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:

Verify SKILL.md exists
Check for scripts/, references/, assets/ directories
Read SKILL.md frontmatter and body
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 Skill Completeness Checklist.

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:

Read the category definition from Skill Completeness Checklist
Review checklist items for that category
Search SKILL.md and supporting files for evidence
Score 0-3 based on rubric (below)
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:

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

Quality Categories Reference

All 8 categories are detailed in Skill Completeness Checklist 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

Skill Completeness Checklist - detailed quality categories, checklist items, and examples from Anthropic's official skills repository