactoring skill that forces one-transformation-per-commit (you'd batch 5 without it)
- A performance skill that blocks optimization until baseline is measured (you'd skip measurement without it)
- A duplication skill that runs jscpd (you can't token-compare every file pair manually)

**Test:** If the skill contains hard stops, anti-pattern tables, measurement gates, or external tool invocations — it is almost certainly providing process value even if the knowledge feels familiar.