screenshot-feature-extractor
About
This Claude Skill analyzes product screenshots through a multi-agent pipeline to extract feature lists and generate development task checklists. It's designed for competitive analysis, creating PRDs from UI designs, and batch processing multiple app screens. The core principle is to describe what to build (features/interactions) without prescribing specific technical implementation.
Quick Install
Claude Code
Recommended/plugin add https://github.com/davila7/claude-code-templatesgit clone https://github.com/davila7/claude-code-templates.git ~/.claude/skills/screenshot-feature-extractorCopy and paste this command in Claude Code to install this skill
Documentation
Screenshot Analyzer (Multi-Agent)
Extract product features from UI screenshots using a coordinated multi-agent analysis pipeline.
Core principle: Describe WHAT to build (features/interactions), NOT HOW (no tech stack).
Multi-Agent Architecture
This skill orchestrates 5 specialized agents for comprehensive analysis:
┌─────────────────┐
│ Coordinator │
│ (this skill) │
└────────┬────────┘
│
┌───────────────────┼───────────────────┐
│ │ │
▼ ▼ ▼
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ UI Analyzer │ │ Interaction │ │ Business │
│ (parallel) │ │ Analyzer │ │ Analyzer │
│ │ │ (parallel) │ │ (parallel) │
└────────┬────────┘ └────────┬────────┘ └────────┬────────┘
│ │ │
└───────────────────┼───────────────────┘
▼
┌─────────────────┐
│ Synthesizer │
│ (sequential) │
└────────┬────────┘
│
▼
┌─────────────────┐
│ Reviewer │
│ (sequential) │
└─────────────────┘
Process
Phase 1: Screenshot Collection
Gather all screenshots to analyze:
- Read the screenshot file(s) provided by the user
- For each screenshot, note the file path and any context provided
- If multiple screenshots, determine if they are from the same product
Phase 2: Parallel Analysis
Launch THREE Task agents IN PARALLEL for each screenshot:
Agent 1: screenshot-ui-analyzer
Analyze this screenshot for UI components, layout structure, and design patterns.
Screenshot: [file path]
Return your analysis as JSON.
Agent 2: screenshot-interaction-analyzer
Analyze this screenshot for user interactions, navigation flows, and state transitions.
Screenshot: [file path]
Return your analysis as JSON.
Agent 3: screenshot-business-analyzer
Analyze this screenshot for business functions, data entities, and domain logic.
Screenshot: [file path]
Return your analysis as JSON.
IMPORTANT: Use the Task tool with THREE parallel calls in a single message to maximize efficiency.
Phase 3: Synthesis
After all parallel analyses complete, launch the synthesizer agent:
Agent 4: screenshot-synthesizer
Synthesize these analysis results into a unified development task list.
UI Analysis:
[paste UI analyzer result]
Interaction Analysis:
[paste Interaction analyzer result]
Business Analysis:
[paste Business analyzer result]
Product Name: [product name]
Output file: docs/plans/YYYY-MM-DD-<product>-features.md
Phase 4: Review
Launch the reviewer agent to validate the output:
Agent 5: screenshot-reviewer
Review this task list for completeness and quality.
Original screenshot(s): [file paths]
Task list: [synthesized output]
If issues found, provide corrections.
Phase 5: Output
- Write final task list to
docs/plans/YYYY-MM-DD-<product>-features.md - Use format from references/output-format.md
- Present summary to user
Key Guidelines
- Use
- [ ]checkbox format for all tasks - Break features into small, executable subtasks
- Focus on user interactions, not implementation details
- For multiple screenshots: deduplicate features across all screens
- For competitive analysis: highlight unique features and gaps
Benefits of Multi-Agent Approach
- Thoroughness - Three specialized perspectives catch more details
- Speed - Parallel analysis reduces total time
- Quality - Synthesis + Review ensures coherent, complete output
- Specialization - Each agent focuses on its domain expertise
GitHub Repository
Related Skills
langchain
MetaLangChain is a framework for building LLM applications using agents, chains, and RAG pipelines. It supports multiple LLM providers, offers 500+ integrations, and includes features like tool calling and memory management. Use it for rapid prototyping and deploying production systems like chatbots, autonomous agents, and question-answering services.
Algorithmic Art Generation
MetaThis skill helps developers create algorithmic art using p5.js, focusing on generative art, computational aesthetics, and interactive visualizations. It automatically activates for topics like "generative art" or "p5.js visualization" and guides you through creating unique algorithms with features like seeded randomness, flow fields, and particle systems. Use it when you need to build reproducible, code-driven artistic patterns.
webapp-testing
TestingThis Claude Skill provides a Playwright-based toolkit for testing local web applications through Python scripts. It enables frontend verification, UI debugging, screenshot capture, and log viewing while managing server lifecycles. Use it for browser automation tasks but run scripts directly rather than reading their source code to avoid context pollution.
requesting-code-review
DesignThis skill dispatches a code-reviewer subagent to analyze code changes against requirements before proceeding. It should be used after completing tasks, implementing major features, or before merging to main. The review helps catch issues early by comparing the current implementation with the original plan.
