bionemo-agent-toolkit
NewTurn any agent into a life science expert with NVIDIA BioNeMo skills.
Summary
This skill equips any Claude Code agent with NVIDIA BioNeMo capabilities, enabling it to access and analyze life science data, run molecular simulations, and retrieve biomedical insights.
- It transforms a general-purpose agent into a specialized life science expert, making it valuable for researchers and developers working on drug discovery, genomics, or bioinformatics.
Install & Usage
mkdir -p .claude/agentsAdd the configuration to .claude/agents/bionemo-agent-toolkit.md
@bionemo-agent-toolkitUse Cases
Usage Examples
/bionemo-agent-toolkit fetch protein structure for BRCA1
/bionemo-agent-toolkit run molecular docking with ligand 'aspirin' on target 'COX-2'
Use BioNeMo to generate embeddings for these SMILES strings: CCO, CCCO, C1=CC=CC=C1
Security Audits
Frequently Asked Questions
What is bionemo-agent-toolkit?
This skill equips any Claude Code agent with NVIDIA BioNeMo capabilities, enabling it to access and analyze life science data, run molecular simulations, and retrieve biomedical insights. It transforms a general-purpose agent into a specialized life science expert, making it valuable for researchers and developers working on drug discovery, genomics, or bioinformatics.
How to install bionemo-agent-toolkit?
To install bionemo-agent-toolkit: create the agents directory (mkdir -p .claude/agents), then add the config to .claude/agents/bionemo-agent-toolkit.md. Finally, @bionemo-agent-toolkit in Claude Code.
What is bionemo-agent-toolkit best for?
bionemo-agent-toolkit is a agent categorized under General. It is designed for: agent. Created by NVIDIA-BioNeMo.
What can I use bionemo-agent-toolkit for?
bionemo-agent-toolkit is useful for: A researcher uses the skill to fetch protein structure data from BioNeMo for a specific gene target.; A developer integrates molecular docking simulations into an automated drug screening pipeline.; A bioinformatician queries BioNeMo for sequence alignment and variant analysis on a genomic dataset.; A scientist generates molecular embeddings for a set of compounds to predict their properties.; A team automates the retrieval of latest biomedical literature and annotations for a disease pathway.; A developer uses the skill to run a pre-trained BioNeMo model for protein-ligand interaction prediction..