Which LLM models work best with Biomni?

While it supports various providers, Anthropic Claude (Sonnet 3.5 or Opus) is recommended for the best balance of scientific reasoning and code generation quality.

Does Biomni require a local database?

Yes, Biomni automatically downloads an approximately 11GB biomedical data lake on its first use to support its reasoning and analysis functions.

Can Biomni help with clinical genomics?

Yes, it can perform tasks like rare disease diagnosis, variant pathogenicity prediction, and phenotype-genotype mapping by leveraging integrated clinical datasets.

Is it safe to run Biomni locally?

Biomni executes LLM-generated code with system privileges to perform analysis. It is highly recommended to run the agent in isolated environments like Docker or virtual machines.

Biomni is an autonomous agent framework from Stanford SNAP lab designed for executing complex biomedical research tasks across various scientific domains using LLMs.

Biomni Biomedical Research Agent

Name: Biomni Biomedical Research Agent
Author: Microck

byMicrock

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数据科学与机器学习

Automates complex biomedical research tasks including genomics, drug discovery, and clinical data analysis using an autonomous AI agent framework.

Biomni is an open-source autonomous agent framework developed by Stanford's SNAP lab, designed specifically to bridge the gap between high-level biological reasoning and technical data execution. It enables Claude to perform multi-step planning, generate and run analysis code, and retrieve insights from an integrated 11GB biomedical knowledge base encompassing gene databases, protein structures, and clinical literature. This skill is indispensable for researchers and bioinformaticians working on CRISPR design, single-cell RNA-seq, drug ADMET prediction, and GWAS interpretation, providing a unified interface for sophisticated scientific discovery.

主要功能

01Autonomous multi-step biological reasoning and task decomposition

02Dynamic Python code generation and execution for data pipelines

03Integrated access to 11GB of curated biomedical databases

04Cross-domain support for genomics, proteomics, and drug discovery

05Built-in evaluation framework for benchmarking research outputs

0681 GitHub stars

使用场景

01Predicting drug ADMET properties and performing target identification

02Analyzing single-cell RNA-seq datasets for cell type annotation and trajectory analysis

03Designing genome-wide CRISPR screens and prioritizing gene targets

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add microck/ordinary-claude-skills biomni

For use in Claude.ai and ChatGPT

Download Skill