Extract
Turn a proven pattern or debugging solution into a standalone reusable skill with SKILL.md, reference docs, and examples.
$ npx promptcreek add extractAuto-detects your installed agents and installs the skill to each one.
What This Skill Does
The /si:extract skill transforms recurring patterns or debugging solutions into standalone, portable skills. It identifies patterns, determines the skill scope, generates a skill name, and creates the necessary skill files. This skill enables users to reuse proven solutions across multiple projects, improving efficiency and consistency.
When to Use
- Transform a recurring pattern into a reusable skill.
- Extract a debugging solution for future use.
- Create a skill from a pattern found in auto-memory.
- Reuse proven solutions across multiple projects.
- Quickly generate skill files from a pattern description.
- Preview skill creation without generating files.
Key Features
Installation
$ npx promptcreek add extractAuto-detects your installed agents (Claude Code, Cursor, Codex, etc.) and installs the skill to each one.
View Full Skill Content
/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:
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:
---
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
nameanddescription - [ ]
namematches 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?
Supported Agents
Attribution
Details
- License
- MIT
- Source
- seeded
- Published
- 3/17/2026
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