skill-distillery（技能酿酒厂）

Skill Distillery

Distill raw material into potent skills. Articles, talks, frameworks, recent AI work, or just an idea — skill-distillery extracts the strategy bottom-up and builds interactive, well-architected skills from it.

The core insight: Good skills aren't restructured content — they're distilled strategy. A 5,000-word article becomes a 300-line skill by extracting principles, mental models, and decision frameworks, not by reformatting paragraphs.

Quick Start

From an article or framework:

/skill-distillery https://example.com/great-article-about-design-systems /skill-distillery ~/Documents/talk-transcript.md

From recent AI work:

/skill-distillery capture — "I just figured out a great process for X, help me turn it into a skill"

From a description:

/skill-distillery — "I want a skill that helps me write better API documentation"

Audit an existing skill:

/skill-distillery audit ~/.claude/skills/my-skill /skill-distillery review — "Can you evaluate the language-market-fit skill?"

Add to an existing skill:

/skill-distillery add reference — "I want to add a new reference file to my-skill"

Core Principles

These apply to everything skill-distillery does:

1. Bottom-up synthesis over content restructuring — Extract principles and mental models from source material. Don't reformat — distill.
2. Interactive by default — Use AskUserQuestion for tappable decisions. Ask 2-3 questions at a time, not a wall. Provide shortcuts when context is already available.
3. Propose before building — Always present synthesis, architecture, and approach for user approval before creating files. Wait gates at every decision point.
4. Progressive disclosure — SKILL.md stays lean (200-300 lines). Reference files contain depth. Load conditionally based on what the user needs.
5. Opinionated defaults — Suggest interactive patterns from proven skills (design-portfolio-assistant's discovery phase, language-market-fit's mode detection). The user can override, but the defaults should be good.
6. Research on demand, not by default — Most skill creation doesn't need research. Trigger it only when external APIs/libraries are involved or the user explicitly asks. See Research Protocol below.
7. Tailored to the user's specific goal — Not generic skill-making advice. Every decision adapts to what this particular user is building for this particular purpose.
8. Gotchas are the highest-signal content — The most valuable part of any skill is where Claude typically fails. Every skill should have a dedicated gotchas section documenting specific failure points with "don't do X because Y" structure. Gotchas come from anticipating Claude's failure modes, not theoretical anti-patterns.
9. Publication-ready by default — Every skill gets a README.md assuming it may be published on GitHub. After building, offer to publish via npx skills add (the skills.sh ecosystem). Ask about target agents early — Claude Code only vs. universal — since it shapes how features like AskUserQuestion are implemented.

Research Protocol

When and how to research during skill creation.

When to trigger:

• Skill involves external APIs or libraries needing current docs
• Domain where best practices shift fast (frameworks, deployment patterns)
• Audit reveals the skill references outdated libraries or patterns
• User explicitly asks: "Can you research X?"

When NOT to trigger:

• Source material is self-contained (articles, talks)
• User already knows the domain
• Skill is about internal processes or conventions
• Simple skills that don't reference external tools

How to ask: One gate before spawning anything:

This involves [X]. Want me to research current best practices, or do you have enough context?

If AskUserQuestion is available:

• Yes, research first — Fetch current documentation for accurate implementation
• No, I have context — Proceed with what we have

How to execute: Spawn 2-3 parallel Sonnet agents (fast, cheap, good at retrieval) with specific, scoped questions. Not "research everything about X" — scope to what you actually need.

Example agent prompts:

• "Fetch current Supabase RLS patterns from official docs"
• "Find 2026 best practices for React Native navigation libraries"
• "Check if [library] is still actively maintained"

Use Context7 MCP when available: resolve-library-id → query-docs for library documentation.

How to use results: Feed research back into synthesis and strategy — inform the principles and architecture. Don't dump raw findings into reference files. Present key findings to user before proceeding.

Entry Point Detection

Detect what the user provided and route to the right workflow:

Detection Logic

1. Check $ARGUMENTS for URLs → from-source-material
2. Check $ARGUMENTS for file paths → read the file, determine if it's a skill (has YAML frontmatter with name/description) or source material
3. Check $ARGUMENTS for keywords: "audit", "review", "evaluate", "improve" → audit-existing-skill
4. Check $ARGUMENTS for keywords: "add reference", "add workflow", "add component" → add-component
5. Check $ARGUMENTS for keywords: "capture", "just did", "turn this into" → from-recent-work
6. If $ARGUMENTS contains a description → from-description
7. If no arguments, ask:

If AskUserQuestion is available:

• Turn source material into a skill — I have an article, talk, or framework
• Capture recent work — I just did something and want to save the process
• Describe what I want — I'll explain what the skill should do
• Audit an existing skill — Evaluate a skill I have or found

Otherwise ask: "What would you like to do? (1) Turn source material into a skill, (2) Capture a recent process, (3) Describe what I want, (4) Audit an existing skill"

After selecting a workflow, read it and follow it exactly.

Reference Index

All supporting knowledge in references/:

Workflow Index

Templates

Ready-to-copy starting points in templates/: