Audit Skill Completeness

Purpose

Evaluates a single skill directory against 8 quality categories derived from Anthropic's official skills repository. Each category is scored 0-3, producing an overall completeness percentage and actionable recommendations for improvement.

When to Use

Invoke this skill when:

• Pre-marketplace publication review - verify skill meets quality standards
• Post-creation quality check - evaluate newly created skills
• Skill improvement planning - identify specific quality gaps
• Comparing local skills to Anthropic patterns - benchmark against official standards
• Marketplace readiness assessment - determine if skill is publication-ready

Workflow

Step 1: Discovery

Read the skill directory structure:

skill-path/
├── SKILL.md          # Required - main skill definition
├── scripts/          # Optional - executable automation
├── references/       # Optional - supporting documentation
└── assets/           # Optional - reusable output resources

Actions:

1. Verify SKILL.md exists
2. Check for scripts/, references/, assets/ directories
3. Read SKILL.md frontmatter and body
4. List all files in each directory

Validation:

• If SKILL.md missing, report error and exit
• If path is not a directory, report error and exit

Step 2: Evaluate Quality Categories

Run through each of the 8 categories using the detailed checklist in references/skill-completeness-checklist.md.

Quality Categories:

Category	Evaluates	Key Indicators
1. Preparation	Prerequisites met before work begins	Environment verification, input inspection, metadata extraction scripts
2. Progression	Concrete steps with right level of control	Clear sequence, deterministic scripts, working examples, decision trees
3. Verification	Output correctness confirmed before success	Explicit verification steps, automated checks, error-correction loops, acceptance criteria
4. Scripts	Executable automation for core operations	Repetitive operations scripted, --help support, edge case handling, tested output
5. Examples	Teaching through demonstration	Working code with imports, exact input→output pairs, common cases, edge case handling
6. Anti-Patterns	Explicit "what NOT to do"	Known failure modes documented, bad output shown, corrections side-by-side
7. References	Domain knowledge AI cannot generate	API/schema/format documentation, organized sections, linked from workflow steps
8. Assets	Reusable output resources bundled	Templates, fonts, images, boilerplate the AI uses (not reads)

Evaluation Process:

For each category:

1. Read the category definition from references/skill-completeness-checklist.md
2. Review checklist items for that category
3. Search SKILL.md and supporting files for evidence
4. Score 0-3 based on rubric (below)
5. Document findings with file:line references

Step 3: Score and Report

Calculate overall score and write report to .claude/audits/completeness-report-{skill-slug}.md.

Report Structure:

# Skill Completeness Report: {skill-name}

**Evaluated:** {timestamp}
**Skill Path:** {absolute-path}

## Overall Score: {percentage}% ({score}/24)

| Category | Score | Label | Findings |
|----------|-------|-------|----------|
| 1. Preparation | 2 | Adequate | Environment checks present, missing metadata extraction |
| 2. Progression | 3 | Exemplary | Clear workflow, deterministic scripts, decision tree |
| ... | ... | ... | ... |

## Category Details

### 1. Preparation (2/3 - Adequate)

**What was evaluated:**
- Environment verification before starting
- Input inspection before acting
- Metadata extraction scripts

**Evidence found:**
- ✅ Environment check at SKILL.md:45-50
- ✅ Input validation at SKILL.md:65
- ❌ No metadata extraction script in scripts/

**Recommendation:**
Add a script to extract structured metadata from inputs so the AI operates on verified data instead of assumptions.

### 2. Progression (3/3 - Exemplary)

...

## Recommendations for Improvement

1. **High Priority:** Add metadata extraction script (Preparation)
2. **Medium Priority:** Include anti-pattern examples (Anti-Patterns)
3. **Low Priority:** Add visual validation examples (Verification)

## Reference

This audit follows patterns from Anthropic's official skills repository:
- https://github.com/anthropics/skills

Checklist: [Skill Completeness Checklist](./references/skill-completeness-checklist.md)

Output Location:

Report written to .claude/audits/completeness-report-{skill-slug}.md

If .claude/audits/ does not exist, create it.

Scoring Rubric

Each category is scored 0-3 based on presence and quality of evidence:

Score	Label	Meaning	Criteria
0	None	Category not addressed	No evidence found for any checklist items
1	Minimal	Basic attempt, significant gaps	1-2 checklist items present, core patterns missing
2	Adequate	Meets expectations, minor gaps	3-4 checklist items present, core patterns followed
3	Exemplary	Exceeds expectations, Anthropic patterns	All or most checklist items present, matches Anthropic quality

Overall Score Calculation:

Sum of category scores / 24 * 100 = percentage

Scoring Guidelines:

• Preparation (0-3):

  • 0: No environment checks, no input validation, no metadata extraction
  • 1: Environment checks OR input validation present
  • 2: Environment checks AND input validation present
  • 3: Environment checks, input validation, AND metadata extraction scripts

• Progression (0-3):

  • 0: No clear workflow, AI must generate all code
  • 1: Workflow defined but no scripts or examples
  • 2: Workflow defined with scripts OR examples
  • 3: Workflow defined with scripts AND examples AND decision trees

• Verification (0-3):

  • 0: No verification steps mentioned
  • 1: Manual verification suggested but not enforced
  • 2: Verification steps defined with acceptance criteria
  • 3: Automated verification scripts with error-correction loops

• Scripts (0-3):

  • 0: No scripts provided
  • 1: 1-2 scripts, limited functionality
  • 2: 3-5 scripts covering core operations
  • 3: 6+ scripts, --help support, comprehensive coverage

• Examples (0-3):

  • 0: No examples provided
  • 1: Abstract examples or pseudocode only
  • 2: Working examples with imports and realistic data
  • 3: Working examples covering common AND edge cases

• Anti-Patterns (0-3):

  • 0: No anti-patterns documented
  • 1: Anti-patterns mentioned but not shown
  • 2: Anti-patterns shown with corrections
  • 3: Anti-patterns shown with corrections AND reasoning

• References (0-3):

  • 0: No reference material
  • 1: External links only (not bundled)
  • 2: 1-2 reference files in references/
  • 3: 3+ reference files, organized by topic, linked from workflow

• Assets (0-3):

  • 0: No assets provided
  • 1: 1-2 asset files
  • 2: 3-5 asset files, organized
  • 3: 6+ asset files or comprehensive asset library

Output Format

Report filename: completeness-report-{skill-slug}.md

Where {skill-slug} is the skill directory name (e.g., audit-skill-completeness → completeness-report-audit-skill-completeness.md)

Report sections:

1. Header - skill name, path, timestamp
2. Overall Score - percentage and raw score
3. Summary Table - all categories with scores
4. Category Details - for each category:
   • Score and label
   • What was evaluated (checklist items)
   • Evidence found (file:line references)
   • Recommendations for improvement
5. Recommendations Summary - prioritized list
6. Reference - link to checklist and Anthropic repository

Quality Categories Reference

All 8 categories are detailed in references/skill-completeness-checklist.md with:

• Checklist items for each category
• Examples from Anthropic's official skills
• Patterns observed across creative, document, and developer skills
• Rationale for why each pattern matters

Additional Resources

• references/skill-completeness-checklist.md - detailed quality categories, checklist items, and examples from Anthropic's official skills repository