Skill Importer

Bring externally-built Claude Skills up to factory specification. Point me at a skill folder and I'll analyze what's there, identify gaps, and generate the missing pieces.

Capabilities

• Gap Analysis: Scan skill folder and identify missing factory-required files
• Structure Upgrade: Restructure SKILL.md to match factory template
• File Generation: Create missing HOW_TO_USE.md, sample_input.json, expected_output.json, README.md
• Validation: Check YAML frontmatter, naming conventions, Python code quality
• Composability Check: Ensure skill follows factory composability patterns

Input Requirements

Provide:

• Path to skill folder: Absolute or relative path to the skill to upgrade
• Skill context (optional): Additional info about what the skill does if not clear from files

Output

After import/upgrade:

skill-name/
├── SKILL.md          (restructured to factory template)
├── HOW_TO_USE.md     (generated if missing)
├── README.md         (generated if missing)
├── sample_input.json (generated if missing)
├── expected_output.json (generated if missing)
└── *.py              (preserved, analyzed for docs)

Workflow

Step 1: Analyze Existing Skill

Read all files in the skill folder:

• Check SKILL.md for YAML frontmatter (name, description)
• Identify existing Python modules
• List any existing sample/expected files
• Note documentation gaps

Step 2: Gap Report

Generate a checklist:

[ ] SKILL.md - exists but needs restructuring
[x] YAML frontmatter - valid
[ ] HOW_TO_USE.md - missing
[ ] README.md - missing
[ ] sample_input.json - missing
[ ] expected_output.json - missing
[x] Python modules - analyze.py found

Step 3: Upgrade SKILL.md

Restructure to factory template:

---
name: skill-name-kebab-case
description: One-line description
---

# Human-Readable Title

Brief introduction.

## Capabilities
- **Feature 1**: Description
- **Feature 2**: Description

## Input Requirements
- What data/info needed
- Required vs optional

## Output Formats
- What gets produced
- File types

## How to Use
Example phrases...

## Scripts (if applicable)
- `script.py`: What it does

## Best Practices
1. Guidelines

## Limitations
- Honest constraints

Step 4: Generate Missing Files

HOW_TO_USE.md:

# How to Use This Skill

Hey Claude—I just added the "{skill-name}" skill. Can you [primary use case]?

## Example Invocations

**Example 1:**
[Natural language request]

**Example 2:**
[Alternative request]

## What to Provide
- [Input 1]
- [Input 2]

## What You'll Get
- [Output 1]
- [Output 2]

sample_input.json: Minimal realistic example based on skill purpose

expected_output.json: Expected output structure

README.md: Installation and overview

Step 5: Validate

Run final validation:

• YAML frontmatter present and valid
• Name is kebab-case
• Description is concise (10-25 words)
• All required files present
• No backup/cache files
• Python compiles (if present)

How to Use

Import a skill: "Import the skill at generated-skills/issue-analysis" "Bring generated-skills/my-custom-skill up to factory spec"

Analyze only (no changes): "Analyze generated-skills/issue-analysis and show me what's missing"

Upgrade specific files: "Generate a HOW_TO_USE.md for generated-skills/issue-analysis"

Factory Compliance Checklist

File	Required	Purpose
SKILL.md	Yes	Main definition with YAML frontmatter
HOW_TO_USE.md	Yes	Example invocations
sample_input.json	Yes	Test input data
expected_output.json	Yes	Expected output
README.md	Recommended	Installation guide
*.py	If needed	Functional code

Best Practices

1. Run analysis first before making changes
2. Review generated files - they're templates to customize
3. Test sample_input produces expected_output
4. Ensure skill name is unique in your catalog

Limitations

• Cannot infer complex business logic from minimal context
• Generated samples may need manual refinement
• Python analysis is structural, not behavioral
• Won't auto-fix broken Python code