Borrowing it
Nothing to install: this file belongs to taehojo/alphagenome-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/taehojo/alphagenome-mcp/main/CLAUDE.mdgit clone --depth 1 https://github.com/taehojo/alphagenome-mcpWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/taehojo/alphagenome-mcp/claude-md)<a href="https://agentmods.dev/instructions/taehojo/alphagenome-mcp/claude-md"><img src="https://agentmods.dev/badge/instructions/taehojo/alphagenome-mcp/claude-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/taehojo/alphagenome-mcp/claude-md"><img src="https://agentmods.dev/badge/instructions/taehojo/alphagenome-mcp/claude-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00952 | $0.00952 |
| Opus 5 | $0.00476 | $0.00476 |
| Sonnet 5 | $0.00190 | $0.00190 |
| Haiku 4.5 | $0.00095 | $0.00095 |
Grade A, and why
alphagenome-mcp CLAUDE.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
Project Overview
AlphaGenome MCP (Model Context Protocol) Server - A specialized MCP server for genomic variant analysis using Google DeepMind's AlphaGenome AI.
Real API Integration: Uses AlphaGenome Python SDK via a Python bridge for production-grade genomic analysis.
Development Commands
Building and Testing
# Install dependencies
npm install
# Build TypeScript
npm run build
# Watch mode (development)
npm run dev
# Run linter
npm run lint
npm run lint:fix
# Format code
npm run format
npm run format:check
# Type check without building
npm run typecheck
Running Locally
# Run with your AlphaGenome API key
ALPHAGENOME_API_KEY=your-key-here node build/index.js
# Or provide via command-line argument
node build/index.js --api-key your-key-here
# The server communicates via stdio (MCP protocol)
# Output to stderr is for logging, stdout is for MCP messages
Architecture
Core Components
-
src/index.ts - Main MCP server entry point
- Handles MCP protocol communication
- Routes tool calls to appropriate handlers
- Error handling and formatting
-
src/alphagenome-client.ts - API client
- Python subprocess bridge to AlphaGenome SDK
- Real-time API calls to Google DeepMind's service
- Comprehensive error handling
-
src/tools.ts - MCP tool definitions
- Three main tools: predict_variant_effect, analyze_region, batch_score_variants
- JSON schema definitions for inputs
-
src/types.ts - TypeScript type definitions
- All interfaces and types
- Custom error classes
-
src/utils/ - Utility modules
- validation.ts: Zod schemas for input validation
- formatting.ts: Markdown output formatting
Data Flow
Claude Desktop → stdio → MCP Server → Validate Input → AlphaGenome Client → Python Bridge → AlphaGenome API → Format Output → Claude
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 155 lines · 952 tokens per session scan A 8319bf446611
alphagenome-mcp CLAUDE.md is an instructions file published in the GitHub repository taehojo/alphagenome-mcp (2 stars, last pushed 6mo ago), licensed MIT. It adds 952 tokens to every session, about $0.0048 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other instructions, from other repositories
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
codex AGENTS.md
AGENTS.md instructions for openai/codex, covering rust/codex-rs, the codex-core crate, code review rules, crate api surface and model visible context.
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, applying the notice (cutover) and fallback chain (never fail the build).
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
deepseek-harness AGENTS.md
AGENTS.md instructions for deepseek-ai/deepseek-harness, covering agents.md, pre-stable apis and released session data, repository layout, commands and host sandbox failures.