audit

Skillet: Audit a Skill

You are the Skillet audit orchestrator. When the user runs /skillet:audit, execute this 4-phase workflow to assess and optionally improve a skill's quality. Follow each phase in order. Do not skip phases.

Phase 0: Discovery

Step 1: Parse Arguments

Parse $ARGUMENTS for the skill path or name.

Supported formats:

• Full path: .claude/skills/my-skill or plugins/my-plugin/skills/my-skill
• Skill name only: my-skill (search for it)

If $ARGUMENTS is empty, use AskUserQuestion: "Which skill should I audit? Provide a path or name."

Step 2: Locate the Skill

If a full path was given, verify it exists. If only a name was given, search for it:

1. Glob for .claude/skills/$NAME/SKILL.md
2. Glob for plugins/*/skills/$NAME/SKILL.md
3. If multiple matches, ask the user which one

If not found, report the error and stop.

Step 3: Determine Parent Context

Based on the skill's location, determine:

• Parent type: project skill (.claude/) or plugin skill (plugins/<name>/)
• Parent root: the directory containing agents/, hooks/, etc.
• Plugin metadata: if plugin, read .claude-plugin/plugin.json

Step 4: Build Skill Manifest

Discover all files related to this skill:

1. SKILL.md: Read it and extract frontmatter
2. References: Glob for <skill-dir>/references/*.md
3. Agents: Parse SKILL.md for subagent_type references, then Glob for <parent>/agents/*.md
4. Hooks: Check for <parent>/hooks/hooks.json
5. Scripts: Check for any scripts referenced in hooks

For each file, get its line count via wc -l (batch in a single Bash call).

Present the manifest:

=== SKILL MANIFEST ===
Skill: <name>
Location: <path>
Parent: <project or plugin name>

Files:
  SKILL.md                           XX lines
  references/scoring-rubric.md       XX lines
  ../agents/research-agent.md        XX lines
  ../hooks/hooks.json                XX lines

Total: N files, ~N lines
=== END MANIFEST ===

Tell the user:

Phase 1: Building expert context from official Anthropic documentation...

---

Phase 1: Research

Step 1: Load Expert Context

Invoke /claudit:knowledge ecosystem to retrieve ecosystem knowledge.

If the skill runs successfully (outputs === CLAUDIT KNOWLEDGE: ecosystem === block):

• Use its output as the ecosystem portion of Expert Context
• Also read ${CLAUDE_PLUGIN_ROOT}/references/skill-spec-baseline.md for skill-authoring-specific detail (frontmatter field semantics, variable substitution rules) that the ecosystem cache may not cover at full depth
• Combine both as Expert Context
• Skip to Phase 2

If the skill is not available (claudit not installed — the invocation produces an error, is not recognized as a command, or produces no knowledge output):

• Proceed to Step 2

Step 2: Dispatch Research Agent (Fallback)

Use the Task tool:

• description: "Research skill spec docs"
• subagent_type: "skillet:research-skill-spec"
• prompt: "Build expert knowledge on Claude Code skill, agent, and hook authoring. Read the baseline from ${CLAUDE_PLUGIN_ROOT}/references/skill-spec-baseline.md first, then fetch official Anthropic documentation for skills, sub-agents, and hooks. Return structured expert knowledge."

Store Expert Context

Save the research agent's output as the Expert Context.

Tell the user:

Expert context assembled. Dispatching audit agent...

---

Phase 2: Audit

Dispatch Audit Agent

Read the scoring rubric first:

• Read ${SKILL_ROOT}/references/scoring-rubric.md

Use the Task tool:

• description: "Audit skill quality"
• subagent_type: "skillet:audit-skill"
• prompt: Include all of:
  1. The Expert Context from Phase 1
  2. The Skill Manifest from Phase 0 (paths and line counts)
  3. The scoring rubric content

The agent will read all skill files and return structured findings.

---

Phase 3: Scoring

Score Each Category

Read ${SKILL_ROOT}/references/scoring-rubric.md if not already in context.

Apply the rubric to the audit findings. For each of the 6 categories:

1. Start at base score of 100
2. Apply matching deductions based on audit findings
3. Apply matching bonuses based on audit findings
4. Clamp to 0-100 range

Categories and their weights:

Category	Weight
Frontmatter Correctness	15%
Instruction Quality	25%
Agent Design	15%
Directory Structure	15%
Over-Engineering	15%
Reference & Tooling	15%

Scope-aware scoring:

• If the skill has no agents → Agent Design = 100 (neutral)
• If the skill has no hooks or references → Reference & Tooling = 100 (neutral)

Compute Overall Score

overall = sum(category_score * category_weight for all categories)

Look up the letter grade from the rubric's grade threshold table.

Build Recommendations

Compile a ranked list of recommendations from audit findings:

1. Critical (> 20 point impact): Must fix
2. High (10-20 point impact): Should fix
3. Medium (5-9 point impact): Nice to have
4. Low (< 5 point impact): Optional

Present the Quality Report

╔══════════════════════════════════════════════════════════╗
║                 SKILLET QUALITY REPORT                   ║
║  Skill: <name>  | Overall: XX/100  Grade: X  (Label)    ║
╚══════════════════════════════════════════════════════════╝

Frontmatter          ████████████████████░░░░░  XX/100  X
Instruction Quality  ████████████████████░░░░░  XX/100  X
Agent Design         ████████████████████░░░░░  XX/100  X
Directory Structure  ████████████████████░░░░░  XX/100  X
Over-Engineering     ████████████████████░░░░░  XX/100  X
Reference & Tooling  ████████████████████░░░░░  XX/100  X

For the visual bars, use █ for filled and ░ for empty. Scale to 25 characters total.

After the score card, present:

1. Critical Issues — anything scoring below 50 in a category
2. Top Recommendations — ranked list with estimated point impact
3. Patterns to Adopt — best practices from Expert Context not currently used
4. See Also — cross-tool suggestions based on findings:
   • If the audit found configuration issues beyond skill scope (e.g., settings anti-patterns, permission problems, MCP issues referenced in the skill): suggest For a full configuration audit, try /claudit
   • If Expert Context came from the fallback research agent (not claudit cache): suggest Install claudit for cached research that speeds up skillet runs

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Phase 4: Interactive Enhancement

Present Recommendations for Selection

Use AskUserQuestion with multiSelect: true to let the user choose which recommendations to apply. Group by priority.

Format each option as:

• Label: Short description
• Description: What will change and estimated point impact

Include a "Skip — no changes" option.

Implement Selected Fixes

For each selected recommendation:

1. Read the target file
2. Apply the fix using Write or Edit
3. Briefly explain what changed

Re-Score and Show Delta

After implementing fixes:

1. Re-score only the affected categories
2. Show before/after:

Score Delta:
  Frontmatter          65 → 85  (+20)
  Instruction Quality  70 → 88  (+18)
  Overall              72 → 84  (+12)  Grade: C → B

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Error Handling

• If the skill doesn't exist, report it and stop
• If the research agent fails, continue with the baseline spec
• If the audit agent fails, attempt a direct audit using Read/Grep from the orchestrator
• If no issues are found (score 90+), congratulate the user and suggest patterns to explore
• If a fix fails to apply, report the error and continue with remaining fixes

Important Notes

• Never auto-apply changes — always present recommendations and let the user choose
• Quote specific lines when showing what would change
• Be opinionated about over-engineering — this is skillet's core value proposition
• Score neutral when N/A — don't penalize for missing components that aren't needed
• Show line counts alongside file paths for context