Biomni logo

Biomni

Community
jimmc414
biomni

Autonomous biomedical AI agent framework for executing complex research tasks across genomics, drug discovery, molecular biology, and clinical analysis. Use this skill when conducting multi-step biomedical research including CRISPR screening design, single-cell RNA-seq analysis, ADMET prediction, GWAS interpretation, rare disease diagnosis, or lab protocol optimization. Leverages LLM reasoning with code execution and integrated biomedical databases.

Overview

Publisherjimmc414
RepositoryKosmos
Skill namebiomni
Stars
585
Forks
105
Bundled files
5
Links
Skill sourceRepository
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by jimmc414 on GitHub. Read the source before you install it.

Installation

Install the Biomni AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/jimmc414/Kosmos.git /tmp/Kosmos
mkdir -p .claude/skills
cp -r /tmp/Kosmos/kosmos-claude-scientific-skills/scientific-skills/biomni .claude/skills/biomni
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Biomni in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Biomni on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Biomni is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Biomni

Overview

Biomni is an open-source biomedical AI agent framework from Stanford's SNAP lab that autonomously executes complex research tasks across biomedical domains. Use this skill when working on multi-step biological reasoning tasks, analyzing biomedical data, or conducting research spanning genomics, drug discovery, molecular biology, and clinical analysis.

Core Capabilities

Biomni excels at:

  1. Multi-step biological reasoning - Autonomous task decomposition and planning for complex biomedical queries
  2. Code generation and execution - Dynamic analysis pipeline creation for data processing
  3. Knowledge retrieval - Access to ~11GB of integrated biomedical databases and literature
  4. Cross-domain problem solving - Unified interface for genomics, proteomics, drug discovery, and clinical tasks

When to Use This Skill

Use biomni for:

  • CRISPR screening - Design screens, prioritize genes, analyze knockout effects
  • Single-cell RNA-seq - Cell type annotation, differential expression, trajectory analysis
  • Drug discovery - ADMET prediction, target identification, compound optimization
  • GWAS analysis - Variant interpretation, causal gene identification, pathway enrichment
  • Clinical genomics - Rare disease diagnosis, variant pathogenicity, phenotype-genotype mapping
  • Lab protocols - Protocol optimization, literature synthesis, experimental design

Quick Start

Installation and Setup

Install Biomni and configure API keys for LLM providers:

bash
uv pip install biomni --upgrade

Configure API keys (store in .env file or environment variables):

bash
export ANTHROPIC_API_KEY="your-key-here"
# Optional: OpenAI, Azure, Google, Groq, AWS Bedrock keys

Use scripts/setup_environment.py for interactive setup assistance.

Basic Usage Pattern

python
from biomni.agent import A1

# Initialize agent with data path and LLM choice
agent = A1(path='./data', llm='claude-sonnet-4-20250514')

# Execute biomedical task autonomously
agent.go("Your biomedical research question or task")

# Save conversation history and results
agent.save_conversation_history("report.pdf")

Working with Biomni

1. Agent Initialization

The A1 class is the primary interface for biomni:

python
from biomni.agent import A1
from biomni.config import default_config

# Basic initialization
agent = A1(
    path='./data',  # Path to data lake (~11GB downloaded on first use)
    llm='claude-sonnet-4-20250514'  # LLM model selection
)

# Advanced configuration
default_config.llm = "gpt-4"
default_config.timeout_seconds = 1200
default_config.max_iterations = 50

Supported LLM Providers:

  • Anthropic Claude (recommended): claude-sonnet-4-20250514, claude-opus-4-20250514
  • OpenAI: gpt-4, gpt-4-turbo
  • Azure OpenAI: via Azure configuration
  • Google Gemini: gemini-2.0-flash-exp
  • Groq: llama-3.3-70b-versatile
  • AWS Bedrock: Various models via Bedrock API

See references/llm_providers.md for detailed LLM configuration instructions.

2. Task Execution Workflow

Biomni follows an autonomous agent workflow:

python
# Step 1: Initialize agent
agent = A1(path='./data', llm='claude-sonnet-4-20250514')

# Step 2: Execute task with natural language query
result = agent.go("""
Design a CRISPR screen to identify genes regulating autophagy in
HEK293 cells. Prioritize genes based on essentiality and pathway
relevance.
""")

# Step 3: Review generated code and analysis
# Agent autonomously:
# - Decomposes task into sub-steps
# - Retrieves relevant biological knowledge
# - Generates and executes analysis code
# - Interprets results and provides insights

