Skill Creator

Create self-contained skills that package workflows into portable, discoverable units.

Creation Process

Step 1: Understand the Workflow

Ask the user to describe the workflow. Identify:

• What triggers this workflow (user request, event, schedule)
• What inputs it needs (URLs, files, API keys, config)
• What steps it follows (in order, with dependencies)
• What outputs it produces (files, API calls, messages, data)
• What can go wrong (rate limits, auth failures, missing data)

If the workflow was just completed manually, reverse-engineer it: "Let me crystallize exactly what we just did into a reusable skill."

Step 2: Plan the Skill Contents

For each step, decide the execution method:

Rules:

• Every script has ONE job. If it does multiple things, split it.
• If 2+ skills would reference it, it goes in context/ not references/.
• Default to Python for deterministic work. MCP adds token overhead.

Step 3: Scaffold the Skill

Run the init script:

python3 .claude/skills/skill-creator/scripts/init_skill.py SKILL_NAME

This creates the directory structure with placeholders. Then populate.

Step 4: Write the Frontmatter

---
name: kebab-case-name
description: What it does. Use when user says "trigger 1", "trigger 2", or asks to do X.
model: sonnet
context: fork
allowed-tools: Bash(python3 .claude/skills/SKILL_NAME/scripts/*)
---

Frontmatter decisions:

See references/frontmatter-guide.md for detailed examples.

Step 5: Write the SKILL.md Body

Use imperative SOP format:

# Skill Title

## Objective
One sentence: what this skill achieves.

## Inputs Required
- List all inputs, credentials, config needed

## Execution Steps

### Step 1: Name of Step
\`\`\`bash
python3 .claude/skills/SKILL_NAME/scripts/script_name.py --flag value
\`\`\`
**Input**: What the script takes
**Output**: What the script produces (JSON structure)

## Edge Cases & Error Handling
### Scenario Name
- What goes wrong
- How to handle it

Body rules:

• Under 500 lines. Move detailed docs to references/.
• Every script command includes full path from project root.
• Include expected output JSON structures for each script.

Step 6: Implement Scripts

Write Python scripts in scripts/. Each script:

• Has ONE job
• Accepts input via CLI args
• Returns JSON to stdout on success
• Returns JSON error to stderr on failure
• Handles its own errors

Step 7: Test the Skill

Three tests:

1. Trigger test: Fresh session. Ask naturally without naming the skill. Does Claude find it?
2. Functional test: Run 4-5 times with different inputs. Consistent output?
3. Value test: Compare output WITH vs WITHOUT the skill. Measurable improvement?

Step 8: Iterate

After real usage, refine:

• Tighten instructions where Claude improvised incorrectly
• Add edge cases discovered in production
• Move sections to references/ if SKILL.md exceeds 500 lines

Skill Anatomy

.claude/skills/skill-name/
├── SKILL.md              # Process definition (<500 lines)
├── scripts/              # Python scripts (one job each)
├── references/           # Skill-specific docs
└── assets/               # Templates, fonts, icons

What does NOT go in the skill:

• Business knowledge used by multiple skills → context/
• Runtime behavior settings → config/
• Persistent data → data/
• Temporary files → .tmp/

Design Patterns

See references/patterns.md for detailed pattern documentation:

• Sequential — Steps in strict order with dependencies
• Iterative refinement — Generate → validate → fix → repeat
• Multi-MCP coordination — Cross-service workflows
• Context-aware branching — Different paths based on input type
• Domain-specific — Embedded business rules and compliance