udayanwalvekar

udayanwalvekar / clearshot

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📸 Structured screenshot intelligence for AI coding tools. Give your AI X-ray vision for UI screenshots.

61
8
100% credibility
Found Mar 24, 2026 at 61 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Shell
AI Summary

Clearshot enhances AI coding tools by automatically analyzing UI screenshots with precise structural details like colors, spacing, and layouts to help accurately recreate or critique interfaces.

How It Works

1
🔍 Discover Clearshot

While chatting with your AI coding helper about rebuilding a user interface from a screenshot, you learn about Clearshot to make it smarter at spotting details.

2
📥 Get Clearshot

Run a simple one-line command to bring Clearshot to your AI helper, and it sets itself up in your tools folder.

3
❓ Quick Setup Choices

The first time it runs, Clearshot asks two easy questions about updates and sharing usage stats so you can choose what feels right.

4
🖼️ Share a Screenshot

Paste a picture of a user interface into your AI chat when asking to rebuild or check something.

5
✨ Magic Analysis Happens

Clearshot quietly jumps in, scans the screenshot, and pulls out exact colors, spaces, button styles, and layouts to share with the AI.

✅ Perfect UI Recreation

Your AI now sees every tiny detail like exact hex colors and pixel gaps, rebuilding the interface spot-on or giving spot-on feedback.

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AI-Generated Review

What is clearshot?

Clearshot brings structured screenshot intelligence to AI coding tools like Claude, turning vague UI descriptions into precise specs with hex colors like #3B82F6, 8px gaps, and element breakdowns. Paste a screenshot into a Claude chat, and it auto-runs local analysis across three levels—spatial maps, design systems, and blueprints—only on relevant UI images. Install via npx clearshot, Claude skills add, or a curl bash script; it's Shell-based with opt-in telemetry to a Convex backend.

Why is it gaining traction?

It delivers github llm structured output that AI vision models lack, extracting CSS patterns and responsive details without cloud uploads, unlike generic screenshot parsers. Developers hook on the auto-activation for frontend tasks, skipping manual prompts, and its research roots in Microsoft Omniparser and ScreenAI boost trust. Privacy focus—no images leave your machine—beats alternatives pushing structured rag github flows.

Who should use this?

Frontend devs cloning UIs from Figma screenshots in Claude sessions, or prototyping clearshot imaging solutions with pixel-perfect recreations. Suited for indie hackers building structured app github projects, or teams critiquing designs via chat. Skip if you're not in Claude workflows or need browser-based tools.

Verdict

Early stage at 61 stars and 1.0% credibility score, but strong README docs and MIT license make it worth a quick npx test for Claude frontend users. Maturity lags in adoption, yet local precision fills a real gap—try before betting production workflows.

(198 words)

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