Best ClawHub Skills for Healthcare AI Teams

OpenClaw passed 346,000 GitHub stars in April 2026, making it the most-starred software project on GitHub. Healthcare organizations noticed. The framework's skill-based architecture lets teams add clinical reasoning, literature search, and genomics pipelines to an AI agent by dropping a SKILL.md file into a directory. No fine-tuning, no custom model training.

The appeal is practical. A research team that needs PubMed search, variant annotation, and FDA regulatory guidance can install three skills and have a working agent in an afternoon. Compare that to building custom API integrations for each database, and the time savings are obvious.

But there is a serious catch. OpenClaw was not designed for regulated environments. Security researchers at Bitsight found over 30,000 publicly exposed OpenClaw instances in early 2026. Oasis Security documented a vulnerability chain that allowed any website to silently take full control of an agent. The ClawHavoc campaign planted 341 malicious skills on ClawHub in a single weekend.

Healthcare teams should treat OpenClaw skills as powerful research accelerators, not production-ready clinical tools. Every skill recommendation below includes compliance context so you can make informed decisions about what belongs in your workflow.

FreedomIntelligence's OpenClaw-Medical-Skills is the largest open-source medical AI skills library, with 869 curated skills aggregated from 12+ repositories. It covers clinical documentation, genomics, drug discovery, bioinformatics, regulatory compliance, and medical devices.

The library breaks down into eight categories:

• General and Core: 10 skills
• Medical and Clinical: 119 skills
• Scientific Databases: 43 skills
• Bioinformatics (gptomics): 239 skills
• Omics and Computational Biology: 59 skills
• ClawBio Pipelines: 21 skills
• BioOS Extended Suite: 285 skills
• Data Science and Tools: 93 skills

Installation uses git sparse-checkout to avoid downloading large data files, then points OpenClaw to the cloned directory. You can also install selectively by category if you only need clinical or genomics skills.

Best For: Teams that want broad medical coverage without assembling skills from dozens of separate repositories.

Source: FreedomIntelligence/OpenClaw-Medical-Skills on GitHub

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Clinical documentation is where most healthcare teams start with OpenClaw skills, because the workflow is familiar and the risk profile is lower than diagnostic tasks.

clinical-reports

Generates structured clinical documents including case reports, diagnostic reports, clinical trial reports, and patient summaries. The skill follows ICH-GCP formatting standards and includes templates for SOAP notes and discharge summaries. Useful for reducing documentation burden during research, though any output touching real patient data needs human review and proper de-identification.

prior-auth-review-skill

Automates the review side of prior authorization requests by assessing medical necessity criteria and generating draft PA decisions. Payer organizations use this to triage incoming requests before human review. It does not replace clinical judgment, but it can flag straightforward approvals and route complex cases to specialists faster.

pubmed-search

Queries PubMed for peer-reviewed biomedical literature using natural language. This is one of the most broadly useful medical skills because it works for any team that needs to stay current on research. Ask the agent "find recent studies on GLP-1 receptor agonists and cardiovascular outcomes" and it returns structured results with citations.

clinicaltrials-database

Searches ClinicalTrials.gov by condition, drug, location, status, or phase. Research teams use this for competitive intelligence, patient recruitment planning, and identifying gaps in the trial landscape. The skill handles the ClinicalTrials.gov API so the agent can answer questions like "how many Phase 3 trials for NASH are currently recruiting in the US?"

fhir-developer-skill

A development guide for building FHIR REST API endpoints covering Patient, Observation, Encounter, Condition, and MedicationRequest resources. This is less of an autonomous agent skill and more of a coding assistant that understands the HL7 FHIR specification. Useful when your team is building healthcare data integrations and needs the agent to generate compliant endpoint code.

The genomics category is where OpenClaw medical skills get technically deep. ClawBio, a dedicated bioinformatics skill library built on OpenClaw, provides 63 skills plus access to 8,000 Galaxy bioinformatics tools through its Galaxy Bridge integration.

ClawBio Core Skills

ClawBio runs local-first, which matters for genomic data. Your sequences never leave your machine. Every analysis exports a reproducibility bundle with commands, environment details, and SHA-256 checksums so results can be verified without the agent.

Key ClawBio capabilities include GWAS lookup across 9 genomic databases (GWAS Catalog, Open Targets, PheWeb for UK Biobank, FinnGen, and Biobank Japan, GTEx, eQTL Catalogue), polygenic risk scoring, pharmacogenomics analysis, and ancestry PCA. The federated variant lookup is particularly valuable because it queries multiple databases in a single call rather than requiring separate API integrations.

Source: ClawBio on GitHub

variant-calling and structural-variant-analysis

From the OpenClaw-Medical-Skills library, these skills handle VCF annotation and ACMG variant classification. The variant-calling skill processes VCF files and annotates them against ClinVar, gnomAD, and COSMIC databases. The structural-variant-analysis skill identifies larger genomic rearrangements. Both produce structured output that feeds into downstream clinical interpretation.

genome-compare Compares two genomic datasets to identify differences, shared variants, and population-level patterns. Research teams use this for case-control comparisons and family trio analyses. The skill outputs a structured report highlighting clinically significant variants.

rnaseq-deseq2 and single-cell analysis The bioinformatics pipeline skills cover RNA-seq differential expression analysis using DESeq2, scRNA-seq workflows, and FASTQ quality control. These turn an OpenClaw agent into a bioinformatics workbench that can process sequencing data end-to-end, from raw reads through statistical analysis.

