skill
MedClawMini
A focused, production-minded library of 197 clinical-AI and healthcare data-science skills for the OpenClaw agent platform featuring data quality, clinical NLP, big-data ML, explainable AI, drug safet
Set up
Skills are installed by placing their folder where your agent loads skills.
The download comes from github.com/rbr7/MedClawMini itself, not from our server, so what you get is exactly what its author published.
Install instructions come from the README
Skills do not use an MCP config. Follow the steps in the README below.
README
synced Oct 23, 2026MedClawMini
A focused, production-minded library of clinical-AI and healthcare data-science skills for OpenClaw.
197 curated skills · Data Quality · Clinical NLP · Big-Data ML · Explainable AI · Drug Safety · Regulatory
What Is MedClawMini?
MedClawMini is a compact, curated library of 197 agent skills for clinical AI and
applied healthcare data science. Each skill is a self-contained SKILL.md module that teaches
an OpenClaw agent a real workflow connecting to live databases and computational tools and
producing structured, auditable output.
The library is scoped to six domains and led by a purpose-built Healthcare Data-Science & Data-Quality suite: turning messy payer/provider and clinical data into trustworthy, analyzable, model-ready assets and building, explaining, and validating the models on top.
⭐ Healthcare Data-Science & Data-Quality Suite
Thirteen skills built around the core problems of real healthcare data work data quality, entity resolution, text mining, scalable ML, interpretability, and experimentation.
| Skill | What it does | DS capability |
|---|---|---|
healthcare-data-quality-profiling | Profile & score healthcare data across 6 DQ dimensions (completeness, validity, consistency, uniqueness, conformity, timeliness); detect schema drift; emit a reusable Great Expectations rule suite. | Data quality |
patient-record-entity-resolution | Link & deduplicate patient/provider/member records (MPI/EMPI) with blocking, probabilistic Fellegi-Sunter matching (Splink), and golden-record survivorship. | Entity resolution / linkage |
claims-anomaly-detection | Flag erroneous & outlier claims with robust statistics, Isolation Forest/LOF/autoencoders, time-series detection, and fraud-waste-abuse signals each flag explained. | Anomaly detection |
medical-ontology-code-mapping | Normalize free-text/coded data to ICD-10, CPT/HCPCS, SNOMED CT, RxNorm, NDC, LOINC and crosswalk between them via UMLS. | Ontology / terminology mapping |
clinical-nlp-entity-extraction | Clinical NER + negation/assertion + UMLS/SNOMED/RxNorm linking (medspaCy/scispaCy) to turn free-text notes into structured entity tables. | NLP entity extraction |
clinical-text-summarization | Extractive & abstractive summarization of clinical text (BART/PEGASUS/T5) with a built-in faithfulness/hallucination guard. | NLP summarization |
clinical-text-search-elk | Searchable clinical corpus on the ELK stack / OpenSearch with medical synonyms and hybrid BM25 + vector (kNN) retrieval. | Search / IR (ELK) |
snorkel-weak-supervision-labeling | Generate large training sets programmatically with Snorkel labeling functions and the LabelModel no manual annotation army. | Weak supervision (Snorkel) |
spark-healthcare-data-pipeline | Scalable claims/EHR ETL & point-in-time feature engineering with PySpark and Scala Spark on Hadoop/Delta Lake (billions of rows). | Big data (Spark/Scala) |
healthcare-predictive-modeling | Build & validate risk/cost/readmission models with leakage-safe grouped CV, bias-variance management, regularization, and probability calibration. | Predictive modeling / ML theory |
