explaingit

goekelab/awesome-genomic-skills

18Audience · researcherComplexity · 1/5Setup · easy

TLDR

A curated directory of ready-made AI agent skills and live database connections for genomics and bioinformatics work, covering variant calling, RNA-seq, single-cell analysis, protein structure, and drug discovery pipelines.

Mindmap

mindmap
  root((awesome-genomic-skills))
    Categories
      Skill libraries
      MCP servers
      Benchmarks
      Collections
    Analysis Types
      Variant calling
      RNA-seq
      Single-cell
      Protein structure
    Databases
      UniProt
      Ensembl
      gnomAD
      PubMed
    AI Agents
      Claude Code
      OpenAI
      DeepMind
Click or tap to explore — scroll the page freely

Code map

Detail Auto

An interactive map of this repo's files and how they connect — its source is parsed live in your browser. Click Visualize to build it.

filefunction / class

Things people build with this

USE CASE 1

Load pre-built bioinformatics skills into Claude Code to automate variant calling or RNA-seq workflows without writing the pipeline yourself

USE CASE 2

Connect an AI agent to genomics databases like UniProt, Ensembl, or gnomAD using the listed MCP servers for live programmatic queries

USE CASE 3

Discover benchmarks to evaluate how well AI agents perform on biological analysis tasks before choosing one for your research pipeline

Tech stack

Markdown

Getting it running

Difficulty · easy Time to first run · 5min

In plain English

This repository is a curated directory of tools and resources for using AI coding assistants to do genomics and bioinformatics work. It organizes entries into a few categories: skill libraries, MCP servers, benchmarks, and general collections of agent skills. A skill, as the README defines it, is a Markdown file that teaches an AI agent how to perform a specific task. An agent loads these files into its context and can then follow the documented steps when asked. An MCP server, by contrast, is a running service that gives the agent a live connection to external databases or tools, like querying a genetic variant database or running a protein structure lookup. The distinction matters because skills teach procedure while MCP servers provide data access. The entries span well-known projects from groups like Google DeepMind, OpenAI, and Anthropic, as well as independent academic labs and open-source teams. The skills listed cover common bioinformatics workflows including variant calling from DNA sequencing data, RNA-seq gene expression analysis, single-cell analysis, protein structure work, drug discovery pipelines, and clinical genomics. Several entries include hundreds of pre-built skills for specific analysis types. MCP server entries give AI agents direct programmatic access to databases like UniProt, Ensembl, gnomAD, PubMed, and others used routinely in life sciences research. Some of the larger bundles combine both skills and MCP servers in one package. The list also points to genomics-specific benchmarks for evaluating how well AI agents perform on biological analysis tasks, and to broader skill collections that are not specific to life sciences but may be useful in research contexts. This is a reference and discovery resource, not a software package itself. No installation is needed to browse it, and individual entries link to their respective repositories.

Copy-paste prompts

Prompt 1
I do RNA-seq analysis in Python. Which skills from awesome-genomic-skills should I load into Claude Code to automate differential expression analysis, and how do I install them?
Prompt 2
I want to give my AI agent live access to gnomAD and UniProt using the MCP servers listed in this repo. Walk me through setting up one of those MCP server entries.
Prompt 3
Which entries in awesome-genomic-skills cover single-cell analysis workflows and what AI agents are they compatible with?
Open on GitHub → Explain another repo

← goekelab on gitmyhub — every repo by this author, as a profile.

Verify against the repo before relying on details.