extract

pbakaus/impeccable · 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/pbakaus/impeccable --skill extract
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summary

Identify and extract reusable components, design tokens, and patterns into a cohesive design system.

  • Analyzes target areas to find repeated UI patterns, hard-coded values, and inconsistent variations worth systematizing
  • Guides extraction planning including component APIs, token hierarchies, naming conventions, and migration strategies
  • Emphasizes incremental growth: extract only patterns used 3+ times or with clear reuse potential, avoiding over-generalization
  • Includes accessibilit
skill.md

Identify reusable patterns, components, and design tokens, then extract and consolidate them into the design system for systematic reuse.

Discover

Analyze the target area to identify extraction opportunities:

  1. Find the design system: Locate your design system, component library, or shared UI directory (grep for "design system", "ui", "components", etc.). Understand its structure:

    • Component organization and naming conventions
    • Design token structure (if any)
    • Documentation patterns
    • Import/export conventions

    CRITICAL: If no design system exists, ask before creating one. Understand the preferred location and structure first.

  2. Identify patterns: Look for:

    • Repeated components: Similar UI patterns used multiple times (buttons, cards, inputs, etc.)
    • Hard-coded values: Colors, spacing, typography, shadows that should be tokens
    • Inconsistent variations: Multiple implementations of the same concept (3 different button styles)
    • Reusable patterns: Layout patterns, composition patterns, interaction patterns worth systematizing
  3. Assess value: Not everything should be extracted. Consider:

    • Is this used 3+ times, or likely to be reused?
    • Would systematizing this improve consistency?
    • Is this a general pattern or context-specific?
    • What's the maintenance cost vs benefit?

Plan Extraction

Create a systematic extraction plan:

  • Components to extract: Which UI elements become reusable components?
  • Tokens to create: Which hard-coded values become design tokens?
  • Variants to support: What variations does each component need?
  • Naming conventions: Component names, token names, prop names that match existing patterns
  • Migration path: How to refactor existing uses to consume the new shared versions

IMPORTANT: Design systems grow incrementally. Extract what's clearly reusable now, not everything that might someday be reusable.

Extract & Enrich

Build improved, reusable versions:

  • Components: Create well-designed components with:

    • Clear props API with sensible defaults
    • Proper variants for different use cases
    • Accessibility built in (ARIA, keyboard navigation, focus management)
    • Documentation and usage examples
  • Design tokens: Create tokens with:

    • Clear naming (primitive vs semantic)
    • Proper hierarchy and organization
    • Documentation of when to use each token
  • Patterns: Document patterns with:

    • When to use this pattern
    • Code examples
    • Variations and combinations

NEVER:

  • Extract one-off, context-specific implementations without generalization
  • Create components so generic they're useless
  • Extract without considering existing design system conventions
  • Skip proper TypeScript types or prop documentation
  • Create tokens for every single value (tokens should have semantic meaning)

Migrate

Replace existing uses with the new shared versions:

  • Find all instances: Search for the patterns you've extracted
  • Replace systematically: Update each use to consume the shared version
  • Test thoroughly: Ensure visual and functional parity
  • Delete dead code: Remove the old implementations

Document

Update design system documentation:

  • Add new components to the component library
  • Document token usage and values
  • Add examples and guidelines
  • Update any Storybook or component catalog

Remember: A good design system is a living system. Extract patterns as they emerge, enrich them thoughtfully, and maintain them consistently.

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/pbakaus/impeccable --skill extract

The skills CLI fetches extract from GitHub repository pbakaus/impeccable 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)
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general reviews

Ratings

4.625 reviews
  • Noor Huang· Dec 20, 2024

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

  • Dhruvi Jain· Dec 8, 2024

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

  • Oshnikdeep· Nov 27, 2024

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

  • Noor Jain· Nov 19, 2024

    extract has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Charlotte Sethi· Nov 11, 2024

    Solid pick for teams standardizing on skills: extract is focused, and the summary matches what you get after install.

  • Ganesh Mohane· Oct 18, 2024

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

  • Sakura Martin· Oct 10, 2024

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

  • Noor Anderson· Oct 2, 2024

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

  • Rahul Santra· Sep 25, 2024

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

  • Diego Smith· Sep 25, 2024

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

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