explainable-ml-healthcare | Interpret ML & deep-learning models with SHAP, LIME, Integrated Gradients, and Grad-CAM, plus subgroup fairness audits and model cards. | Explainable AI / interpretability |
ab-testing-healthcare | Design & analyze A/B tests power analysis, frequentist + Bayesian, sequential methods, CUPED variance reduction with safety guardrail metrics. | A/B testing / experimentation |
ml-model-validation-regulatory | Validate, document & monitor clinical ML for SaMD governance (GMLP, IEC 62304, ISO 14971) with drift monitoring and model cards. | Model validation / governance |
Skills Overview
| Category | Count | Focus |
|---|---|---|
| 🧰 General Tools | 15 | Web/search, document tooling, and the cross-cutting healthcare data-engineering & experimentation skills. |
| 🏥 Clinical & Medical | 103 | Clinical reports, decision support, literature & trials, imaging, mental health, and the clinical NLP / predictive-ML / explainability skills. |
| 💊 Drug Discovery & Safety | 44 | Drug-safety signals, cheminformatics, and AI-driven discovery/design agents. |
| 🩺 Health & Wellness | 15 | Patient-facing wellness, lifestyle, and population-health analytics. |
| 📋 Medical Device & Regulatory | 2 | Quality systems, regulatory documentation, and clinical-ML validation/governance. |
| ⚙️ Simulation & Ontology | 18 | Numerical simulation, HPC orchestration, and medical terminology/ontology mapping. |
| Total | 197 |
Skills by Category
🧰 General Tools (15)
Web/search, document tooling, and the cross-cutting healthcare data-engineering & experimentation skills.
| Skill | Description |
|---|---|
| ⭐ healthcare-data-quality-profiling | Profile & score healthcare data across 6 DQ dimensions (completeness, validity, consistency, uniqueness, conformity, timeliness); detect schema drift; emit a reusable Great Expectations rule suite. |
| ⭐ patient-record-entity-resolution | Link & deduplicate patient/provider/member records (MPI/EMPI) with blocking, probabilistic Fellegi-Sunter matching (Splink), and golden-record survivorship. |
| ⭐ claims-anomaly-detection | Flag erroneous & outlier claims with robust statistics, Isolation Forest/LOF/autoencoders, time-series detection, and fraud-waste-abuse signals each flag explained. |
| ⭐ spark-healthcare-data-pipeline | Scalable claims/EHR ETL & point-in-time feature engineering with PySpark and Scala Spark on Hadoop/Delta Lake (billions of rows). |
| ⭐ ab-testing-healthcare | Design & analyze A/B tests power analysis, frequentist + Bayesian, sequential methods, CUPED variance reduction with safety guardrail metrics. |
| agent-browser | Browse the web for any task research topics, read articles, interact with web apps, fill forms, take screenshots, extract data, and test web pages |
| find-skills | Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest… |
| multi-search-engine | "Multi search engine integration with 17 engines (8 CN + 9 Global) |
| wikipedia-search | Search and fetch structured content from Wikipedia using the MediaWiki API for reliable, encyclopedic information |
| deep-research | Execute autonomous multi-step deep research on any topic |
| Use this skill whenever the user wants to do anything with PDF files | |
| docx | "Use this skill whenever the user wants to create, read, edit, or manipulate Word documents (.docx files) |
| xlsx | "Use this skill any time a spreadsheet file is the primary input or output |
| pptx | "Use this skill any time a .pptx file is involved in any way as input, output, or both |
| doc-coauthoring | Guide users through a structured workflow for co-authoring documentation |
🏥 Clinical & Medical (103)
Clinical reports, decision support, literature & trials, imaging, mental health, and the clinical NLP / predictive-ML / explainability skills.
