Can Biomni help with clinical data analysis?

Yes, it is designed for tasks including rare disease diagnosis, variant interpretation, and mapping phenotypes to genotypes.

What is the Biomni Claude Code skill?

Biomni is an open-source framework from Stanford SNAP lab that enables Claude to act as an autonomous agent for complex biomedical research tasks.

Which LLM models work best with Biomni?

While it supports various providers, Claude 3.5 Sonnet is highly recommended for the best balance of speed and biological reasoning accuracy.

Is the code executed by Biomni secure?

Biomni executes LLM-generated code with system privileges. For security, it is recommended to run the skill in isolated environments like Docker or virtual machines.

Does Biomni require a large data download?

Yes, on its first use, Biomni downloads an integrated ~11GB biomedical data lake to support its reasoning and analysis capabilities.

Biomni Biomedical AI Research Agent

Name: Biomni Biomedical AI Research Agent
Author: Activer007

byActiver007

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Ciencia de Datos y ML

Automates complex biomedical research tasks including genomics, drug discovery, and clinical analysis through autonomous AI reasoning and code execution.

Biomni is a specialized biomedical AI framework designed to function as an autonomous research assistant within Claude Code. Developed by Stanford's SNAP lab, it empowers researchers to execute multi-step biological queries by decomposing tasks into executable code and data analysis pipelines. By leveraging an integrated 11GB data lake containing genomics, proteomics, and clinical literature, Biomni bridges the gap between high-level biological hypotheses and technical implementation, making it an indispensable tool for bioinformaticians and life science developers working on CRISPR design, single-cell analysis, or drug property prediction.

Características Principales

01Cross-domain support for genomics, proteomics, and clinical diagnostics

02Access to a built-in 11GB biomedical knowledge base and data lake

031 GitHub stars

04Multi-step biological reasoning and autonomous task planning

05Dynamic code generation and execution for data processing pipelines

06Standardized evaluation framework for benchmarking research outputs

Casos de Uso

01Designing genome-wide CRISPR screens and prioritizing gene targets based on pathway relevance

02Predicting ADMET properties and identifying therapeutic targets for drug discovery candidates

03Processing single-cell RNA-seq data for cell type annotation and differential expression analysis

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add activer007/ordinary-claude-skills biomni

For use in Claude.ai and ChatGPT

Download Skill