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Extract

Turn a proven pattern or debugging solution into a standalone reusable skill with SKILL.md, reference docs, and examples.

$ npx promptcreek add extract

Auto-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

Transforms patterns into standalone skills.
Identifies patterns from user descriptions or auto-memory.
Determines skill scope through interactive questions.
Generates skill name based on descriptive keywords.
Creates SKILL.md, README.md, and reference files.
Uses the `skill-extractor` agent for file generation.

Installation

Run in your project directory:
$ npx promptcreek add extract

Auto-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 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?
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Supported Agents

Claude CodeCursorCodexGemini CLIAiderWindsurfOpenClaw

Details

License
MIT
Source
seeded
Published
3/17/2026

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