| Skill | Description |
|---|---|
| ⭐ clinical-nlp-entity-extraction | Clinical NER + negation/assertion + UMLS/SNOMED/RxNorm linking (medspaCy/scispaCy) to turn free-text notes into structured entity tables. |
| ⭐ clinical-text-summarization | Extractive & abstractive summarization of clinical text (BART/PEGASUS/T5) with a built-in faithfulness/hallucination guard. |
| ⭐ clinical-text-search-elk | Searchable clinical corpus on the ELK stack / OpenSearch with medical synonyms and hybrid BM25 + vector (kNN) retrieval. |
| ⭐ snorkel-weak-supervision-labeling | Generate large training sets programmatically with Snorkel labeling functions and the LabelModel no manual annotation army. |
| ⭐ healthcare-predictive-modeling | Build & validate risk/cost/readmission models with leakage-safe grouped CV, bias-variance management, regularization, and probability calibration. |
| ⭐ explainable-ml-healthcare | Interpret ML & deep-learning models with SHAP, LIME, Integrated Gradients, and Grad-CAM, plus subgroup fairness audits and model cards. |
| pubmed-search | Search PubMed for scientific literature |
| medical-research-toolkit | Query 14+ biomedical databases for drug repurposing, target discovery, clinical trials, and literature research |
| medical-specialty-briefs | Generate daily or on-demand medical research briefs for any medical specialty |
| usmle | Prepare for US medical licensing exams with progress tracking, weak area analysis, question bank management, and residency match planning. |
| medical-entity-extractor | Extract medical entities (symptoms, medications, lab values, diagnoses) from patient messages. |
| patiently-ai | Patiently AI simplifies medical documents for patients |
| biomedical-search | Complete biomedical information search combining PubMed, preprints, clinical trials, and FDA drug labels |
| medical-imaging-review | > |
| fhir-developer-skill | > |
| clinical-trial-protocol-skill | Generate clinical trial protocols for medical devices or drugs |
| prior-auth-review-skill | Automate payer review of prior authorization (PA) requests |
| clinical-reports | "Write comprehensive clinical reports including case reports (CARE guidelines), diagnostic reports (radiology/pathology/lab), clinical trial reports (ICH-E3, SAE,… |
| clinicaltrials-database | "Query ClinicalTrials.gov via API v2 |
| clinical-decision-support | "Generate professional clinical decision support (CDS) documents for pharmaceutical and clinical research settings, including patient cohort analyses… |
| clinical-trial-design | Strategic clinical trial design feasibility assessment using MedToolkit |
| disease-research | Generate comprehensive disease research reports using 100+ MedToolkit tools |
| literature-deep-research | Conduct comprehensive literature research with target disambiguation, evidence grading, and structured theme extraction |
| clinical-guidelines | Search and retrieve clinical practice guidelines across 12+ authoritative sources including NICE, WHO, ADA, AHA/ACC, NCCN, SIGN, CPIC, CMA, CTFPHC, GIN, MAGICapp,… |
| drug-research | Generates comprehensive drug research reports with compound disambiguation, evidence grading, and mandatory completeness sections |
| drug-repurposing | Identify drug repurposing candidates using MedToolkit for target-based, compound-based, and disease-driven strategies |
| drug-drug-interaction | Comprehensive drug-drug interaction (DDI) prediction and risk assessment |
| rare-disease-diagnosis | Provide differential diagnosis for patients with suspected rare diseases based on phenotype and genetic data |
| pharmacovigilance | Analyze drug safety signals from FDA adverse event reports, label warnings, and pharmacogenomic data |
| clinical-trial-matching | AI-driven patient-to-trial matching for precision medicine and oncology |
| literature-review | Conduct comprehensive, systematic literature reviews using multiple academic databases (PubMed, arXiv, bioRxiv, Semantic Scholar, etc.) |
| precision-oncology | Provide actionable treatment recommendations for cancer patients based on molecular profile |
| cancer-variant-interpretation | Provide comprehensive clinical interpretation of somatic mutations in cancer |
| variant-analysis | Production-ready VCF processing, variant annotation, mutation analysis, and structural variant (SV/CNV) interpretation for bioinformatics questions |
