The first bioinformatics-native AI agent skill library.
Built on OpenClaw (180k+ GitHub stars). Local-first. Privacy-focused. Reproducible.
A community contributor built a nutrigenomics skill and ran it — from raw genetic data to personalised nutrition report with radar charts, heatmaps, and reproducibility bundle:
https://github.com/ClawBio/ClawBio/releases/download/v0.2.0/david-nutrigx-demo.mp4
What just happened behind the scenes
- The AI agent read
SKILL.md— a specification that encodes the correct bioinformatics decisions (40 SNPs, 13 nutrient domains, evidence-based risk thresholds) - It ran the Python skill locally — no genetic data left the machine
- It produced a markdown report with figures, tables, and a reproducibility bundle (
commands.sh,environment.yml,checksums.sha256) - Anyone can re-run the exact same analysis and get identical results, SHA-256 verified
PharmGx Reporter: 12 genes, 51 drugs, under 1 second
You read a paper. You want to reproduce Figure 3. So you:
- Go to GitHub. Clone the repo.
- Wrong Python version. Fix dependencies.
- Need the reference data — where is it?
- Download 2GB from Zenodo. Link is dead.
- Email the first author. Wait 3 weeks.
- Paths are hardcoded to
/home/jsmith/data/. - 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.
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.
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- It hallucinates star allele calls and uses outdated CPIC guidelines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.
Every ClawBio analysis ships with a reproducibility bundle — not as an afterthought, but as part of the output:
report/
├── report.md # Full analysis with figures and tables
├── figures/ # Publication-quality PNGs
├── tables/ # CSV data tables
├── commands.sh # Exact commands to reproduce
├── environment.yml # Conda environment snapshot
└── checksums.sha256 # SHA-256 of every input and output file
Why this matters: a reviewer can re-run your analysis in 30 seconds. A collaborator can reproduce your Figure 3 without emailing you. Future-you can regenerate results two years later from the same bundle.
| 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 |
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 reportDemo 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.
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_reportDemo 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.
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_reportKey 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.
- OpenClaw installed and configured
- Python 3.9+
- Bioinformatics tools for your skill of choice (see individual SKILL.md files)
# 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 reportEvery skill includes demo data so you can try it immediately without your own files.
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.
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.
ClawBio was announced at the London Bioinformatics Meetup on 26 February 2026.
- Slides: clawbio.github.io/ClawBio/slides/
- Talk: 10 Tips for Becoming a Top 1% AI User — with live demos of all three MVP skills
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/ClawBio/ClawBio}
}- 🦖 Slides: manuelcorpas.github.io/ClawBio/slides/
- OpenClaw — The agent platform
- ClawHub — Skill registry
- HEIM Index — Health Equity Index for Minorities
MIT — clone it, run it, build a skill, submit a PR. 🦖