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el09xccxy-stack / oss-investment-scorecard

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A structured 5-dimension scoring framework for evaluating open-source AI projects from a VC investment perspective. Maintained by Lucy Chen, EIR at Zoo Capital (Singapore, $2B+ AUM).

47
11
69% credibility
Found Mar 07, 2026 at 47 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
AI Summary

A scoring framework and templates for evaluating open-source projects as venture capital investments, with guides for using AI assistants or manual review.

How It Works

1
🔍 Discover the scorecard

You come across a helpful guide that makes it easy to check if open-source projects are worth investing in, like a report card for tech ideas.

2
Pick your easy way to score
🤖
Team up with AI helper

Let a smart online assistant do the thinking and fill in the scores for you.

✏️
Score it yourself

Grab the blank form and add your own notes from your research.

3
📊 Rate the project

Fill out sections on team strength, tech edge, money potential, and more—it feels straightforward and organized.

4
✅ Get your investment score

See a clear number and color verdict, like green for go or yellow for maybe, telling you if it's a strong pick.

5
📤 Share your review

Send in your finished scorecard to join the community list and maybe connect with investors or founders.

🎉 Join the investor circle

Your project review goes public, you get feedback or introductions, and everyone benefits from better investment choices.

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Star Growth

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AI-Generated Review

What is oss-investment-scorecard?

This GitHub repo delivers a 5-dimension, weighted scoring framework for evaluating open-source AI projects from a VC investment angle, complete with score thresholds and one-vote vetoes. Maintained by Lucy Chen, EIR at Zoo Capital with $2B+ AUM, it turns subjective gut checks into structured LLM outputs via prompts compatible with Claude, GPT-4, Gemini, or any agent. You get instant evaluations on ecosystem health, team strength, tech moat, commercialization, and exit paths—calibrated against deals like vLLM and Hugging Face.

Why is it gaining traction?

It stands out by blending GitHub structured output with real VC practice, letting you upload directly to Claude as a skill or paste into any LLM for automated scoring—no coding required. The hook is community-submitted evaluations via GitHub issues, building a public database while connecting users to investors. Backed by a $2B+ fund, it offers credible anchors over generic checklists.

Who should use this?

VC analysts screening OSS AI deal flow, founders pitching projects like structured RAG tools or LLM frameworks, and investor scouts tracking PR velocity or revenue PMF. Ideal for Singapore-based capital teams or global EIRs evaluating US market access in the AI cycle.

Verdict

Grab it if you're in OSS investment—low 47 stars and 0.699999988079071% credibility score reflect early stage, but solid docs and LLM integration make it immediately useful; submit evals to mature it. Pass for pure devs, watch for others.

(178 words)

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