screenshot-feature-extractor

davila7/claude-code-templates · updated Apr 8, 2026

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$npx skills add https://github.com/davila7/claude-code-templates --skill screenshot-feature-extractor
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summary

Extract product features from UI screenshots using a coordinated multi-agent analysis pipeline.

skill.md

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:

  1. Read the screenshot file(s) provided by the user
  2. For each screenshot, note the file path and any context provided
  3. 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

  1. Write final task list to docs/plans/YYYY-MM-DD-<product>-features.md
  2. Use format from references/output-format.md
  3. 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

  1. Thoroughness - Three specialized perspectives catch more details
  2. Speed - Parallel analysis reduces total time
  3. Quality - Synthesis + Review ensures coherent, complete output
  4. Specialization - Each agent focuses on its domain expertise
how to use screenshot-feature-extractor

How to use screenshot-feature-extractor on Cursor

AI-first code editor with Composer

1

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 screenshot-feature-extractor
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/davila7/claude-code-templates --skill screenshot-feature-extractor

The skills CLI fetches screenshot-feature-extractor from GitHub repository davila7/claude-code-templates and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/screenshot-feature-extractor

Reload or restart Cursor to activate screenshot-feature-extractor. Access the skill through slash commands (e.g., /screenshot-feature-extractor) 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

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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. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 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

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.848 reviews
  • Pratham Ware· Dec 16, 2024

    Registry listing for screenshot-feature-extractor matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Camila Mehta· Dec 12, 2024

    screenshot-feature-extractor reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Xiao Kapoor· Dec 8, 2024

    Useful defaults in screenshot-feature-extractor — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Ren Farah· Dec 8, 2024

    screenshot-feature-extractor is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Ren Nasser· Nov 27, 2024

    Solid pick for teams standardizing on skills: screenshot-feature-extractor is focused, and the summary matches what you get after install.

  • Min White· Nov 27, 2024

    screenshot-feature-extractor has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Maya Lopez· Nov 27, 2024

    Keeps context tight: screenshot-feature-extractor is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Yash Thakker· Nov 7, 2024

    screenshot-feature-extractor reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Charlotte Abebe· Nov 3, 2024

    Registry listing for screenshot-feature-extractor matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Dhruvi Jain· Oct 26, 2024

    I recommend screenshot-feature-extractor for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

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