Is Biomni suitable for clinical genomics?

Yes, it can be used for rare disease diagnosis, variant pathogenicity interpretation, and phenotype-genotype mapping by leveraging integrated clinical datasets.

Which LLMs are compatible with the Biomni skill?

While Claude (Sonnet 4 or Opus) is recommended for best results, Biomni also supports OpenAI GPT-4, Google Gemini, Groq, and AWS Bedrock models.

What kind of data does Biomni have access to?

Biomni integrates approximately 11GB of data, including Ensembl, UniProt, ClinVar, PubMed abstracts, and various pathway databases like KEGG and Reactome.

Can Biomni execute code autonomously?

Yes, Biomni generates and executes Python code to process data and create analysis pipelines. It is recommended to run these tasks in isolated environments like Docker for security.

Biomni Biomedical AI Agent

Name: Biomni Biomedical AI Agent
Author: Activer007

byActiver007

0•

Data Science & ML

Automates complex biomedical research tasks including genomics analysis, drug discovery, and CRISPR screening using integrated data and code execution.

Biomni is a specialized autonomous agent framework developed by Stanford's SNAP lab, designed to streamline multi-step biomedical research. By combining LLM reasoning with an integrated 11GB knowledge base and dynamic code execution, it empowers researchers to perform sophisticated tasks across genomics, proteomics, and clinical analysis. Whether you are designing CRISPR screens, interpreting GWAS results, or predicting drug ADMET properties, Biomni handles task decomposition, data retrieval, and analysis pipeline creation to deliver actionable biological insights directly within your development environment.

Key Features

01Dynamic Python code generation and execution for data processing

02Multi-step autonomous biological reasoning and task planning

03Standardized evaluation framework for benchmarking biomedical task accuracy

04Integrated access to 11GB of curated biomedical databases and literature

05Cross-domain capabilities spanning genomics, proteomics, and drug discovery

060 GitHub stars

Use Cases

01Analyzing single-cell RNA-seq datasets for cell type annotation and differential expression

02Predicting ADMET properties and identifying targets for drug discovery projects

03Designing genome-wide CRISPR knockout screens and prioritizing target genes

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