The first bioinformatics-native AI agent skill library.
Built on OpenClaw (180k+ GitHub stars). Local-first. Privacy-focused. Reproducible.
14 skills and growing. Local-first. No cloud. No guessing.
Snap a photo of a medication in Telegram. ClawBio identifies the drug from the packaging, queries your pharmacogenomic profile from your own genome, and returns a personalised dosage card — on your machine, in seconds:
Warfarin | CYP2C9 *1/*2 Intermediate · VKORC1 High Sensitivity AVOID — DO NOT USE · Standard dose causes over-anticoagulation in this genotype.
Or take any genetic variant (identified by its rsID — a unique label like rs9923231) and search nine genomic databases at once to find every known disease association, tissue-specific effect, and population frequency. Or estimate your genetic predisposition to conditions like type 2 diabetes by combining thousands of small-effect variants into a single polygenic risk score. Or explore the UK Biobank — a half-million-person research dataset — by asking in plain English what fields measure blood pressure, grip strength, or depression, and get back the exact field IDs, descriptions, and linked publications you need.
Every result ships with a reproducibility bundle: commands.sh, environment.yml, and SHA-256 checksums. A reviewer can reproduce your Figure 3 in 30 seconds without emailing you.
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 requests to the right skill automatically |
| PharmGx Reporter | MVP | 12 genes, 51 drugs, CPIC guidelines from consumer genetic data |
| Drug Photo | MVP | Snap a medication photo → personalised dosage card from your genotype |
| ClinPGx | MVP | Gene-drug lookup from ClinPGx, PharmGKB, CPIC, and FDA drug labels |
| GWAS Lookup | MVP | Federated variant query across 9 genomic databases |
| GWAS PRS | MVP | Polygenic risk scores from the PGS Catalog for 6+ traits |
| Profile Report | MVP | Unified personal genomic report: PGx + ancestry + PRS + nutrigenomics |
| UKB Navigator | MVP | Semantic search across the UK Biobank schema |
| Equity Scorer | MVP | HEIM diversity metrics from VCF or ancestry CSV |
| NutriGx Advisor | MVP (community) | Personalised nutrigenomics — 40 SNPs, 13 dietary domains |
| Metagenomics Profiler | MVP | Kraken2 / RGI / HUMAnN3 taxonomy, resistome, and functional profiles |
| Ancestry PCA | MVP | PCA vs SGDP (345 samples, 164 populations) with confidence ellipses |
| Semantic Similarity | MVP | Semantic Isolation Index from 13.1M PubMed abstracts |
| Genome Comparator | MVP | Pairwise IBS vs George Church (PGP-1) + ancestry estimation |
| VCF Annotator | Planned | Variant annotation with VEP, ClinVar, gnomAD |
| Lit Synthesizer | Planned | PubMed/bioRxiv search with LLM summarisation and citation graphs |
| scRNA Orchestrator | MVP | Scanpy automation: QC, clustering, marker 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, including gzip)
- 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.
Snap a photo of any medication in Telegram. ClawBio identifies the drug from the packaging and returns a personalised dosage card against your own genotype.
- Claude vision extracts drug name and visible dose from the photo
- Cross-references your 23andMe genotype against 31 PGx SNPs
- Four-tier classification: STANDARD DOSING / USE WITH CAUTION / AVOID / INSUFFICIENT DATA
- Correct VKORC1 complement-strand handling (23andMe reports minus strand for rs9923231)
- Works for warfarin, clopidogrel, codeine, simvastatin, tamoxifen, sertraline, and 20+ others
python pharmgx_reporter.py --drug warfarin --dose "5mg" --input my_23andme.txt --output reportNo command needed in Telegram — send any medication photo and RoboTerri triggers the skill automatically.
Federated variant query across nine genomic databases in a single command:
| Database | What you get |
|---|---|
| GWAS Catalog | Genome-wide significant associations |
| gnomAD | Allele frequencies across 125,748 exomes |
| ClinVar | Clinical significance and condition links |
| Open Targets | Disease-gene evidence scores |
| Ensembl | Functional annotation, regulatory impact |
| GTEx | eQTL data, tissue-specific expression effects |
| LDlink | Linkage disequilibrium across 26 populations |
| UK Biobank PheWAS | Phenome-wide associations across 4,000+ traits |
| LOVD | Variant pathogenicity database |
python gwas_lookup.py --rsid rs3798220 --output report
python gwas_lookup.py --demo --output /tmp/gwas_lookup_demoSemantic search across the UK Biobank schema. Ask in plain English what UK Biobank measures about any phenotype — get field IDs, descriptions, data types, participant counts, and linked publications back instantly.
python ukb_navigator.py --query "grip strength" --output report
python ukb_navigator.py --field 21001 --output report # BMI
python ukb_navigator.py --demo --output /tmp/ukb_demoBuilt on a ChromaDB embedding of the full UKB Data Showcase (22,000+ fields).
