extract
Turn a proven pattern or debugging solution into a standalone reusable skill with SKILL.md, reference docs, and examples.
Best use case
extract is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Turn a proven pattern or debugging solution into a standalone reusable skill with SKILL.md, reference docs, and examples.
Teams using extract should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/extract/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How extract Compares
| Feature / Agent | extract | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Turn a proven pattern or debugging solution into a standalone reusable skill with SKILL.md, reference docs, and examples.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# /si:extract — Create Skills from Patterns
Transforms a recurring pattern or debugging solution into a standalone, portable skill that can be installed in any project.
## Usage
```
/si:extract <pattern description> # Interactive extraction
/si:extract <pattern> --name docker-m1-fixes # Specify skill name
/si:extract <pattern> --output ./skills/ # Custom output directory
/si:extract <pattern> --dry-run # Preview without creating files
```
## When to Extract
A learning qualifies for skill extraction when ANY of these are true:
| Criterion | Signal |
|---|---|
| **Recurring** | Same issue across 2+ projects |
| **Non-obvious** | Required real debugging to discover |
| **Broadly applicable** | Not tied to one specific codebase |
| **Complex solution** | Multi-step fix that's easy to forget |
| **User-flagged** | "Save this as a skill", "I want to reuse this" |
## Workflow
### Step 1: Identify the pattern
Read the user's description. Search auto-memory for related entries:
```bash
MEMORY_DIR="$HOME/.claude/projects/$(pwd | sed 's|/|%2F|g; s|%2F|/|; s|^/||')/memory"
grep -rni "<keywords>" "$MEMORY_DIR/"
```
If found in auto-memory, use those entries as source material. If not, use the user's description directly.
### Step 2: Determine skill scope
Ask (max 2 questions):
- "What problem does this solve?" (if not clear)
- "Should this include code examples?" (if applicable)
### Step 3: Generate skill name
Rules for naming:
- Lowercase, hyphens between words
- Descriptive but concise (2-4 words)
- Examples: `docker-m1-fixes`, `api-timeout-patterns`, `pnpm-workspace-setup`
### Step 4: Create the skill files
**Spawn the `skill-extractor` agent** for the actual file generation.
The agent creates:
```
<skill-name>/
├── SKILL.md # Main skill file with frontmatter
├── README.md # Human-readable overview
└── reference/ # (optional) Supporting documentation
└── examples.md # Concrete examples and edge cases
```
### Step 5: SKILL.md structure
The generated SKILL.md must follow this format:
```markdown
---
name: <skill-name>
description: "<one-line description>. Use when: <trigger conditions>."
---
# <Skill Title>
> One-line summary of what this skill solves.
## Quick Reference
| Problem | Solution |
|---------|----------|
| {{problem 1}} | {{solution 1}} |
| {{problem 2}} | {{solution 2}} |
## The Problem
{{2-3 sentences explaining what goes wrong and why it's non-obvious.}}
## Solutions
### Option 1: {{Name}} (Recommended)
{{Step-by-step with code examples.}}
### Option 2: {{Alternative}}
{{For when Option 1 doesn't apply.}}
## Trade-offs
| Approach | Pros | Cons |
|----------|------|------|
| Option 1 | {{pros}} | {{cons}} |
| Option 2 | {{pros}} | {{cons}} |
## Edge Cases
- {{edge case 1 and how to handle it}}
- {{edge case 2 and how to handle it}}
```
### Step 6: Quality gates
Before finalizing, verify:
- [ ] SKILL.md has valid YAML frontmatter with `name` and `description`
- [ ] `name` matches the folder name (lowercase, hyphens)
- [ ] Description includes "Use when:" trigger conditions
- [ ] Solutions are self-contained (no external context needed)
- [ ] Code examples are complete and copy-pasteable
- [ ] No project-specific hardcoded values (paths, URLs, credentials)
- [ ] No unnecessary dependencies
### Step 7: Report
```
✅ Skill extracted: {{skill-name}}
Files created:
{{path}}/SKILL.md ({{lines}} lines)
{{path}}/README.md ({{lines}} lines)
{{path}}/reference/examples.md ({{lines}} lines)
Install: /plugin install (copy to your skills directory)
Publish: clawhub publish {{path}}
Source: MEMORY.md entries at lines {{n, m, ...}} (retained — the skill is portable, the memory is project-specific)
```
## Examples
### Extracting a debugging pattern
```
/si:extract "Fix for Docker builds failing on Apple Silicon with platform mismatch"
```
Creates `docker-m1-fixes/SKILL.md` with:
- The platform mismatch error message
- Three solutions (build flag, Dockerfile, docker-compose)
- Trade-offs table
- Performance note about Rosetta 2 emulation
### Extracting a workflow pattern
```
/si:extract "Always regenerate TypeScript API client after modifying OpenAPI spec"
```
Creates `api-client-regen/SKILL.md` with:
- Why manual regen is needed
- The exact command sequence
- CI integration snippet
- Common failure modes
## Tips
- Extract patterns that would save time in a *different* project
- Keep skills focused — one problem per skill
- Include the error messages people would search for
- Test the skill by reading it without the original context — does it make sense?Related Skills
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