Reuse,
don't reinvent.

🌳 Save a token, save a tree.

Not a repo health-scorer — a reuse decision engine.

Every "is this repo any good?" tool grades maintenance. RepoHunter answers the question you actually have: should I adopt this — for my project, on my machine? A GO / MAYBE / SKIP call and a plan to do it — and now a skill your AI agent calls before it installs anything.

🔥 Every token spent regenerating code that already exists is wasted.

Regenerating a ~500-line module ≈ ~20,000 tokens all-in ≈ $0.04–$0.80 cheap model → premium · all-in = the code plus the context & retries it takes to generate it

…now multiply that by everyone, every day ↓

scenario	AI-assisted devs	redundant lines / dev·day	× 40 = tokens / day
low	5M	60	~12B
mid	10M	100	~40B
high	25M	200	~200B

A back-of-the-envelope — not a measurement. Every assumption is shown so you can argue with it. Across the model tiers, the mid case is ~$80K–$1.6M/day (≈ $29M–$584M/year) of compute burned re-writing code that already exists. That's the problem RepoHunter exists to kill.

Estimate your own savings →

★ Star it on GitHub

Runs locally · pluggable AI (local model or your key) · MIT open source · built & maintained in the open by a human + his AI

# clone, point it at your project, go hunting git clone https://github.com/meetziggy/repohunter cp config.example.json config.json python3 repohunter.py refresh python3 repohunter.py serve

Try it — right here

Drop any repo. Get a live verdict.

Real GitHub data, scored in your browser — popularity, freshness, health, maturity, a GO / MAYBE / SKIP call, and whether it runs on an ordinary machine. (The full AI dossier, tailored to your project, runs when you install the tool.)

Or try:

See the full store (what it looks like running locally)

RepoHunter store: repos ranked with GO / MAYBE / SKIP verdicts, resource-fit, and scores

the vast majority of software runs on open sourcereuse, don't re-burnthe greenest code is the code you never regeneratereinventing what already exists is wasted effortgenerating code that already exists burns energy + money for nothingAI reaches for the same popular libraries — the long tail goes dark the vast majority of software runs on open sourcereuse, don't re-burnthe greenest code is the code you never regeneratereinventing what already exists is wasted effortgenerating code that already exists burns energy + money for nothingAI reaches for the same popular libraries — the long tail goes dark

Where this is headed: as AI agents write more of the code, your agent calls RepoHunter automatically — an MCP server that checks before it installs. Same verdict, zero clicks.

Not another repo health-scorer

It answers the question you actually have

Every other tool tells you “is this repo well-maintained?” RepoHunter tells you “should I adopt this — for my project, on my machine — and what will it cost me?”

🎯

An integration dossier

Relevance to your project, how to integrate, the enhancement you'd gain, build-vs-integrate, and a GO / MAYBE / SKIP call.

💻

Runs-on-your-hardware

A real resource-fit check plus a rough cost to integrate — tokens, time, agents. Nobody else does this.

📥

Ingest from anywhere

Drop a repo, search GitHub live, or paste a “top-10 repos” YouTube video — RepoHunter pulls the repos out.

🔒

Local-first & honest

Runs on your machine with a local model — no bill, no lock-in. It plans; it never installs behind your back.

🧭

Knows what kind it is

Classifies each repo and proposes the right move — integrate, add as a source, or just learn from it.

⭐

Promotes the author

Every link sends you to star the original. RepoHunter celebrates open source — it doesn't strip-mine it.

Low-carbon by default

The greenest code is the code you never regenerate

Every token an AI generates burns real energy — electricity and water in a data center. When millions of us regenerate the same auth flow, the same RAG pipeline, the same library that already exists, that's redundant compute for zero new value. RepoHunter finds what's already built, so you don't reinvent — and don't re-burn.

💸

Fewer tokens, smaller bill

Adopting proven code beats regenerating it from scratch. You skip the compute — and the cost that comes with it.

🌱

Less compute, less carbon

AI inference runs on real power and cooling. Reuse simply avoids it. Think of it as recycling, for software.

🧬

Breaks the monoculture

AI reaches for the same ten popular libraries every time. RepoHunter surfaces the long tail — so we stop all building the same thing.

It's not about an exact number — it's that reusing proven code means you don't spend the time, cost, or energy of rebuilding it.

