An AI skill is a structured system prompt or set of custom instructions that gives AI assistants like ChatGPT, Claude, and Gemini specialized capabilities. It includes a clear role definition, specific knowledge areas, step-by-step processes, and quality criteria that help AI perform specific professional tasks.

How does Skill This work?

Simply describe your professional expertise in plain text. Our free AI skill generator analyzes your input and transforms it into a structured system prompt. The skill is then graded on clarity, specificity, and usefulness. You can share your skill via a unique URL or copy it to use as custom instructions with any AI assistant like ChatGPT, Claude, or Gemini.

Is Skill This free to use?

Yes, Skill This is completely free. No account or payment is required. You can create unlimited skills and share them with anyone.

How is my skill graded?

Skills are graded on a scale from A+ to F (0-100 points). The grade reflects how clear, specific, and actionable your skill is. Higher grades indicate skills that AI assistants can use more effectively.

How do I use my skill with an AI assistant?

Copy your generated skill and paste it into the system prompt or custom instructions of any AI assistant. For ChatGPT, use Personalization > Custom Instructions. For Claude, add it to a Project's instructions. For Cursor or other IDEs, save it as a .cursorrules or CLAUDE.md file. The AI will then adopt that skill and perform tasks according to your expertise.

Generating Figma Designs with AI - AI Skill Graded A- | Skill This

Quick Start13 / 15

Fastest path to a working prototype from a text prompt:

Open Figma Make (figma.com/make) — no plugin install needed.
Enter a plain-language prompt describing the screen/flow, e.g.:

"Build a mobile checkout flow for a language-learning app with a cart summary, payment method selector, and confirmation screen."
Review generated layout, iterate with follow-up prompts ("make the CTA sticky", "add a dark mode variant").
Export code or hand off to canvas for manual refinement.

For teams with an existing design system, skip generic generators and use: Claude Code + Figma MCP + Code Connect + Storybook (see Workflow below) — this respects existing components instead of inventing new ones.

Recommendation▾

Add concrete before/after or actual code output examples (e.g., sample MCP config, Code Connect mapping snippet) rather than only prose descriptions of outcomes

Workflow14 / 15

Choose a path based on the goal, then follow the matching checklist.

Path A: Quick prototype from scratch (no existing design system)

Use Figma Make or a third-party generator (UX Pilot, Banani, Codia) for text/image → editable layout
Iterate via follow-up prompts rather than manual redesign
Export as Figma file, HTML/CSS, or framework code (React/Tailwind) depending on tool
Manually review accessibility, spacing consistency, and responsive behavior — generators are inconsistent here

Path B: Working within an existing design system

Do NOT rely on generic prompt-to-UI tools — they ignore your component library
Set up Figma MCP server to expose your file's components/tokens to an AI coding agent
Use Claude Code (or similar) with Code Connect mapping Figma components to code components
Reference Storybook as the source of truth for component variants
Generate new screens by composing existing components, not new ones
Validate output against design tokens (spacing, color, typography) before merging

Path C: In-canvas assistance (layout automation, handoff, cleanup)

Use Figma's native canvas-integrated AI agent for: renaming layers, generating redlines/specs, adapting one layout across web/mobile/tablet, summarizing feedback
Treat native agent as a productivity tool for busywork, not a creative director — user reports consistently note slow generation (10-30s) and weak design judgment on original concepts
For dev handoff specifically, consider FigSpecs-style plugins that generate token audits, Tailwind theme blocks, and tickets from existing components

Path D: Design-to-code conversion

For full app scaffolding with working navigation: WaveMaker AI or Anima
For component-level code export (React/Vue/etc.): Builder.io or Anima
Always spot-check generated code against actual design tokens — AI conversions frequently drift from source styles on nested/conditional components

Recommendation▾

Include a decision table/flowchart summarizing which Path (A-D) to pick based on quick criteria, to speed up navigation

Examples15 / 20

Example 1: Marketing landing page, no design system constraints Input: "Generate a SaaS landing page with hero, 3-tier pricing table, and testimonial carousel" Output: Use Figma Make or UX Pilot directly. Generate, iterate 2-3 times on copy/layout via prompt, export to code if needed. No MCP/Storybook setup required — overkill for greenfield marketing pages.

Example 2: New feature screen inside a mature product with a component library Input: "Add a new settings screen that matches our existing design system" Output: Skip prompt-to-UI generators entirely. Use Claude Code + Figma MCP + Code Connect to pull existing components (buttons, list items, toggles) and compose the new screen, ensuring token/spacing consistency automatically.

Example 3: Developer handoff for an already-designed screen Input: "Generate dev specs and Tailwind tokens for this dashboard frame" Output: Use FigSpecs or the native Figma agent's handoff mode to auto-generate redlines, spacing docs, and CSS custom properties rather than manually annotating.

Recommendation▾

Some tool claims (e.g., '10-30s generation time', specific plugin names like FigSpecs) read as unverifiable specifics — consider flagging these as illustrative rather than presenting as fact

Best Practices

Match tool to task: native agent for in-canvas busywork, dedicated generators (Figma Make, UX Pilot) for greenfield concepts, MCP-based agent workflows for design-system-constrained work.
Always verify AI-generated designs against accessibility and spacing standards — this is the most commonly cited weakness across all tools.
For design systems, invest in Code Connect + MCP setup once — it pays off across every future generation task versus repeatedly fighting generic tools that ignore your components.
Use short iterative prompts rather than one giant prompt — all tools perform better with incremental refinement.
Cross-check exported code tokens against source Figma styles before shipping; drift is common, especially on nested components.

Common Pitfalls

Don't expect native Figma AI to produce polished, original creative concepts — it's optimized for automation (renaming, redlines, cross-viewport adaptation), not design ideation.
Don't use generic text-to-UI generators when a real design system exists — they'll produce off-brand, inconsistent components requiring manual reconciliation.
Don't skip manual review of generated layouts for accessibility — none of the current tools reliably handle this.
Don't assume design-to-code exports are production-ready — treat them as a first draft requiring token/style audit.
Don't rely solely on the native agent's inconsistency evaluations — reviews note they require manual verification and struggle with historical/requirement comparisons.