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.
git clone https://github.com/ClawBio/ClawBio.git && cd ClawBio
pip install -r requirements.txt
python clawbio.py run pharmgx --demoPharmGx demo runs in <2 seconds. Only needs Python 3.10+.
python clawbio.py list # See available skills
python clawbio.py run pharmgx --demo # Pharmacogenomics (1s)
python clawbio.py run equity --demo # Equity scoring (55s)
python clawbio.py run nutrigx --demo # Nutrigenomics (60s)
python clawbio.py run metagenomics --demo # Metagenomics (3s)
python clawbio.py run scrna --demo # scRNA clustering + marker detection
python clawbio.py run compare --demo # Manuel Corpas vs George Church (10s)
python clawbio.py run gwas-lookup --demo # rs3798220 across 9 databases (5s)
python clawbio.py run prs --demo # Polygenic risk scores (10s)
python clawbio.py run ukb-navigator --demo # UK Biobank schema search (5s)
python clawbio.py run profile --demo # Unified genomic profile (30s)python clawbio.py run pharmgx --input my_23andme.txt --output results/pip install pytest
python -m pytest
RoboTerri — ClawBio's Telegram agent, inspired by Prof. Teresa K. Attwood
ClawBio skills are also available through RoboTerri, a Telegram AI agent named after Prof. Teresa K. Attwood — a pioneer of bioinformatics education, founding Chair of GOBLET, and winner of the 2021 ISCB Outstanding Contributions Award. Send a genetic data file, a medication photo, or a plain-English question. Get back a summary, full report, and figures directly in Telegram.
Install RoboTerri — Step-by-step tutorial: Set up your own Telegram bot running ClawBio skills in ~20 minutes.
You: [send 23andMe file]
RoboTerri: Running PharmGx Reporter...
CYP2D6 *4/*4 — Poor Metabolizer → 10 drugs AVOID
[report.md attached]
[3 figures attached]
You: [send photo of warfarin packet]
RoboTerri: Warfarin detected. Running Drug Photo skill...
CYP2C9 *1/*2 · VKORC1 High Sensitivity
AVOID — DO NOT USE at standard dose.
You: run gwas-lookup rs3798220
RoboTerri: Querying 9 databases...
rs3798220 (LPA) — coronary artery disease, Lp(a) levels.
eQTL in liver (GTEx). gnomAD MAF 0.07.
RoboTerri auto-detects file type (23andMe .txt, AncestryDNA .csv, VCF, FASTQ) and routes to the right skill via the Bio Orchestrator. Photos of medications trigger the Drug Photo skill automatically — no command needed.
Telegram (RoboTerri) CLI (clawbio.py) Python (import clawbio)
│ │ │
└──────────┬───────────┘───────────────────────┘
│
┌──────▼──────┐
│ Bio │ ← routes by file type + keywords
│ Orchestrator│
└──────┬──────┘
│
┌─────────────────▼──────────────────────────────────────┐
│ │
PharmGx Equity NutriGx Metagenomics Ancestry
Reporter Scorer Advisor Profiler PCA ...
│ │
└─────────────────┬──────────────────────────────────────┘
│
┌──────▼──────┐
│ Markdown │ ← report + figures + checksums
│ Report │ + reproducibility bundle
└─────────────┘
Each skill is standalone — the orchestrator routes to the right one, but every skill also works independently. The clawbio.run_skill() API is importable by any agent (RoboTerri, RoboIsaac, Claude Code).
See docs/architecture.md for the full design.
ClawBio is designed to be discovered and used by AI coding agents, not just humans.
| Resource | Purpose |
|---|---|
llms.txt |
Token-optimized project summary for any LLM (llmstxt.org standard) |
AGENTS.md |
Universal guide for AI coding agents — setup, commands, style, structure, git workflow |
CLAUDE.md |
Claude-specific routing table, CLI reference, demo commands, safety rules |
skills/catalog.json |
Machine-readable skill index with trigger keywords, chaining partners, and demo commands |
Agents can also run python clawbio.py list to discover available skills programmatically.
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-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 is built on OpenClaw. On 1 March 2026, at the UK AI Agent Hack at Imperial College London, Manuel Corpas introduced ClawBio to Peter Steinberger — the creator of OpenClaw itself.
Manuel Corpas introduces ClawBio to Peter Steinberger · UK AI Agent Hack, Imperial College London · Watch on YouTube →
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: clawbio.github.io/ClawBio/slides/
- 🦖 Tutorial: Install RoboTerri (Telegram agent)
- 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. 🦖