| variant-interpretation | Systematic clinical variant interpretation from raw variant calls to ACMG-classified recommendations with structural impact analysis |
| structural-variant-analysis | Comprehensive structural variant (SV) analysis skill for clinical genomics |
| polygenic-risk-score | Build and interpret polygenic risk scores (PRS) for complex diseases using GWAS summary statistics |
| precision-medicine-stratification | Comprehensive patient stratification for precision medicine by integrating genomic, clinical, and therapeutic data |
| gwas-trait-to-gene | Discover genes associated with diseases and traits using GWAS data from the GWAS Catalog (500,000+ associations) and Open Targets Genetics (L2G predictions) |
| gwas-drug-discovery | Transform GWAS signals into actionable drug targets and repurposing opportunities |
| gwas-study-explorer | Compare GWAS studies, perform meta-analyses, and assess replication across cohorts |
| gwas-finemapping | Identify and prioritize causal variants at GWAS loci using statistical fine-mapping and locus-to-gene predictions |
| gwas-snp-interpretation | Interpret genetic variants (SNPs) from GWAS studies by aggregating evidence from multiple databases (GWAS Catalog, Open Targets Genetics, ClinVar) |
| phylogenetics | Production-ready phylogenetics and sequence analysis skill for alignment processing, tree analysis, and evolutionary metrics |
| epigenomics | Production-ready genomics and epigenomics data processing for BixBench questions |
| rnaseq-deseq2 | Production-ready RNA-seq differential expression analysis using PyDESeq2 |
| single-cell | "Production-ready single-cell and expression matrix analysis using scanpy, anndata, and scipy |
| spatial-transcriptomics | Analyze spatial transcriptomics data to map gene expression in tissue architecture |
| spatial-omics-analysis | Computational analysis framework for spatial multi-omics data integration |
| proteomics-analysis | Analyze mass spectrometry proteomics data including protein quantification, differential expression, post-translational modifications (PTMs), and protein-protein… |
| metabolomics | Comprehensive metabolomics research skill for identifying metabolites, analyzing studies, and searching metabolomics databases |
| metabolomics-analysis | Analyze metabolomics data including metabolite identification, quantification, pathway analysis, and metabolic flux |
| multi-omics-integration | Integrate and analyze multiple omics datasets (transcriptomics, proteomics, epigenomics, genomics, metabolomics) for systems biology and precision medicine |
| multiomic-disease-characterization | Comprehensive multi-omics disease characterization integrating genomics, transcriptomics, proteomics, pathway, and therapeutic layers for systems-level understanding |
| expression-data-retrieval | Retrieves gene expression and omics datasets from ArrayExpress and BioStudies with gene disambiguation, experiment quality assessment, and structured reports |
| gene-enrichment | Perform comprehensive gene enrichment and pathway analysis using gseapy (ORA and GSEA), PANTHER, STRING, Reactome, and 40+ MedToolkit tools |
| systems-biology | Comprehensive systems biology and pathway analysis using multiple pathway databases (Reactome, KEGG, WikiPathways, Pathway Commons, BioModels) |
| protein-interactions | Analyze protein-protein interaction networks using STRING, BioGRID, and SASBDB databases |
| protein-structure-retrieval | Retrieves protein structure data from RCSB PDB, PDBe, and AlphaFold with protein disambiguation, quality assessment, and comprehensive structural profiles |
| protein-therapeutic-design | Design novel protein therapeutics (binders, enzymes, scaffolds) using AI-guided de novo design |
| antibody-engineering | Comprehensive antibody engineering and optimization for therapeutic development |
| immune-repertoire-analysis | Comprehensive immune repertoire analysis for T-cell and B-cell receptor sequencing data |
| immunotherapy-response-prediction | Predict patient response to immune checkpoint inhibitors (ICIs) using multi-biomarker integration |
| infectious-disease | Rapid pathogen characterization and drug repurposing analysis for infectious disease outbreaks |
| crispr-screen-analysis | Comprehensive CRISPR screen analysis for functional genomics |
