extract

alirezarezvani/claude-skills · updated Apr 8, 2026

MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.

$npx skills add https://github.com/alirezarezvani/claude-skills --skill extract
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summary

Transforms a recurring pattern or debugging solution into a standalone, portable skill that can be installed in any project.

skill.md

/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?
how to use extract

How to use extract on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add extract
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/alirezarezvani/claude-skills --skill extract

The skills CLI fetches extract from GitHub repository alirezarezvani/claude-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/extract

Reload or restart Cursor to activate extract. Access the skill through slash commands (e.g., /extract) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.546 reviews
  • Kiara Rao· Dec 24, 2024

    Useful defaults in extract — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Charlotte Zhang· Dec 20, 2024

    extract is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Layla Mehta· Dec 16, 2024

    We added extract from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Charlotte Abbas· Dec 12, 2024

    I recommend extract for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Chen Torres· Dec 4, 2024

    extract reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Liam Gonzalez· Nov 19, 2024

    We added extract from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Zaid Garcia· Nov 15, 2024

    I recommend extract for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Chen Menon· Nov 11, 2024

    extract fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Kabir Ramirez· Nov 3, 2024

    Useful defaults in extract — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Henry Liu· Oct 22, 2024

    Registry listing for extract matched our evaluation — installs cleanly and behaves as described in the markdown.

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