- Update skill count to 170, Python tools to 213, references to 314 across all docs - Regenerate all 170 skill doc pages from latest SKILL.md sources - Update CLAUDE.md with v2.1.1 highlights, accurate architecture tree, and roadmap - Update README.md badges and overview table - Update marketplace.json metadata description and version - Update mkdocs.yml, index.md, getting-started.md with correct numbers
186 lines
5.1 KiB
Markdown
186 lines
5.1 KiB
Markdown
---
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title: "/si:extract — Create Skills from Patterns"
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description: "/si:extract — Create Skills from Patterns - Claude Code skill from the Engineering - Core domain."
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---
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# /si:extract — Create Skills from Patterns
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**Domain:** Engineering - Core | **Skill:** `extract` | **Source:** [`engineering-team/self-improving-agent/skills/extract/SKILL.md`](https://github.com/alirezarezvani/claude-skills/tree/main/engineering-team/self-improving-agent/skills/extract/SKILL.md)
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---
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# /si:extract — Create Skills from Patterns
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Transforms a recurring pattern or debugging solution into a standalone, portable skill that can be installed in any project.
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## Usage
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```
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/si:extract <pattern description> # Interactive extraction
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/si:extract <pattern> --name docker-m1-fixes # Specify skill name
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/si:extract <pattern> --output ./skills/ # Custom output directory
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/si:extract <pattern> --dry-run # Preview without creating files
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```
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## When to Extract
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A learning qualifies for skill extraction when ANY of these are true:
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| Criterion | Signal |
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|---|---|
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| **Recurring** | Same issue across 2+ projects |
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| **Non-obvious** | Required real debugging to discover |
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| **Broadly applicable** | Not tied to one specific codebase |
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| **Complex solution** | Multi-step fix that's easy to forget |
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| **User-flagged** | "Save this as a skill", "I want to reuse this" |
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## Workflow
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### Step 1: Identify the pattern
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Read the user's description. Search auto-memory for related entries:
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```bash
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MEMORY_DIR="$HOME/.claude/projects/$(pwd | sed 's|/|%2F|g; s|%2F|/|; s|^/||')/memory"
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grep -rni "<keywords>" "$MEMORY_DIR/"
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```
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If found in auto-memory, use those entries as source material. If not, use the user's description directly.
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### Step 2: Determine skill scope
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Ask (max 2 questions):
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- "What problem does this solve?" (if not clear)
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- "Should this include code examples?" (if applicable)
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### Step 3: Generate skill name
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Rules for naming:
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- Lowercase, hyphens between words
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- Descriptive but concise (2-4 words)
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- Examples: `docker-m1-fixes`, `api-timeout-patterns`, `pnpm-workspace-setup`
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### Step 4: Create the skill files
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**Spawn the `skill-extractor` agent** for the actual file generation.
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The agent creates:
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```
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<skill-name>/
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├── SKILL.md # Main skill file with frontmatter
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├── README.md # Human-readable overview
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└── reference/ # (optional) Supporting documentation
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└── examples.md # Concrete examples and edge cases
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```
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### Step 5: SKILL.md structure
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The generated SKILL.md must follow this format:
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```markdown
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---
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name: "skill-name"
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description: "<one-line description>. Use when: <trigger conditions>."
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---
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# <Skill Title>
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> One-line summary of what this skill solves.
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## Quick Reference
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| Problem | Solution |
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|---------|----------|
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| {{problem 1}} | {{solution 1}} |
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| {{problem 2}} | {{solution 2}} |
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## The Problem
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{{2-3 sentences explaining what goes wrong and why it's non-obvious.}}
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## Solutions
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### Option 1: {{Name}} (Recommended)
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{{Step-by-step with code examples.}}
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### Option 2: {{Alternative}}
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{{For when Option 1 doesn't apply.}}
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## Trade-offs
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| Approach | Pros | Cons |
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|----------|------|------|
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| Option 1 | {{pros}} | {{cons}} |
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| Option 2 | {{pros}} | {{cons}} |
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## Edge Cases
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- {{edge case 1 and how to handle it}}
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- {{edge case 2 and how to handle it}}
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```
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### Step 6: Quality gates
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Before finalizing, verify:
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- [ ] SKILL.md has valid YAML frontmatter with `name` and `description`
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- [ ] `name` matches the folder name (lowercase, hyphens)
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- [ ] Description includes "Use when:" trigger conditions
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- [ ] Solutions are self-contained (no external context needed)
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- [ ] Code examples are complete and copy-pasteable
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- [ ] No project-specific hardcoded values (paths, URLs, credentials)
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- [ ] No unnecessary dependencies
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### Step 7: Report
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```
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✅ Skill extracted: {{skill-name}}
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Files created:
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{{path}}/SKILL.md ({{lines}} lines)
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{{path}}/README.md ({{lines}} lines)
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{{path}}/reference/examples.md ({{lines}} lines)
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Install: /plugin install (copy to your skills directory)
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Publish: clawhub publish {{path}}
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Source: MEMORY.md entries at lines {{n, m, ...}} (retained — the skill is portable, the memory is project-specific)
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```
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## Examples
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### Extracting a debugging pattern
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```
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/si:extract "Fix for Docker builds failing on Apple Silicon with platform mismatch"
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```
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Creates `docker-m1-fixes/SKILL.md` with:
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- The platform mismatch error message
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- Three solutions (build flag, Dockerfile, docker-compose)
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- Trade-offs table
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- Performance note about Rosetta 2 emulation
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### Extracting a workflow pattern
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```
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/si:extract "Always regenerate TypeScript API client after modifying OpenAPI spec"
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```
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Creates `api-client-regen/SKILL.md` with:
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- Why manual regen is needed
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- The exact command sequence
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- CI integration snippet
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- Common failure modes
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## Tips
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- Extract patterns that would save time in a *different* project
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- Keep skills focused — one problem per skill
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- Include the error messages people would search for
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- Test the skill by reading it without the original context — does it make sense?
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