Dsadd4

Dsadd4 / AgentFigureGallery

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Drop-in scientific plotting skill for Claude Code, Codex, Cursor, and other coding agents.

huggingface.codatasetsdsadd4AgentFigureGallery agent-skill agent-tools ai-agent bioinformatics cell-style
33
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100% credibility
Found May 02, 2026 at 19 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

AgentFigureGallery is a curated visual reference library of scientific figures that helps AI coding agents produce publication-quality plots by incorporating human preferences.

How It Works

1
🔍 Discover the gallery

You find a collection of beautiful science charts from top journals to help your AI create stunning plots.

2
🚀 Set up in a minute

Run one easy command to download the gallery and get everything ready on your computer.

3
🤖 Connect your AI helper

Link the gallery to your favorite coding AI so it checks examples before drawing anything.

4
👀 Browse and pick favorites

Open a fun browser view of matching charts, like the ones you love, reject the rest, and build your taste profile.

5
📦 Save your selection

Bundle up your chosen examples into a handy package to hand off to the AI.

6
✨ AI draws pro figures

Watch your AI create publication-ready charts that match the style of Nature or Cell perfectly.

🎉 Perfect science visuals

Now your projects have gorgeous, professional plots every time, saving hours of tweaking.

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AI-Generated Review

What is AgentFigureGallery?

AgentFigureGallery is a Python tool that equips scientific plotting agents with a codex-ready visual reference memory of 16k+ publication-quality figures from Nature, Cell, and Science. It lets agents query plot types like embedding plots or heatmaps, displays candidates in a browser gallery for human-in-the-loop like/reject/select feedback, and exports selected references as bundles for precise plotting code generation. Developers get one-command installs via curl script, CLI queries like `agentfiguregallery gallery --plot-type embedding_plot --serve`, and skill wrappers for Codex, Claude Code, or Cursor.

Why is it gaining traction?

It stands out by bridging AI agents' plotting blind spots with reusable human taste memory—likes and rejects persist across sessions, sharpening results over time. The dynamic gallery UI and agent-specific installs (e.g., Cursor project rules) make it dead simple to integrate into workflows, unlike raw prompt engineering or static image datasets. Early adopters praise the before/after benchmarks showing crisper, pub-ready outputs.

Who should use this?

Bioinformatics engineers generating cell atlas embeddings or heatmaps via Cursor/Claude. Researchers prompting agents for Nature-style multi-panel figures. Scientific devs tired of tweaking Matplotlib/Seaborn guesses into publication visuals.

Verdict

Worth a spin for agent-driven scientific plotting—installs fast, demos convince quickly—but at 16 stars and 1.0% credibility, it's alpha-stage with basic docs and no tests. Fork and contribute if it fits; otherwise, monitor for maturity.

(198 words)

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