figma▌
tech-leads-club/agent-skills · updated May 23, 2026
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Use the Figma MCP server to fetch design context, screenshots, variables, and assets from Figma, and to translate Figma nodes into production code. Use when a task involves Figma URLs, node IDs, design-to-code implementation, or Figma MCP setup and troubleshooting. Covers general Figma data fetching and exploration. Do NOT use when the goal is specifically pixel-perfect code implementation from a Figma design (use figma-implement-design instead).
| name | figma |
| description | Use the Figma MCP server to fetch design context, screenshots, variables, and assets from Figma, and to translate Figma nodes into production code. Use when a task involves Figma URLs, node IDs, design-to-code implementation, or Figma MCP setup and troubleshooting. Covers general Figma data fetching and exploration. Do NOT use when the goal is specifically pixel-perfect code implementation from a Figma design (use figma-implement-design instead). |
| metadata | author: github.com/openai/skills version: '1.0.0' |
Figma MCP
Use the Figma MCP server for Figma-driven implementation. For setup and debugging details (env vars, config, verification), see references/figma-mcp-config.md.
Figma MCP Integration Rules
These rules define how to translate Figma inputs into code for this project and must be followed for every Figma-driven change.
Required flow (do not skip)
- Run get_design_context first to fetch the structured representation for the exact node(s).
- If the response is too large or truncated, run get_metadata to get the high-level node map and then re-fetch only the required node(s) with get_design_context.
- Run get_screenshot for a visual reference of the node variant being implemented.
- Only after you have both get_design_context and get_screenshot, download any assets needed and start implementation.
- Translate the output (usually React + Tailwind) into this project's conventions, styles and framework. Reuse the project's color tokens, components, and typography wherever possible.
- Validate against Figma for 1:1 look and behavior before marking complete.
Implementation rules
- Treat the Figma MCP output (React + Tailwind) as a representation of design and behavior, not as final code style.
- Replace Tailwind utility classes with the project's preferred utilities/design-system tokens when applicable.
- Reuse existing components (e.g., buttons, inputs, typography, icon wrappers) instead of duplicating functionality.
- Use the project's color system, typography scale, and spacing tokens consistently.
- Respect existing routing, state management, and data-fetch patterns already adopted in the repo.
- Strive for 1:1 visual parity with the Figma design. When conflicts arise, prefer design-system tokens and adjust spacing or sizes minimally to match visuals.
- Validate the final UI against the Figma screenshot for both look and behavior.
Asset handling
- The Figma MCP Server provides an assets endpoint which can serve image and SVG assets.
- IMPORTANT: If the Figma MCP Server returns a localhost source for an image or an SVG, use that image or SVG source directly.
- IMPORTANT: DO NOT import/add new icon packages, all the assets should be in the Figma payload.
- IMPORTANT: do NOT use or create placeholders if a localhost source is provided.
Link-based prompting
- The server is link-based: copy the Figma frame/layer link and give that URL to the MCP client when asking for implementation help.
- The client cannot browse the URL but extracts the node ID from the link; always ensure the link points to the exact node/variant you want.
References
references/figma-mcp-config.md— setup, verification, troubleshooting, and link-based usage reminders.references/figma-tools-and-prompts.md— tool catalog and prompt patterns for selecting frameworks/components and fetching metadata.
How to use figma on Cursor
AI-first code editor with Composer
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 figma
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches figma from GitHub repository tech-leads-club/agent-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate figma. Access the skill through slash commands (e.g., /figma) 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
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.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 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▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.6★★★★★44 reviews- ★★★★★Michael Haddad· Dec 28, 2024
Useful defaults in figma — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Anika Singh· Dec 24, 2024
Keeps context tight: figma is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Ganesh Mohane· Dec 12, 2024
I recommend figma for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Hassan Rahman· Dec 8, 2024
We added figma from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Tariq Wang· Nov 19, 2024
figma is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Tariq Li· Nov 7, 2024
We added figma from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Rahul Santra· Nov 3, 2024
figma fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Zara Robinson· Oct 26, 2024
Keeps context tight: figma is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Pratham Ware· Oct 22, 2024
figma has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Tariq Patel· Oct 10, 2024
figma reduced setup friction for our internal harness; good balance of opinion and flexibility.
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