What reuse is worth — to you

Your inputs. Your math. We don't put a number on it — you do.

We won't invent a savings figure and paste it on a billboard. Put in your own reality and watch the estimate move. Every assumption stays on screen — nothing hidden, nothing asserted by us.

🧮 What this calculator does

It estimates what you save by reusing existing code instead of rebuilding it from scratch — in three things you actually care about: your time, your money, and AI tokens.

🔥 Wait — what's a "token"?

AI models read and write in tokens — tiny chunks of text, about 4 characters each. It's the unit they're billed by. Every time an AI regenerates code that already exists somewhere, it burns tokens you pay for — for zero new value. Reuse the code and you skip the burn entirely.

👇 How to use it

Type in your own numbers below. The estimate updates live. We never assert a figure — the math is yours, and every assumption is shown. Save a token, save a tree. 🌳

Times a month you reach for something that likely already exists every reuse is a build you skip

Hours it'd take you to build & maintain one yourself writing is the cheap part; owning it forever isn't

Your time, per hour ($) your rate — or leave blank to see hours only

Avg size of what you'd have rebuilt (lines) all-in ≈ lines × ~40 tokens (code + context + retries)

⏱️

—

hours / month
you don't rebuild

🔥

—

AI tokens / month
you don't burn (all-in)

💵 In AI compute, that's roughly this much saved per year — by model tier:

Budget model$—

Standard model$—

Premium model$—

illustrative rates ≈ Budget $2 · Standard $10 · Premium $40 per 1M tokens · all-in ≈ 40 tokens/line

Put in your numbers above — I'll do the math live.

Rough and illustrative — a way to reason about your situation, not a promise or a claim about typical results. RepoHunter helps you find and vet what to reuse; the value depends entirely on what you'd have built instead.

🔍 Don't trust us — validate it

Every number here comes from one short, transparent algorithm. Read it, download and run it, change the assumptions — or paste it into any AI and ask it to poke holes. No trust required.

# all-in tokens = the code PLUS the context & retries it takes to generate it
tokens/month  = reuses × lines × 40        # 40 ≈ 13 output tokens/line × ~3 overhead
$/year (tier) = tokens/month × 12 ÷ 1e6 × rate
                # rate = $2 Budget · $10 Standard · $40 Premium per 1M tokens
ecosystem/day = AI-devs × redundant-lines/day × 40

⬇ Download the algorithm (Python)

Own a repo? Wear the badge.

A live RepoHunter score you can embed — and can't fake

One line in your README shows your repo's current RepoHunter verdict, straight from live GitHub data. You embed it — but you can't edit what it says. That's the whole point: a badge nobody can game is a badge worth trusting. Stale repo, honest badge.

Paste into your README:

[![RepoHunter](https://img.repohunter.dev/badge/ollama/ollama.svg)](https://repohunter.dev)

Or share the full live report → repohunter.dev/r/ollama/ollama

Transparent score over live GitHub data — not a safety or security rating (that layer ships only after we validate it). Public repos need no login; the badge updates itself.

Why it exists

A love letter to open source, disguised as a tool

Every week, thousands of people give away brilliant work for free — and most of it is never found. Discovery rewards whatever's already popular, so two things waste away at once: great projects die in obscurity, and the rest of us reinvent what already exists because we couldn't find it.

The whole digital world runs on maintainers nobody thanks. And now that AI writes more of the code — reaching for the same ten popular libraries every time — the long tail of great, perfect-for-you software is going dark faster.

RepoHunter pushes back. It finds the right code for you — underdogs included — tells you the honest truth about whether it fits your project and your hardware, and sends the credit home: every path ends at a ★ for the author. Reuse, with gratitude.

Built by a veteran and his AI, in the open. Free forever. — Chris & Ziggy 🤖 · Read the manifesto →

The human behind it

A first-time developer + his AI

Chris Gorzelic veteran · first-time AI dev

A U.S. veteran learning to build in the open. Chris created RepoHunter with his AI agent, Ziggy — proof that reuse-first, local-first, agent-built software is within reach for anyone willing to start.

About Chris → Chris on GitHub ↗

🐾

No coffee. Give back instead.

RepoHunter is free, always. If it saves you time, don't buy me a coffee — do something better. I'm a veteran, and I'd rather send that goodwill to those who served on four legs and two.

🐕‍🦺 K9s For Warriors 🎖️ Mission K9 Rescue