ClawBio/ClawBio

What it solves

ClawBio provides a standardized way to execute complex bioinformatics workflows using AI agents. It solves the problem of "stochastic" AI code generation—where LLMs might hallucinate scientific guidelines or use outdated methods—by replacing ad-hoc prompts with a library of versioned, peer-reviewable "skills." These skills ensure that domain expertise (such as pharmacogenomic guidelines) is encoded as a machine-readable contract rather than relying on the model's training data.

How it works

ClawBio uses a "specification-first" approach. Each skill is a self-contained directory containing a declarative specification (SKILL.md), validated Python code, demo data, and reproducibility bundles (including shell commands and environment snapshots). This allows an AI agent to orchestrate the analysis without improvising the scientific logic. The system is local-first, meaning data is processed on the user's machine by default, and it is agent-agnostic, working across different LLMs (Claude, ChatGPT, Ollama).

Who it’s for

It is designed for bioinformaticians, genetic researchers, and clinicians who need reproducible, verifiable AI-assisted genomic analysis without compromising data privacy or scientific accuracy.

Highlights

• Extensive Skill Library: Includes 96 skills covering personal pharmacogenomics, population GWAS lookups, clinical variant reporting, and single-cell RNA-seq wrappers.
• Reproducibility Bundles: Exports replay commands, environment metadata, and checksums to allow results to be verified without the original AI session.
• Local-First Privacy: Processes genomic data locally to ensure privacy, with explicit documentation of any skills that require external API calls.
• Validation Infrastructure: Features a benchmark suite with ground truth data and mock API servers to score skill accuracy (e.g., gene recovery rates and F1 scores).
• Genomebook Sandbox: A synthetic genetics environment for modeling Mendelian inheritance and breeding fictional character profiles.