🦖 ClawBio

The first bioinformatics-native AI agent skill library.
Built on OpenClaw (180k+ GitHub stars). Local-first. Privacy-focused. Reproducible.

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The Problem

You read a paper. You want to reproduce Figure 3. So you:

1. Go to GitHub. Clone the repo.
2. Wrong Python version. Fix dependencies.
3. Need the reference data — where is it?
4. Download 2GB from Zenodo. Link is dead.
5. Email the first author. Wait 3 weeks.
6. Paths are hardcoded to /home/jsmith/data/.
7. Two days later: still broken. You give up.

Now imagine the same paper published a skill:

python ancestry_pca.py --demo --output fig3
# Figure 3 reproduced. Identical. SHA-256 verified. 30 seconds.

That's ClawBio. Every figure in your paper should be one command away from reproduction.

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🦖 What Is ClawBio?

A skill is a domain expert's knowledge — frozen into code — that an AI agent executes correctly every time.

ChatGPT / Claude  = a smart generalist who guesses at bioinformatics
🦖 ClawBio skill  = a domain expert's proven pipeline that the AI executes

• Local-first: Your genomic data never leaves your laptop. No cloud uploads, no data exfiltration.
• Reproducible: Every analysis exports commands.sh, environment.yml, and SHA-256 checksums. Anyone can reproduce it without the agent.
• Modular: Each skill is a self-contained directory (SKILL.md + Python scripts) that plugs into the orchestrator.
• MIT licensed: Open-source, free, community-driven.

Why Not Just Use ChatGPT?

Ask Claude to "profile my pharmacogenes from this 23andMe file." It'll write plausible Python. But:

• It hallucinates star allele calls and uses outdated CPIC guidelines
• It forgets CYP2D6 *4 is no-function (not reduced)
• You spend 45 minutes debugging its output
• No reproducibility bundle. No audit log. No checksums.

ClawBio encodes the correct bioinformatics decisions so the agent gets it right first time, every time.

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🦖 Skills

Skill	Status	Description
Bio Orchestrator	MVP	Routes bioinformatics requests to the right specialist skill
PharmGx Reporter	MVP	Pharmacogenomic report: 12 genes, 51 drugs, CPIC guidelines
Ancestry PCA	MVP	PCA decomposition vs SGDP (345 samples, 164 global populations)
Semantic Similarity	MVP	Semantic Isolation Index for 175 GBD diseases from 13.1M PubMed abstracts
Equity Scorer	Planned	HEIM diversity metrics from VCF/ancestry data
VCF Annotator	Planned	Variant annotation with VEP, ClinVar, gnomAD + ancestry context
Lit Synthesizer	Planned	PubMed/bioRxiv search with LLM summarisation and citation graphs
scRNA Orchestrator	Planned	Scanpy automation: QC, clustering, DE analysis, visualisation
Struct Predictor	Planned	AlphaFold/Boltz local structure prediction
Repro Enforcer	Planned	Export any analysis as Conda env + Singularity + Nextflow pipeline

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🦖 MVP Skills in Detail

PharmGx Reporter — Personal Scale

Generates a pharmacogenomic report from consumer genetic data (23andMe, AncestryDNA):

• Parses raw genetic data (auto-detects format)
• Extracts 31 pharmacogenomic SNPs across 12 genes (CYP2C19, CYP2D6, CYP2C9, VKORC1, SLCO1B1, DPYD, TPMT, UGT1A1, CYP3A5, CYP2B6, NUDT15, CYP1A2)
• Calls star alleles and determines metabolizer phenotypes
• Looks up CPIC drug recommendations for 51 medications
• Zero dependencies. Runs in < 1 second.

python pharmgx_reporter.py --input demo_patient.txt --output report

Demo result: CYP2D6 *4/*4 (Poor Metabolizer) → 10 drugs AVOID (codeine, tramadol, 7 TCAs, tamoxifen), 20 caution, 21 standard.

~7% of people are CYP2D6 Poor Metabolizers — codeine gives them zero pain relief. ~0.5% carry DPYD variants where standard 5-FU dose can be lethal. This skill catches both.

