biomni

Execute complex biomedical research tasks across genomics, drug discovery, and clinical analysis.

1|Updated Jan 14, 2026
One-click install
npx skills add https://github.com/Sologa/codex-pipeline --skill biomni-sologa
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: biomni
Source: https://github.com/Sologa/codex-pipeline/tree/main/.codex/skills/biomni
Command: npx skills add https://github.com/Sologa/codex-pipeline --skill biomni-sologa

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates complex, multi-step biomedical research tasks, from experimental design to data analysis, by leveraging advanced AI reasoning and code execution.

Core Features & Use Cases

  • Autonomous Task Execution: Handles complex queries across genomics, drug discovery, and molecular biology.
  • Integrated Data Access: Utilizes ~11GB of biomedical databases for in-depth analysis.
  • Code Generation & Execution: Dynamically creates and runs analysis pipelines.
  • Use Case: Design a CRISPR screen, analyze single-cell RNA-seq data, predict drug ADMET properties, or interpret GWAS results with natural language prompts.

Quick Start

Initialize the agent with your preferred LLM and execute a biomedical research question.

Frequently Asked Questions about biomni

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
What is an autonomous AI agent for biomedical research?

An autonomous AI agent for biomedical research automates complex, multi-step tasks by integrating LLM reasoning with code execution. It handles experimental design and data analysis across genomics, drug discovery, molecular biology, and clinical analysis.

How do I analyze single-cell RNA-seq data using an AI agent?

To analyze single-cell RNA-seq data, you initialize the AI agent with your preferred LLM and provide a natural language prompt. The agent dynamically generates and executes the analysis pipeline by querying its biomedical databases.

Can I design a CRISPR screen and predict drug ADMET properties with natural language prompts?

Yes, you can design CRISPR screens and predict drug ADMET properties using natural language prompts. The agent leverages LLM reasoning and code execution to autonomously handle these complex molecular biology and drug discovery tasks.

Does this biomedical research agent require external database connections?

No external database connections are required for core analysis. The agent integrates approximately 11GB of biomedical databases internally, providing direct access for in-depth genomic, molecular, and clinical data analysis.

What is the best way to interpret GWAS results without writing manual analysis code?

The best way to interpret GWAS results without manual coding is using an autonomous AI agent. It dynamically creates and runs the analysis pipelines via LLM reasoning, translating your natural language queries into executable code.

What limitations exist when using an AI agent for genomics data analysis?

A limitation for genomics data analysis is the dependency on the integrated ~11GB database scope. Additionally, while the agent dynamically generates code, complex single-cell RNA-seq or GWAS pipelines require clear natural language prompts to execute accurately.