Transforms a recurring pattern or debugging solution into a standalone, portable skill that can be installed in any project.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionextractExecute the skills CLI command in your project's root directory to begin installation:
Fetches extract from alirezarezvani/claude-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate extract. Access via /extract in your agent's command palette.
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 environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
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
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
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Transforms a recurring pattern or debugging solution into a standalone, portable skill that can be installed in any project.
/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
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" |
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.
Ask (max 2 questions):
Rules for naming:
docker-m1-fixes, api-timeout-patterns, pnpm-workspace-setupSpawn 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
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}}
Before finalizing, verify:
name and descriptionname matches the folder name (lowercase, hyphens)✅ 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)
/si:extract "Fix for Docker builds failing on Apple Silicon with platform mismatch"
Creates docker-m1-fixes/SKILL.md with:
/si:extract "Always regenerate TypeScript API client after modifying OpenAPI spec"
Creates api-client-regen/SKILL.md with:
Make data-driven prioritization decisions faster
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
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
alirezarezvani/claude-skills
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
Useful defaults in extract — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
extract is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
We added extract from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
I recommend extract for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
extract reduced setup friction for our internal harness; good balance of opinion and flexibility.
We added extract from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
I recommend extract for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
extract fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Useful defaults in extract — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Registry listing for extract matched our evaluation — installs cleanly and behaves as described in the markdown.
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