kakashi-ventures

kakashi-ventures / magellan-cli

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Autonomous AI experiment in cross-disciplinary scientific discovery. Can a multi-agent system autonomously find real scientific connections that humans haven't made yet? This project tests that question.

10
1
100% credibility
Found Mar 30, 2026 at 10 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

An autonomous AI system that generates novel cross-disciplinary scientific hypotheses by simulating a multi-agent research pipeline focused on undiscovered public knowledge.

How It Works

1
🔍 Discover Magellan

You hear about a cool tool that uses smart AI to uncover hidden links between different science fields, sparking your curiosity for new ideas.

2
🛠️ Get everything ready

Follow easy steps to prepare your computer with a special AI helper and basic tools, no expertise needed.

3
🚀 Start the adventure

Simply tell it to explore by typing /discover, then step away as it hunts for fresh scientific connections on its own.

4
⏳ Watch it work

Check progress anytime with /status, feeling excited as it builds ideas through thinking, checking, and refining over 20-55 minutes.

5
📋 Review discoveries

Get back beautiful cards with testable new hypotheses, complete with checks for novelty and strength.

6
✅ Double-check ideas

Let it automatically consult other smart thinkers for agreement, or use ready-made guides to verify by hand.

🏆 Share your finds

Connect to the discovery website to claim credit, see your rank on the leaderboard, and contribute to science.

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AI-Generated Review

What is magellan-cli?

Magellan-cli is a Python CLI that launches a multi-agent AI pipeline in Anthropic's Claude Code terminal to autonomously discover novel scientific hypotheses by linking disjoint fields, like circadian biology to tumor immune evasion. Type `/discover` (or target it with `/discover A × B`), step away for 20-55 minutes, and get structured hypothesis cards with computational checks, critiques, rankings, and optional cross-model validation via GPT and Gemini APIs. It solves the "undiscovered public knowledge" problem—no domain expertise needed, just Claude Pro and Node.js for exports.

Why is it gaining traction?

It stands out with fully autonomous target selection via 10 strategies for disjoint literatures, unlike goal-dependent tools like Google AI Co-Scientist; cycles generate, evolve, and quality-gate ideas with impact-aware scoring. Users love the one-command workflow producing testable outputs, persistent discovery logs for meta-learning, and web attribution for leaderboards—echoing autonomous exploration GitHub projects but for science. Commands like `/status`, `/evolve`, and `/export gpt` make iteration dead simple.

Who should use this?

Life sciences researchers bridging fields, such as linking autonomous experiments in electronic materials to broader discovery. AI tinkerers building autonomous coders or testers for multi-agent systems in scientific domains. Domain experts providing `--context` or `--papers` for targeted runs, like solving antibiotic resistance via cross-disciplinary links.

Verdict

Early-stage with 10 stars and 1.0% credibility—solid README and methodology docs, but low maturity means potential hangs in long sessions; test on non-critical ideas first. Intriguing for autonomous experimentation fans: install Claude CLI, run `/discover`, and validate the hype yourself.

(198 words)

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