RuvScan MCP Server - Sublinear intelligence for GitHub discovery
Wellknown found it in public sources; nobody has proven control of it yet. Claiming takes one click if the repository is under your GitHub account, or a small file on your domain otherwise. Verified owners get the badge, 15-minute checks, status alerts, edits that outrank crawled data, and a ranking boost.
Agents can do it too: POST https://wellknown.network/api/v1/claims with {"agent":"iflow-mcp-hulupeep-ruvscan-mcp","method":"well_known_file"} — machine-readable steps at claim.json, guide at /docs/claim.
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# 🧠 RuvScan - MCP Server for Intelligent GitHub Discovery [](LICENSE) [](https://modelcontextprotocol.io) [](https://www.python.org/downloads/) [](https://pypi.org/project/ruvscan-mcp/) [](docker-compose.yml) > **Give Claude the power to discover GitHub tools with sublinear intelligence.** RuvScan is a **Model Context Protocol (MCP) server** that connects to Claude Code CLI, Codex, and Claude Desktop. It turns GitHub into your AI's personal innovation scout — finding tools, frameworks, and solutions you'd never think to search for. **Oh, it's a work in progress - so suggest changes to make it better.* It comes packaged with RUVNET repo but you can add ANY other repo like Andrej Kaparthy's or other folks on the edge of what you are working on. --- ## 🎯 What Is This? **A GitHub search that actually understands what you're trying to build.** ### The Problem You're building something new (an app or feature). You know there's probably a library, framework, or algorithm out there that could 10× your project. But: - 🔍 **Search is broken** - You'd have to know the exact keywords - 📚 **Too many options** - Millions of repos, most irrelevant - 🎯 **Wrong domain** - The best solution might be in a totally different field - ⏰ **Takes forever** - Hours of browsing docs and READMEs ### The Solution **RuvScan thinks like a creative developer**, not a search engine: ``` You: "I'm building an AI app. Context recall is too slow." RuvScan: "Here's a sublinear-time solver that could replace your vector database queries. It's from scientific computing, but the O(log n) algorithm applies perfectly to semantic s…
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