| target-research | Gather comprehensive biological target intelligence from 9 parallel research paths covering protein info, structure, interactions, pathways, expression, variants,… |
| network-pharmacology | Construct and analyze compound-target-disease networks for drug repurposing, polypharmacology discovery, and systems pharmacology |
| statistical-modeling | Perform statistical modeling and regression analysis on biomedical datasets |
| image-analysis | Production-ready microscopy image analysis and quantitative imaging data skill for colony morphometry, cell counting, fluorescence quantification, and statistical… |
| literature-search | Comprehensive scientific literature search across PubMed, arXiv, bioRxiv, medRxiv |
| medrxiv-search | Search medRxiv medical preprints with natural language queries |
| clinical-trials-search | Search ClinicalTrials.gov with natural language queries |
| drug-discovery-search | End-to-end drug discovery platform combining ChEMBL compounds, DrugBank, targets, and FDA labels |
| drug-labels-search | Search FDA drug labels with natural language queries |
| chembl-search | Search ChEMBL bioactive molecules database with natural language queries |
| open-targets-search | Search Open Targets drug-disease associations with natural language queries |
| patents-search | Search global patents with natural language queries |
| drugbank-search | Search DrugBank comprehensive drug database with natural language queries |
| arxiv-search | Search arXiv physics, math, and computer science preprints using natural language queries |
| gwas-database | "Query NHGRI-EBI GWAS Catalog for SNP-trait associations |
| scikit-survival | Comprehensive toolkit for survival analysis and time-to-event modeling in Python using scikit-survival |
| pydicom | Python library for working with DICOM (Digital Imaging and Communications in Medicine) files |
| histolab | Digital pathology image processing toolkit for whole slide images (WSI) |
| pathml | Computational pathology toolkit for analyzing whole-slide images (WSI) and multiparametric imaging data |
| omero-integration | "Microscopy data management platform |
| neurokit2 | Comprehensive biosignal processing toolkit for analyzing physiological data including ECG, EEG, EDA, RSP, PPG, EMG, and EOG signals |
| neuropixels-analysis | "Neuropixels neural recording analysis |
| pyhealth | Comprehensive healthcare AI toolkit for developing, testing, and deploying machine learning models with clinical data |
| scikit-learn | Machine learning in Python with scikit-learn |
| transformers | This skill should be used when working with pre-trained transformer models for natural language processing, computer vision, audio, or multimodal tasks |
| shap | Model interpretability and explainability using SHAP (SHapley Additive exPlanations) |
| umap-learn | "UMAP dimensionality reduction |
| crisis-detection-intervention-ai | Detect crisis signals in user content using NLP, mental health sentiment analysis, and safe intervention protocols |
| crisis-response-protocol | Handle mental health crisis situations in AI coaching safely |
| hipaa-compliance | Ensure HIPAA compliance when handling PHI (Protected Health Information) |
| clinical-diagnostic-reasoning | Identify and counteract cognitive biases in medical decision-making through systematic error analysis and contextual algorithm application |
| speech-pathology-ai | Expert speech-language pathologist specializing in AI-powered speech therapy, phoneme analysis, articulation visualization, voice disorders, fluency intervention, and… |
| hrv-alexithymia-expert | Heart rate variability biometrics and emotional awareness training |
| adhd-daily-planner | Time-blind friendly planning, executive function support, and daily structure for ADHD brains |
| grief-companion | Compassionate bereavement support, memorial creation, grief education, and healing journey guidance |
| jungian-psychologist | Expert in Jungian analytical psychology, depth psychology, shadow work, archetypal analysis, dream interpretation, active imagination, addiction/recovery through… |
| modern-drug-rehab-computer | Comprehensive knowledge system for addiction recovery environments, supporting both residential and outpatient (IOP/PHP) patients |
| recovery-community-moderator | Trauma-informed AI moderator for addiction recovery communities |
💊 Drug Discovery & Safety (44)
Drug-safety signals, cheminformatics, and AI-driven discovery/design agents.
