Skill Quality Validation

Ensures Claude Code skills follow best practices for discoverability, structure, content quality, and effectiveness. This skill provides checklists, patterns, and validation criteria for creating high-quality skills.

When to Use This Skill

Use this skill when you see these patterns:

✅ Yes, use this skill for:

• "Create a new skill for [topic]"
• "Review this skill for quality"
• "Why isn't my skill being invoked?"
• "Improve this skill's structure"
• "Prepare this skill for sharing"
• "Debug skill invocation issues"
• "Make this skill more effective"

❌ No, use different skills for:

• Writing skill content (use topic-specific skills)
• Testing specific functionality (use testing skills)
• Code review (use code-review skills)

Quick Reference

Core Principles

Every skill must have:

• ✅ Specific description with trigger keywords (< 100 chars)
• ✅ Under 500 lines (split into directory if longer)
• ✅ Concrete examples (not abstract)
• ✅ Consistent terminology
• ✅ Progressive disclosure (most important first)

Red flags:

• ❌ Vague description like "Help with Python"
• ❌ Single file over 500 lines
• ❌ Abstract guidance without examples
• ❌ Mixing terminology (e.g., "commit" and "change" without explanation)
• ❌ Time-sensitive info (e.g., "new tool just released")

Quality Checklist Workflow

When creating or reviewing a skill, copy this checklist and follow the steps:

Skill Quality Review Progress:
- [ ] Step 1: Verify description and metadata
- [ ] Step 2: Check structure and organization
- [ ] Step 3: Validate content quality
- [ ] Step 4: Review code and scripts (if applicable)
- [ ] Step 5: Test across models
- [ ] Step 6: Perform real usage testing

Step 1: Verify Description and Metadata

Check the YAML frontmatter:

• [ ] Description includes specific trigger keywords (what users will say)
• [ ] Description explains WHAT the skill does and WHEN to use it
• [ ] Description is in third person ("Validates...", not "Apply...")
• [ ] Description under 1024 characters
• [ ] Priority is set appropriately (5-7 for most skills)
• [ ] Name uses lowercase, hyphens, no reserved words

If checks fail: Update frontmatter before proceeding.

Step 2: Check Structure and Organization

Review file organization:

• [ ] SKILL.md is under 500 lines
• [ ] Uses directory structure if over 500 lines
• [ ] "When to Use This Skill" section exists and is clear
• [ ] Progressive disclosure: most important content first
• [ ] Headers are descriptive and scannable
• [ ] File references are one level deep maximum

If checks fail: Reorganize content or split into supporting files.

Step 3: Validate Content Quality

Review the skill content:

• [ ] Examples are concrete and copy-pasteable
• [ ] All code examples are runnable
• [ ] Terminology is consistent throughout
• [ ] No time-sensitive information (or properly isolated)
• [ ] Workflows have clear numbered steps
• [ ] Decision trees for complex choices
• [ ] All placeholders are explained or replaced

If checks fail: Add missing examples or clarify instructions.

Step 4: Review Code and Scripts

If skill includes executable code:

• [ ] Scripts solve problems (don't punt to Claude)
• [ ] Error handling is explicit with helpful messages
• [ ] All constants are justified (no "voodoo constants")
• [ ] Dependencies are listed with install instructions
• [ ] Paths use forward slashes (not backslashes)
• [ ] Validation/feedback loops for critical operations

If checks fail: Improve error handling and documentation.

Step 5: Test Across Models

Test with all Claude models:

• [ ] Tested with Haiku (simple case works)
• [ ] Tested with Sonnet (moderate complexity works)
• [ ] Tested with Opus (complex case works)
• [ ] Skill invoked correctly in all cases
• [ ] Responses follow skill guidance consistently

If checks fail: Adjust description or add more explicit guidance.

Step 6: Perform Real Usage Testing

Test in actual workflows:

• [ ] Fresh start test (new project, no external docs)
• [ ] Colleague test (someone else uses it)
• [ ] Different project test (verify it's project-agnostic)
• [ ] Error path test (intentionally trigger failures)

If checks fail: Update skill based on observed issues.

File Structure

For skills under 500 lines:

my-skill.md                # Single file

For skills over 500 lines:

my-skill/
├── SKILL.md              # Main instructions (< 500 lines)
├── examples.md           # Detailed examples
├── reference.md          # API/command reference (optional)
└── scripts/              # Helper scripts (optional)
    └── validate.py

Key principles:

• SKILL.md always under 500 lines
• Related files use UPPERCASE for visibility (FORMS.md, EXAMPLES.md)
• Scripts in subdirectory, executed not loaded as context
• Each file has single, clear purpose

Example from real skill:

pdf/
├── SKILL.md              # Core PDF guidance
├── FORMS.md              # Form-filling specific guidance
├── examples.md           # Extended examples
└── scripts/
    ├── analyze_form.py   # Utility script
    └── fill_form.py      # Form processor

Core Quality Standards

1. Description Quality

Format: Frontmatter YAML at top of SKILL.md

---
description: "Specific action + key terms + when to use"
priority: 5
---

Requirements:

• Include key terms that trigger the skill
• Explain both WHAT and WHEN
• Keep under 100 characters
• Use terms users naturally say

📖 See EXAMPLES.md for good/bad examples

2. Content Structure

SKILL.md must be:

• Under 500 lines total
• Well-organized with clear sections
• Using progressive disclosure
• Focused on one coherent topic

If exceeding 500 lines:

1. Split into directory structure
2. Keep core guidance in SKILL.md
3. Move detailed examples to examples.md
4. Move reference material to reference.md
5. Move scripts to scripts/ subdirectory

Progressive disclosure pattern:

# Skill Name

Brief intro (1-2 sentences)

## When to Use

Quick bullet list

## Quick Reference

Most common cases with examples

## Detailed Guidance

(Or link to examples.md)

## Advanced Patterns

(Or link to patterns.md)

3. Terminology Consistency

Rules:

• Use consistent terms throughout all files
• Establish vocabulary early
• Explain synonyms when first used
• Don't mix related terms without explanation

📖 See EXAMPLES.md for patterns

4. Concrete Examples

Every pattern needs a real, runnable example.