# Step 4: Save results
agent.save_conversation_history("autophagy_screen_report.pdf")

3. Common Task Patterns

CRISPR Screening Design
python
agent.go("""
Design a genome-wide CRISPR knockout screen for identifying genes
affecting [phenotype] in [cell type]. Include:
1. sgRNA library design
2. Gene prioritization criteria
3. Expected hit genes based on pathway analysis
""")
Single-Cell RNA-seq Analysis
python
agent.go("""
Analyze this single-cell RNA-seq dataset:
- Perform quality control and filtering
- Identify cell populations via clustering
- Annotate cell types using marker genes
- Conduct differential expression between conditions
File path: [path/to/data.h5ad]
""")
Drug ADMET Prediction
python
agent.go("""
Predict ADMET properties for these drug candidates:
[SMILES strings or compound IDs]
Focus on:
- Absorption (Caco-2 permeability, HIA)
- Distribution (plasma protein binding, BBB penetration)
- Metabolism (CYP450 interaction)
- Excretion (clearance)
- Toxicity (hERG liability, hepatotoxicity)
""")
GWAS Variant Interpretation
python
agent.go("""
Interpret GWAS results for [trait/disease]:
- Identify genome-wide significant variants
- Map variants to causal genes
- Perform pathway enrichment analysis
- Predict functional consequences
Summary statistics file: [path/to/gwas_summary.txt]
""")

See references/use_cases.md for comprehensive task examples across all biomedical domains.

4. Data Integration

Biomni integrates ~11GB of biomedical knowledge sources:

  • Gene databases - Ensembl, NCBI Gene, UniProt
  • Protein structures - PDB, AlphaFold
  • Clinical datasets - ClinVar, OMIM, HPO
  • Literature indices - PubMed abstracts, biomedical ontologies
  • Pathway databases - KEGG, Reactome, GO

Data is automatically downloaded to the specified path on first use.

5. MCP Server Integration

Extend biomni with external tools via Model Context Protocol:

python
# MCP servers can provide:
# - FDA drug databases
# - Web search for literature
# - Custom biomedical APIs
# - Laboratory equipment interfaces

# Configure MCP servers in .biomni/mcp_config.json

6. Evaluation Framework

Benchmark agent performance on biomedical tasks:

python
from biomni.eval import BiomniEval1

evaluator = BiomniEval1()

# Evaluate on specific task types
score = evaluator.evaluate(
    task_type='crispr_design',
    instance_id='test_001',
    answer=agent_output
)

# Access evaluation dataset
dataset = evaluator.load_dataset()

Best Practices

Task Formulation

  • Be specific - Include biological context, organism, cell type, conditions
  • Specify outputs - Clearly state desired analysis outputs and formats
  • Provide data paths - Include file paths for datasets to analyze
  • Set constraints - Mention time/computational limits if relevant

Security Considerations

⚠️ Important: Biomni executes LLM-generated code with full system privileges. For production use:

  • Run in isolated environments (Docker, VMs)
  • Avoid exposing sensitive credentials
  • Review generated code before execution in sensitive contexts
  • Use sandboxed execution environments when possible

Performance Optimization

  • Choose appropriate LLMs - Claude Sonnet 4 recommended for balance of speed/quality
  • Set reasonable timeouts - Adjust default_config.timeout_seconds for complex tasks
  • Monitor iterations - Track max_iterations to prevent runaway loops
  • Cache data - Reuse downloaded data lake across sessions

Result Documentation

python
# Always save conversation history for reproducibility
agent.save_conversation_history("results/project_name_YYYYMMDD.pdf")

# Include in reports:
# - Original task description
# - Generated analysis code
# - Results and interpretations
# - Data sources used

Resources

References

Detailed documentation available in the references/ directory:

  • api_reference.md - Complete API documentation for A1 class, configuration, and evaluation
  • llm_providers.md - LLM provider setup (Anthropic, OpenAI, Azure, Google, Groq, AWS)
  • use_cases.md - Comprehensive task examples for all biomedical domains

Scripts

Helper scripts in the scripts/ directory:

  • setup_environment.py - Interactive environment and API key configuration
  • generate_report.py - Enhanced PDF report generation with custom formatting

External Resources

Troubleshooting

Common Issues

Data download fails

python
# Manually trigger data lake download
agent = A1(path='./data', llm='your-llm')
# First .go() call will download data

API key errors

bash
# Verify environment variables
echo $ANTHROPIC_API_KEY
# Or check .env file in working directory

Timeout on complex tasks

python
from biomni.config import default_config
default_config.timeout_seconds = 3600  # 1 hour

Memory issues with large datasets

  • Use streaming for large files
  • Process data in chunks
  • Increase system memory allocation

Getting Help

For issues or questions:

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Biomni AI skill do?

Autonomous biomedical AI agent framework for executing complex research tasks across genomics, drug discovery, molecular biology, and clinical analysis. Use this skill when conducting multi-step biomedical research including CRISPR screening design, single-cell RNA-seq analysis, ADMET prediction, GWAS interpretation, rare disease diagnosis, or lab protocol optimization. Leverages LLM reasoning with code execution and integrated biomedical databases.

Why use Biomni on TypingMind?

Because you install it once and use it with any model. Biomni is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Biomni in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jimmc414/Kosmos/tree/master/kosmos-claude-scientific-skills/scientific-skills/biomni. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Biomni?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Biomni?

As many as you like. As long as a model supports skills, you can use Biomni with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Biomni AI skill free?

It is published on GitHub by jimmc414. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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