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react:components▌

google-labs-code/stitch-skills · updated Apr 8, 2026

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$npx skills add https://github.com/google-labs-code/stitch-skills --skill react:components
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summary

You are a frontend engineer focused on transforming designs into clean React code. You follow a modular approach and use automated tools to ensure code quality.

skill.md

Stitch to React Components

You are a frontend engineer focused on transforming designs into clean React code. You follow a modular approach and use automated tools to ensure code quality.

Retrieval and networking

  1. Namespace discovery: Run list_tools to find the Stitch MCP prefix. Use this prefix (e.g., stitch:) for all subsequent calls.
  2. Metadata fetch: Call [prefix]:get_screen to retrieve the design JSON.
  3. Check for existing designs: Before downloading, check if .stitch/designs/{page}.html and .stitch/designs/{page}.png already exist:
    • If files exist: Ask the user whether to refresh the designs from the Stitch project using the MCP, or reuse the existing local files. Only re-download if the user confirms.
    • If files do not exist: Proceed to step 4.
  4. High-reliability download: Internal AI fetch tools can fail on Google Cloud Storage domains.
    • HTML: bash scripts/fetch-stitch.sh "[htmlCode.downloadUrl]" ".stitch/designs/{page}.html"
    • Screenshot: Append =w{width} to the screenshot URL first, where {width} is the width value from the screen metadata (Google CDN serves low-res thumbnails by default). Then run: bash scripts/fetch-stitch.sh "[screenshot.downloadUrl]=w{width}" ".stitch/designs/{page}.png"
    • This script handles the necessary redirects and security handshakes.
  5. Visual audit: Review the downloaded screenshot (.stitch/designs/{page}.png) to confirm design intent and layout details.

Architectural rules

  • Modular components: Break the design into independent files. Avoid large, single-file outputs.
  • Logic isolation: Move event handlers and business logic into custom hooks in src/hooks/.
  • Data decoupling: Move all static text, image URLs, and lists into src/data/mockData.ts.
  • Type safety: Every component must include a Readonly TypeScript interface named [ComponentName]Props.
  • Project specific: Focus on the target project's needs and constraints. Leave Google license headers out of the generated React components.
  • Style mapping:
    • Extract the tailwind.config from the HTML <head>.
    • Sync these values with resources/style-guide.json.
    • Use theme-mapped Tailwind classes instead of arbitrary hex codes.

Execution steps

  1. Environment setup: If node_modules is missing, run npm install to enable the validation tools.
  2. Data layer: Create src/data/mockData.ts based on the design content.
  3. Component drafting: Use resources/component-template.tsx as a base. Find and replace all instances of StitchComponent with the actual name of the component you are creating.
  4. Application wiring: Update the project entry point (like App.tsx) to render the new components.
  5. Quality check:
    • Run npm run validate <file_path> for each component.
    • Verify the final output against the resources/architecture-checklist.md.
    • Start the dev server with npm run dev to verify the live result.

Troubleshooting

  • Fetch errors: Ensure the URL is quoted in the bash command to prevent shell errors.
  • Validation errors: Review the AST report and fix any missing interfaces or hardcoded styles.
how to use react:components

How to use react:components 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 react:components
2

Execute installation command

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

$npx skills add https://github.com/google-labs-code/stitch-skills --skill react:components

The skills CLI fetches react:components from GitHub repository google-labs-code/stitch-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/react:components

Reload or restart Cursor to activate react:components. Access the skill through slash commands (e.g., /react:components) 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.

Additional Resources

›View source code on GitHub›Skills CLI documentation›Learn more about Cursor›What are agent skills?

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases▌

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

✓

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

✓

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

✓

Improve work quality by 30-40% with less effort

Implementation Guide▌

Prerequisites

  • ›Claude Desktop or compatible AI client with skill support
  • ›Clear understanding of task or problem to solve
  • ›Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Installation Steps

  1. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 5.Integrate into regular workflow if valuable

Common Pitfalls

  • ⚠Expecting perfect results without iteration
  • ⚠Not providing enough context in prompts
  • ⚠Using skill for tasks outside its intended scope
  • ⚠Accepting outputs without review and validation

Best Practices▌

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • −Don't use without understanding skill limitations
  • −Don't skip validation of outputs
  • −Don't share sensitive information in prompts
  • −Don't expect skill to replace human judgment

💡 Pro Tips

  • ★Be specific about desired format and style
  • ★Ask for multiple options to choose from
  • ★Request explanations to understand reasoning
  • ★Combine AI efficiency with human expertise

When to Use This▌

✓ Use When

Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.

✗ Avoid When

Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.

Learning Path▌

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

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

Ratings

4.5★★★★★41 reviews
  • ★★★★★Isabella Harris· Dec 28, 2024

    react:components reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • ★★★★★Lucas Patel· Dec 16, 2024

    Keeps context tight: react:components is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • ★★★★★Camila Haddad· Dec 4, 2024

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

  • ★★★★★Soo Abbas· Nov 23, 2024

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

  • ★★★★★Isabella Martin· Nov 15, 2024

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

  • ★★★★★Ren Patel· Nov 7, 2024

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

  • ★★★★★Lucas Gupta· Oct 26, 2024

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

  • ★★★★★Soo Ramirez· Oct 14, 2024

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

  • ★★★★★Isabella Sanchez· Oct 6, 2024

    react:components reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • ★★★★★Mei Bansal· Sep 25, 2024

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

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