| Skill | Description |
|---|---|
| adverse-event-detection | Detect and analyze adverse drug event signals using FDA FAERS data, drug labels, disproportionality analysis (PRR, ROR, IC), and biomedical evidence |
| binder-discovery | Discover novel small molecule binders for protein targets using structure-based and ligand-based approaches |
| chemical-compound-retrieval | Retrieves chemical compound information from PubChem and ChEMBL with disambiguation, cross-referencing, and quality assessment |
| chemical-safety | Comprehensive chemical safety and toxicology assessment integrating ADMET-AI predictions, CTD toxicogenomics, FDA label safety data, DrugBank safety profiles, and… |
| drug-target-validation | Comprehensive computational validation of drug targets for early-stage drug discovery |
| sequence-retrieval | Retrieves biological sequences (DNA, RNA, protein) from NCBI and ENA with gene disambiguation, accession type handling, and comprehensive sequence profiles |
| rdkit | "Cheminformatics toolkit for fine-grained molecular control |
| datamol | "Pythonic wrapper around RDKit with simplified interface and sensible defaults |
| medchem | "Medicinal chemistry filters |
| diffdock | "Diffusion-based molecular docking |
| molfeat | "Molecular featurization for ML (100+ featurizers) |
| deepchem | "Molecular machine learning toolkit |
| torchdrug | "Graph-based drug discovery toolkit |
| pytdc | "Therapeutics Data Commons |
| cobrapy | "Constraint-based metabolic modeling (COBRA) |
| agentd-drug-discovery | Use the AgentD workflow to mine evidence, design molecules, and rank candidates with SAR plus ADMET annotations for early drug discovery tasks. |
| chematagent-drug-discovery | Chemical Lab Agent |
| chemcrow-drug-discovery | 'An LLM chemistry agent with expert-designed tools for organic synthesis, drug discovery, and materials design.' |
| medea-therapeutic-discovery | An AI agent for therapeutic discovery that executes transparent, multi-step omics analyses including research planning, code execution, and literature reasoning. |
| molecule-evolution-agent | 'Evolve Molecules' |
| molecular-glue-discovery-agent | 'AI-powered molecular glue discovery for targeted protein degradation, enabling neo-substrate recruitment and undruggable target degradation through E3 ligase… |
| protac-design-agent | 'AI-powered PROTAC (Proteolysis Targeting Chimera) design for targeted protein degradation, integrating ternary complex prediction, linker optimization, and ADMET… |
| tpd-ternary-complex-agent | 'AI-powered ternary complex prediction for targeted protein degradation, modeling POI-degrader-E3 ligase assemblies to optimize PROTAC and molecular glue efficacy.' |
| mage-antibody-generator | Ab seq forge |
| antibody-design-agent | 'An advanced agent for de novo antibody design and optimization using state-of-the-art protein language models (MAGE, RFdiffusion).' |
| aav-vector-design-agent | 'AI-powered adeno-associated virus (AAV) vector design for gene therapy including capsid engineering, promoter selection, and tropism optimization.' |
| protein-structure-prediction | 'Predicts 3D protein structures from amino acid sequences using ESMFold or AlphaFold3 (mock).' |
| crispr-guide-design | Guide foundry |
| crispr-offtarget-predictor | 'Predicts potential off-target sites for a given sgRNA sequence using mismatch analysis.' |
| chemical-property-lookup | Compute RDKit-driven molecular properties (MW, logP, TPSA, QED, Lipinski) for a SMILES string to support downstream drug discovery tools. |
| chemistry-agent | Autonomous chemical synthesis & analysis |
| cryoem-ai-drug-design-agent | 'AI-powered integration of cryo-EM structural data with generative AI and molecular dynamics for structure-based drug design targeting flexible proteins and membrane… |
| time-resolved-cryoem-agent | 'AI-powered time-resolved cryo-EM analysis for capturing protein dynamics, drug-binding kinetics, and conformational transitions for dynamics-based drug discovery.' |
| cnv-caller-agent | 'AI-enhanced copy number variation calling and analysis from sequencing data for cancer genomics, constitutional CNV detection, and chromosomal aberration… |
| popeve-variant-predictor-agent | 'AI-powered genetic variant pathogenicity prediction using PopEVE deep learning model for population-aware disease variant identification and rare disease diagnosis.' |
| varcadd-pathogenicity | Variant Scorer |
| variant-interpretation-acmg | 'Classifies genetic variants according to ACMG (American College of Medical Genetics) guidelines.' |
| gene-panel-design-agent | 'AI-powered design of targeted gene panels for clinical and research applications including cancer diagnostics, pharmacogenomics, and rare disease testing.' |
| pharmacogenomics-agent | 'AI-driven pharmacogenomic analysis for precision dosing and adverse event prediction using multi-omics data.' |