Ancestry PCA — Population Scale

Runs principal component analysis on your cohort against the SGDP reference panel (345 samples, 164 global populations):

• Contig normalisation (chr1 vs 1)
• IBD removal (related individuals filtered)
• Common biallelic SNPs only
• Confidence ellipses per population
• Publication-quality 4-panel figure generated instantly

python ancestry_pca.py --demo --output ancestry_report

Demo result: 736 Peruvian samples across 28 indigenous populations. Amazonian groups (Matzes, Awajun, Candoshi) sit in genetic space that no SGDP population occupies — genuinely underrepresented, not just in GWAS, but in the reference panels themselves.

Semantic Similarity Index — Systemic Scale

Computes a Semantic Isolation Index for diseases using 13.1M PubMed abstracts and PubMedBERT embeddings (768-dim):

• SII (Semantic Isolation Index): higher = more isolated in literature
• KTP (Knowledge Transfer Potential): higher = more cross-disease spillover
• RCC (Research Clustering Coefficient): diversity of research approaches
• Temporal Drift: how research focus evolves over time
• Publication-quality 4-panel figure

python semantic_sim.py --demo --output sem_report

Key finding: Neglected tropical diseases are +38% more semantically isolated (P < 0.0001, Cohen's d = 0.84). 14 of the 25 most isolated diseases are Global South priority conditions. Knowledge silos kill innovation — a malaria immunology breakthrough could help leishmaniasis, but the literatures don't talk to each other.

Corpas et al. (2026). HEIM: Health Equity Index for Measuring structural bias in biomedical research. Under review.

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Quick Start

Prerequisites

• OpenClaw installed and configured
• Python 3.9+
• Bioinformatics tools for your skill of choice (see individual SKILL.md files)

Install and run

# Install a skill
openclaw install skills/pharmgx-reporter

# Run with natural language
openclaw "Profile the pharmacogenes in my 23andMe file at data/raw_genotype.txt"

# Or run directly
python skills/pharmgx-reporter/pharmgx_reporter.py --input data/raw_genotype.txt --output report

Every skill includes demo data so you can try it immediately without your own files.

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🦖 Architecture

User: "Analyse the diversity in my VCF file"
         │
  ┌──────▼──────┐
  │  Bio         │  ← routes by file type + keywords
  │  Orchestrator│
  └──────┬──────┘
         │
  ┌──────▼──────────────────────────────────────────┐
  │                                                  │
  PharmGx    Ancestry    Semantic    Equity    VCF
  Reporter   PCA         Similarity  Scorer    Annotator ...
  │                                                  │
  └──────┬──────────────────────────────────────────┘
         │
  ┌──────▼──────┐
  │  Markdown    │  ← report + figures + checksums
  │  Report      │     + reproducibility bundle
  └─────────────┘

Each skill is standalone — the orchestrator routes to the right one, but every skill also works independently.

See docs/architecture.md for the full design.

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Community Wanted Skills 🦖

We want skills from the bioinformatics community. If you work with genomics, proteomics, metabolomics, imaging, or clinical data — wrap your pipeline as a skill.

Skill	What	Your expertise
claw-gwas	PLINK/REGENIE automation	Statistical genetics
claw-metagenomics	Kraken2/MetaPhlAn wrapper	Microbiome
claw-acmg	Clinical variant classification	Clinical genomics
claw-pathway	GO/KEGG enrichment	Functional genomics
claw-phylogenetics	IQ-TREE/RAxML automation	Evolutionary biology
claw-proteomics	MaxQuant/DIA-NN	Proteomics
claw-spatial	Visium/MERFISH	Spatial transcriptomics

See CONTRIBUTING.md for the submission process and templates/SKILL-TEMPLATE.md for the skill template.

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Presentation

ClawBio was announced at the London Bioinformatics Meetup on 26 February 2026.

• Slides: manuelcorpas.github.io/ClawBio/slides/
• Talk: 10 Tips for Becoming a Top 1% AI User — with live demos of all three MVP skills

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Citation

If you use ClawBio in your research, please cite:

@software{clawbio_2026,
  author = {Corpas, Manuel},
  title = {ClawBio: An Open-Source Library of AI Agent Skills for Reproducible Bioinformatics},
  year = {2026},
  url = {https://github.com/manuelcorpas/ClawBio}
}

Links

• 🦖 Slides: manuelcorpas.github.io/ClawBio/slides/
• OpenClaw — The agent platform
• ClawHub — Skill registry
• HEIM Index — Health Equity Index for Minorities

License

MIT — clone it, run it, build a skill, submit a PR. 🦖