figma▌
davila7/claude-code-templates · updated Apr 8, 2026
MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.
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
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 davila7/claude-code-templates 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▌
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.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 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▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.6★★★★★47 reviews- ★★★★★Maya Sethi· Dec 20, 2024
figma fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Jin Yang· Dec 16, 2024
figma is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Min Thompson· Dec 16, 2024
Registry listing for figma matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Shikha Mishra· Dec 8, 2024
We added figma from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Yash Thakker· Nov 27, 2024
figma fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Maya Sharma· Nov 11, 2024
We added figma from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Aanya Shah· Nov 7, 2024
Solid pick for teams standardizing on skills: figma is focused, and the summary matches what you get after install.
- ★★★★★Min Nasser· Nov 7, 2024
figma reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Aanya Tandon· Oct 26, 2024
We added figma from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Dhruvi Jain· Oct 18, 2024
figma is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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