GoekeLab

GoekeLab / awesome-genomic-skills

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A curated list of awesome genomics and bioinformatics agentic skills, MCPs and benchmarks for Claude Code, Copilot, Codex, Cursor, Gemini CLI, etc

14
2
89% credibility
Found May 29, 2026 at 15 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
AI Summary

This repository is a curated directory of resources for using AI coding assistants in genomics and bioinformatics research. It collects and organizes links to skill libraries (instruction sets that teach AI how to perform scientific tasks), MCP servers (tools that connect AI to biological databases), and benchmarks (tests that measure how well different AI tools perform). The collection is maintained by GoekeLab and includes resources from major institutions like Google DeepMind, OpenAI, Anthropic, and Harvard University. Rather than being a functional program, it's a research guide that helps scientists discover and select the right AI tools for their work.

How It Works

1
🔬 You want AI to help with your genomics research

You've heard that AI coding assistants can help analyze DNA, find genetic variants, and speed up your bioinformatics work.

2
🗺️ You discover a curated collection of tools

You find a comprehensive list that organizes hundreds of resources for using AI in life sciences and genomics research.

3
✨ You explore different categories of resources

You browse through skill libraries that teach AI how to handle genomics tasks, and connection tools that link AI to scientific databases.

4
You choose your path based on your needs
📚
Teaching path: Pick skills for your AI assistant

You select ready-made instruction sets that teach AI agents how to perform specific genomics tasks like RNA analysis or variant calling.

🔌
Connection path: Set up database access

You choose tools that let your AI assistant connect directly to scientific databases like Ensembl, UniProt, and AlphaFold.

5
📊 You check how well different tools work

You look at benchmark results to see which AI tools perform best at genomics tasks before committing to one.

🎉 Your AI assistant becomes a genomics expert

With the right skills and connections in place, your AI helper can now assist with your research tasks like a knowledgeable colleague.

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Star Growth

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AI-Generated Review

What is awesome-genomic-skills?

This is a curated list aggregating skills and MCP servers for AI coding agents working in genomics and bioinformatics. It covers Claude Code, GitHub Copilot, Codex, Cursor, and Gemini CLI, organizing entries into agent skills for bioinformatics work, MCP servers for life sciences, benchmarks for evaluation, and general AI coding agent skill collections. The list explains what skills are (Markdown files teaching agents procedural know-how) versus MCP servers (services giving agents connections to external systems via standardized tool calls).

Why is it gaining traction?

The genomics field is seeing rapid adoption of AI coding agents, and developers need a way to find the right skills and integrations for their workflows. This list solves the discovery problem by curating options from Google DeepMind, OpenAI, Anthropic, and academic labs. It includes practical details like which databases each skill wraps (Ensembl, UniProt, gnomAD, GTEx, ClinVar, and more) and benchmark results showing skill effectiveness.

Who should use this?

Bioinformaticians integrating AI agents into their pipelines, researchers evaluating which agent and skill combinations work best for genomics tasks, and developers building genomics-focused AI tools. Computational biologists working with sequencing data, pharmaceutical researchers exploring drug-target interactions, and lab teams wanting to automate bioinformatics workflows will find the most value here.

Verdict

This is a useful starting point for navigating the fragmented landscape of genomics AI tools, with a credibility score of 0.8999999761581421%. The low star count (14) reflects early-stage community adoption, and the list lacks structured evaluation criteria or testing benchmarks. Treat it as a discovery layer rather than a definitive recommendation.

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