AI skill for structured UI/UX research using Mobbin MCP. Works with Claude Code, Cursor, Gemini CLI, Lovable, and any MCP-compatible agent.
Overview
Turn Mobbin's 300K+ screen library into structured UI research. Give your agent a research goal — it searches Mobbin for screens and flows, visually analyzes the results, and returns pattern clusters, design system components, or competitive comparisons. Works with Claude Code, Cursor, Gemini CLI, Lovable, and any MCP-compatible agent. - Translates abstract UX goals into concrete Mobbin search queries - Searches both and (onboarding, checkout, etc.) - Visually analyzes returned screenshots (layout, hierarchy, components, color) - Clusters findings into recurring UI patterns with linked references - Derives research queries from PRDs and product briefs automatically - Surfaces atomic design system primitives when asked about components - Researches a target UI — decomposes a screenshot, PRD, Figma file, or existing screen, finds the best real-world reference per component, and writes an implementation brief to build from - Supports three output modes: research summary, competitive...
README
Mobbin UI Research Skill
Turn Mobbin’s 300K+ screen library into structured UI research. Give your agent a research goal — it searches Mobbin for screens and flows, visually analyzes the results, and returns pattern clusters, design system components, or competitive comparisons.
Works with Claude Code, Cursor, Gemini CLI, Lovable, and any MCP-compatible agent.
What it does
- Translates abstract UX goals into concrete Mobbin search queries
- Searches both individual screens and multi-step user flows (onboarding, checkout, etc.)
- Visually analyzes returned screenshots (layout, hierarchy, components, color)
- Clusters findings into recurring UI patterns with linked references
- Derives research queries from PRDs and product briefs automatically
- Surfaces atomic design system primitives when asked about components
- Researches a target UI component by component — decomposes a screenshot, PRD, Figma file, or existing screen, finds the best real-world reference per component, and writes an implementation brief to build from
- Supports three output modes: research summary, competitive comparison, product decision log
Install
Requires Mobbin MCP to be connected first. Copy and Paste in Terminal to install skill
npx skills add https://github.com/ddruids/mobbin-skill
Usage
Once installed, the skill activates when you ask about UI research, screen patterns, or design system components:
"Research how fintech apps handle onboarding"
"Compare how crypto wallets display transaction confirmation"
"What are common components for a SaaS dashboard design system?"
"How do AI apps handle empty states on iOS?"
"Here's my PRD — research the key screens and flows"
"Here's a screenshot of the screen I'm building — research each component and pick the best reference so you can build it"
The skill automatically:
- Resolves the target platform (iOS or web)
- Picks the right tool mix —
search_screensfor specific screens,search_flowsfor multi-step journeys - Generates 3-7 concrete search queries (or derives them from your PRD)
- Analyzes the returned screenshots and flow sequences
- Synthesizes findings into structured output with Mobbin URLs
What’s inside
| File | Purpose |
|---|---|
SKILL.md |
Core skill — screen/flow tool selection, query construction, PRD-driven research, analysis framework, output formats, design system component mode, component implementation mode |
references/query-patterns.md |
Query formula, platform-specific guidance, 20+ screen query examples, flow query examples by category |
references/synthesis-framework.md |
8 analysis lenses, 3 output templates (research summary, competitive comparison, product decision log) |
references/component-implementation.md |
Build-from-a-target-UI mode — input adapters (screenshot/PRD/Figma/code), the “best visual” selection rubric, and the per-component implementation brief template |
License
MIT
Recommended Tools
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Install
npx skillfish add ddruids/mobbin-skill