Examples must:

• Be copy-pasteable
• Show actual code/commands
• Include expected output
• Demonstrate the principle

📖 See EXAMPLES.md for good/bad examples

5. File Reference Depth

Keep references one level deep:

See examples.md for detailed patterns # ✅ Good

See examples.md which references patterns.md
which has code in scripts/ # ❌ Bad - too deep

6. Time-Sensitive Information

Isolate or avoid time-sensitive content:

## Current Best Practice (as of 2024)

Use ast-grep for syntax-aware searches

## Legacy Patterns

Previously, ripgrep was used...

📖 See EXAMPLES.md for deprecation patterns

Code and Script Quality

Scripts Should Solve Problems

Don't punt to Claude - solve the problem in the script:

• ✅ Validate and return specific errors
• ✅ Handle edge cases explicitly
• ✅ Provide actionable error messages
• ❌ Leave TODOs for Claude to figure out
• ❌ Generic "check this" functions

Error Handling

Every error path needs helpful messages:

except FileNotFoundError:
    print("Error: jj not found. Install with: brew install jj")
    sys.exit(1)

No Voodoo Constants

Justify all magic numbers:

TIMEOUT_SECONDS = 30  # API requests take 5-10s, allow 3x buffer

Package Verification

List all dependencies with install instructions:

## Dependencies

Required:

- `ast-grep` - Install: `brew install ast-grep`

Verify: `which ast-grep`

📖 See EXAMPLES.md for detailed patterns

Workflow Quality

Clear Steps

Use numbered steps with verification:

1. **Create directory:**
   ```bash
   mkdir my-dir
   ```

Verify: ls my-dir

2. Create file: ...

### Decision Trees

**Complex workflows need decision points:**

```markdown
**Need X?** → Use tool A
**Need Y?** → Use tool B
**Need both?** → Use A then B

📖 See EXAMPLES.md for patterns

Testing

Every skill needs testing across:

• Models: Haiku, Sonnet, Opus
• Scenarios: Simple, edge case, complex
• Real usage: New project, no external help

📖 See TESTING.md for detailed testing guidelines

Troubleshooting

Common issues:

• Skill not being invoked → Check description keywords
• Too broad → Split into focused skills
• Too abstract → Add concrete examples

📖 See TROUBLESHOOTING.md for complete guide

Quality Self-Check

Before considering a skill complete, copy this checklist and verify each item:

Skill Quality Verification:
- [ ] Can someone use this without follow-up questions?
- [ ] Would this work in 6 months?
- [ ] Are examples copy-pasteable and runnable?
- [ ] Can you find guidance in < 30 seconds?
- [ ] Are error messages helpful enough?
- [ ] Does the description include key trigger terms?
- [ ] Is SKILL.md under 500 lines?
- [ ] Are file references one level deep?
- [ ] Is terminology consistent throughout?

If any check fails:

1. Can't use without follow-up questions → Add more concrete examples
2. Won't work in 6 months → Isolate time-sensitive info in "Current Best Practice" sections
3. Examples not copy-pasteable → Complete all placeholders and add setup steps
4. Can't find guidance quickly → Improve headers and add table of contents
5. Error messages unclear → Add context, hints, and recovery steps
6. Description lacks triggers → Add specific terms users naturally say
7. SKILL.md too long → Split into directory with reference files
8. Deep file references → Consolidate or flatten structure
9. Inconsistent terminology → Choose one term and use everywhere

Evaluation Scenarios

Test this skill with these scenarios to ensure it works effectively:

Scenario 1: Simple Case - New Skill Creation

Input: "Help me create a new skill for managing Docker containers"

Expected behavior:

• Skill is invoked and recognized
• Provides description template with trigger keywords
• Suggests file structure (single file vs directory)
• Offers checklist for required sections
• Reminds about concrete examples requirement

Verify:

• Skill invocation happens automatically
• Response includes specific checklist items
• Guidance is actionable and clear

Scenario 2: Edge Case - Skill Not Being Invoked

Input: "My skill exists but Claude never uses it"

Expected behavior:

• Skill is invoked and recognized
• Diagnoses common invocation issues
• Checks description for trigger keywords
• Verifies file location and frontmatter format
• Suggests testing phrases

Verify:

• Troubleshooting steps are provided
• Specific fixes offered for each issue
• Testing methodology explained

Scenario 3: Complex Case - Comprehensive Skill Review

Input: "Review my python-scripts skill for quality and best practices"

Expected behavior:

• Skill is invoked and recognized
• Provides complete quality checklist
• Reviews description, structure, examples, and testing
• Identifies specific gaps or issues
• Suggests prioritized improvements
• References relevant sections of examples.md

Verify:

• All quality dimensions covered
• Specific, actionable feedback provided
• Prioritization of issues clear
• References to supporting documentation included

Additional Resources

• EXAMPLES.md - Detailed good/bad examples for all principles
• TESTING.md - Complete testing guidelines
• TROUBLESHOOTING.md - Common issues and fixes