| multi-ancestry-prs-agent | 'AI-powered multi-ancestry polygenic risk score calculation and optimization for equitable disease risk prediction across diverse global populations.' |
| prs-net-deep-learning-agent | 'Geometric deep learning-based polygenic risk score prediction using PRS-Net for modeling gene interactions, enhanced disease prediction, and cross-ancestry portability.' |
| cellfree-rna-agent | 'AI-powered cell-free RNA analysis from liquid biopsy for cancer detection, tissue-of-origin identification, and non-invasive transcriptomic profiling.' |
| long-read-sequencing-agent | 'AI-powered analysis of long-read sequencing data (PacBio, ONT) for structural variant detection, isoform discovery, epigenetic modifications, and de novo assembly.' |
| bayesian-optimizer | 'Bayesian Optimize' |
🩺 Health & Wellness (15)
Patient-facing wellness, lifestyle, and population-health analytics.
| Skill | Description |
|---|---|
| nutrition-analyzer | Analyze nutrition data, identify dietary patterns, assess nutritional status, and provide personalized nutrition advice. Supports correlation analysis with exercise, sleep, and chronic-disease data. |
| mental-health-analyzer | Analyze mental-health data, identify psychological patterns, assess mental-health status, and provide personalized mental-health advice. Supports correlation analysis with sleep, exercise, nutrition, and other health data. |
| sleep-analyzer | Analyze sleep data, identify sleep patterns, assess sleep quality, and provide personalized sleep-improvement advice. Supports correlation analysis with other health data. |
| rehabilitation-analyzer | Analyze rehabilitation-training data, identify recovery patterns, assess rehabilitation progress, and provide personalized rehabilitation advice. |
| fitness-analyzer | Analyze exercise data, identify activity patterns, assess fitness progress, and provide personalized training advice. Supports correlation analysis with chronic-disease data. |
| health-trend-analyzer | Analyze trends and patterns in health data over time. Correlate changes in medications, symptoms, vital signs, lab results, and other indicators. Identify concerning trends and improvements and provide data-driven insights. Use when users ask about health trends, patterns, or changes over time. Supports multi-dimensional analysis (weight/BMI, symptoms, medication adherence, lab results, mood and sleep), correlation analysis, change detection, and interactive HTML visualization. |
| weightloss-analyzer | Analyze weight-loss data, calculate metabolic rate, track energy deficit, and manage weight-loss phases. |
| goal-analyzer | Analyze health-goal data, identify goal patterns, assess goal progress, and provide personalized goal-management advice. Supports correlation analysis with nutrition, exercise, sleep, and other health data. |
| occupational-health-analyzer | Analyze occupational-health data, identify work-related health risks, assess occupational-health status, and provide personalized occupational-health advice. Supports correlation analysis with sleep, exercise, mental health, and other health data. |
| travel-health-analyzer | Analyze travel-health data, assess destination health risks, provide vaccination recommendations, and generate multilingual emergency medical information cards. Supports professional-grade travel-health risk assessment with WHO/CDC data integration. |
| family-health-analyzer | Analyze family medical history, assess genetic risk, identify family health patterns, and provide personalized prevention advice. |
| tcm-constitution-analyzer | Analyze Traditional Chinese Medicine (TCM) constitution data, identify constitution types, assess constitutional characteristics, and provide personalized wellness advice. Supports correlation analysis with nutrition, exercise, sleep, and other health data. |
| emergency-card | Generate a medical-information summary card for quick access in emergencies. Use when users need travel or appointment preparation, face an emergency, or ask for 'emergency info', 'medical card', or 'first-aid info'. Extracts key information (allergies, medications, acute conditions, implants) and supports multiple output formats (JSON, text, QR code) for first aid or rapid care. |
| ai-analyzer | An AI-driven comprehensive health-analysis system that integrates multi-dimensional health data, identifies abnormal patterns, predicts health risks, and provides personalized advice. Supports intelligent Q&A and AI health-report generation. |
| wellally-tech | Integrate digital health data sources (Apple Health, Fitbit, Oura Ring) and connect to WellAlly.tech knowledge base |
📋 Medical Device & Regulatory (2)
Quality systems, regulatory documentation, and clinical-ML validation/governance.