Drug discovery skills connect OpenClaw agents to chemical databases, interaction predictors, and safety monitoring tools. These are primarily useful for preclinical research teams and pharmacovigilance departments.

tooluniverse-drug-research

Provides structured access to ChEMBL and DrugBank for compound search, target identification, and bioactivity data retrieval. The skill handles the complexity of querying multiple chemical databases and returns unified results. A medicinal chemistry team can ask "find all known inhibitors of EGFR with IC50 below 10 nM" and get structured, citable results.

tooluniverse-pharmacovigilance

Monitors drug safety signals by querying adverse event databases and literature. Pharmacovigilance teams use this to surface emerging safety concerns faster than manual monitoring. The skill can correlate adverse event reports with published case studies and mechanistic hypotheses.

tooluniverse-drug-repurposing

Identifies existing approved drugs that might work for new indications based on target similarity, pathway analysis, and published evidence. Drug repurposing is one of the more promising applications of AI in pharma because the safety data already exists for approved compounds.

binder-discovery

From the LabClaw ecosystem, this skill supports protein binder identification and molecular interaction prediction. It connects target discovery with experimental design, helping researchers identify promising binding candidates before committing to wet lab work.

These drug discovery skills work best when combined with the literature search skills (pubmed-search, medical-research-toolkit) so the agent can cross-reference computational predictions against published evidence.

This is the section most ClawHub healthcare guides skip, and it is the most important one.

OpenClaw cannot sign a Business Associate Agreement. The core project has no built-in PHI detection or redaction. Audit logging exists but requires significant configuration to meet HIPAA's access tracking requirements. The community version of OpenClaw is not a HIPAA-ready system.

The security picture adds urgency. In January 2026, the ClawHavoc campaign uploaded 341 malicious skills to ClawHub, deploying keyloggers and Atomic Stealer malware. ClawHub responded by removing 2,419 suspicious skills total and partnering with VirusTotal for automatic malware scanning. Google's Gemini-powered analysis now runs on every published skill.

What healthcare teams should do

Vet every skill before installation. Read the SKILL.md file. Look for suspicious prerequisite steps, obfuscated code, or base64-encoded commands. Use clawscan or skill-scanner for automated security checks. Follow the 100/3 rule: prefer skills with 100+ downloads and at least 3 months on ClawHub.

Isolate the agent. Run OpenClaw in a sandboxed workspace that limits file system access. Never give an agent access to directories containing PHI, production databases, or credentials. Use a dedicated machine or container, not a workstation with access to clinical systems.

Keep PHI out of the agent context. Use OpenClaw skills for research tasks (literature search, genomics analysis on de-identified data, regulatory guidance) rather than clinical workflows involving patient records. If you need to process documents containing PHI, use a purpose-built system with proper access controls and audit trails.

Use local models for sensitive work. OpenClaw supports local LLM backends. For any analysis involving sensitive data, run inference locally rather than routing through cloud APIs. This eliminates the data-in-transit risk, though it requires hardware investment (8+ CPU cores, 16-32GB RAM, 24GB+ VRAM for clinical workloads).

For document processing and structured data extraction from medical files, Fast.io's Metadata Views can extract fields from PDFs, scanned pages, and handwritten notes using natural language schema definitions, with proper access controls and audit logging built in.

The most effective approach for healthcare teams combines OpenClaw's research skills with a proper storage and collaboration layer. Here is a practical architecture.

Research layer (OpenClaw): Install skills for literature search, genomics analysis, and drug discovery. Use these for non-PHI research tasks where the agent accelerates work that would otherwise take hours of manual database queries.

Storage and handoff layer: Agent-generated research reports, analysis results, and literature summaries need a permanent home where the rest of the team can access them. Local file systems work for solo researchers, but teams need shared workspaces with version control and access management.

Fast.io workspaces handle this coordination. Agents connect through the MCP server using 19 consolidated tools for file upload, search, and sharing. Intelligence Mode auto-indexes uploaded documents for semantic search, so a research report the agent generates today is findable by keyword or meaning tomorrow. The free tier includes 50GB storage, 5,000 credits per month, and 5 workspaces with no credit card required.

Human review layer: Every agent output that might influence a clinical decision gets human review before acting on it. Fast.io's approval workflows let you build review gates directly into the handoff process. The agent uploads a drug interaction analysis, a pharmacist reviews and approves it, and only then does it enter the team's reference library.

Audit trail: Both OpenClaw and your storage layer should log what the agent accessed and produced. Fast.io's audit trails track file access, modifications, and sharing events. For OpenClaw, the HIPAA-compliance skill adds structured access logging, though it requires manual configuration.

This separation keeps research velocity high while maintaining the compliance boundaries that healthcare work demands. The agent accelerates discovery. The storage layer provides accountability. The human review layer provides safety.