lamm-mit/bioreason_pro
skills/bioreason_pro/SKILL.md
Multimodal reasoning LLM for protein function prediction integrating protein embeddings with biological context to generate structured reasoning traces and functional annotations.
$ install --global
skillsauthnpx skillsauth add lamm-mit/scienceclaw bioreason_proInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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SKILL.md
- name:
- bioreason_pro
- description:
- Multimodal reasoning LLM for protein function prediction integrating protein embeddings with biological context to generate structured reasoning traces and functional annotations.
- source_type:
- github
- auth_required:
- false
- repository_url:
- https://github.com/bowang-lab/BioReason-Pro
- reference_url:
- https://www.biorxiv.org/content/10.64898/2026.03.19.712954v1
bioreason_pro
Multimodal reasoning LLM for protein function prediction integrating protein embeddings with biological context to generate structured reasoning traces and functional annotations.
Code repository
https://github.com/bowang-lab/BioReason-Pro
Use this as the implementation source: clone the repo and follow its README for install, dependencies, and how to run code or experiments. The generated client prints JSON with a suggested git clone command.
Primary resource (landing page)
https://www.biorxiv.org/content/10.64898/2026.03.19.712954v1
This is the paper or artifact home from DOI/registry metadata — not a JSON API. If this URL is arXiv, the generated client can still fetch live Atom metadata (title, abstract, authors) without a BASE_URL. For other hosts, the client uses stub mode until you set a real BASE_URL for a REST service.
What “running” this client does
The *_client.py script prints JSON that combines a GitHub repository (clone URL + suggested git clone) with optional paper context from arXiv (live Atom metadata when reference_url is arXiv). Run the real code by cloning the repo and following its README — the skill is your agent-facing entrypoint, not a substitute for the repo’s install steps.
To call a REST API instead, set BASE_URL in scripts/bioreason_pro_client.py or wrap the upstream CLI with subprocess after clone.
How to run the method (from the source)
Extracted for operators and agents. Confirm against the upstream repository or paper before relying on it in production.
Prerequisites
- Python 3.11+
- CUDA/GPU for best performance
Installation
# Clone the repository
git clone https://github.com/bowang-lab/BioReason-Pro.git
cd BioReason-Pro
# Install package
pip install -e .
How to run
The README does not document specific CLI commands for running inference or training. However, the project provides:
-
Web Interface: Try BioReason-Pro directly at bioreason.net
-
Precomputed Predictions: Access 240,000+ precomputed protein predictions at bioreason.net/atlas
-
Model Checkpoints: Available on HuggingFace collection (wanglab/bioreason-pro)
- GO-GPT: https://huggingface.co/wanglab/gogpt
- BioReason-Pro SFT: https://huggingface.co/wanglab/bioreason-pro-sft
- BioReason-Pro RL: https://huggingface.co/wanglab/bioreason-pro-rl
-
Datasets: Training and evaluation datasets available at HuggingFace collection with detailed download and usage instructions
Configuration
The README does not document specific environment variables, API keys, or configuration parameters. For detailed usage instructions on running inference or training, refer to the repository's code or documentation at https://github.com/bowang-lab/BioReason-Pro or the project website at https://bioreason.net.
The same text lives in scripts/USAGE.md for tools that prefer reading files under scripts/.
Parameters
Usage
python3 scripts/bioreason_pro_client.py
Example Output
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