| Skill | Description |
|---|---|
| ⭐ ml-model-validation-regulatory | Validate, document & monitor clinical ML for SaMD governance (GMLP, IEC 62304, ISO 14971) with drift monitoring and model cards. |
| iso-13485-certification | Comprehensive toolkit for preparing ISO 13485 certification documentation for medical device Quality Management Systems |
⚙️ Simulation & Ontology (18)
Numerical simulation, HPC orchestration, and medical terminology/ontology mapping.
| Skill | Description |
|---|---|
| ⭐ medical-ontology-code-mapping | Normalize free-text/coded data to ICD-10, CPT/HCPCS, SNOMED CT, RxNorm, NDC, LOINC and crosswalk between them via UMLS. |
| ontology-validator | > |
| ontology-explorer | > |
| ontology-mapper | > |
| slurm-job-script-generator | Generate SLURM sbatch job scripts and sanity-check HPC resource requests (nodes, tasks, CPUs, memory, GPUs) for simulation runs |
| numerical-integration | Select and configure time integration methods for ODE/PDE simulations |
| nonlinear-solvers | Select and configure nonlinear solvers for f(x)=0 or min F(x) |
| parameter-optimization | Explore and optimize simulation parameters via design of experiments (DOE), sensitivity analysis, and optimizer selection |
| linear-solvers | Select and configure linear solvers for systems Ax=b in dense and sparse problems |
| numerical-stability | Analyze and enforce numerical stability for time-dependent PDE simulations |
| simulation-orchestrator | Orchestrate multi-simulation campaigns including parameter sweeps, batch jobs, and result aggregation |
| simulation-validator | Validate simulations before, during, and after execution |
| convergence-study | Spatial and temporal convergence analysis with Richardson extrapolation and Grid Convergence Index (GCI) for solution verification |
| post-processing | Extract, analyze, and visualize simulation output data |
| performance-profiling | Identify computational bottlenecks, analyze scaling behavior, estimate memory requirements, and receive optimization recommendations for any computational simulation |
| differentiation-schemes | Select and apply numerical differentiation schemes for PDE/ODE discretization |
| time-stepping | Plan and control time-step policies for simulations |
| mesh-generation | Plan and evaluate mesh generation for numerical simulations |
Installation
OpenClaw loads skills from <workspace>/skills/ (per-workspace) or ~/.openclaw/skills/ (global).
git clone --depth=1 <your-repo-url> MedClawMini
cp -r MedClawMini/skills/* <your-workspace>/skills/ # or ~/.openclaw/skills/
Skills are picked up automatically on the next session no restart needed.
Validate a skill before committing:
python scripts/validate_skill.py skills/<skill-name>
Repository Layout
MedClawMini/
├── README.md # this file
├── openclaw.plugin.json # OpenClaw plugin manifest (all skills)
├── scripts/validate_skill.py # SKILL.md frontmatter validator
├── .github/workflows/ # CI: validate skills on PR
└── skills/<skill-name>/SKILL.md # one folder per skill (+ scripts/, references/, examples/)
Each skill module has YAML frontmatter (name, description, …) followed by the workflow the
agent should follow. Add a new skill by creating skills/<name>/SKILL.md and validating it.
License
Released under the MIT License. MedClawMini is an independent, curated skill library for the OpenClaw agent platform.