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From blood pressure tracking to tumor board analysis — plug-and-play health intelligence for your AI assistant.
What is VitaClaw?
VitaClaw is an open-source library of 222 modular health AI skills designed to run inside OpenClaw. Each skill is a self-contained SKILL.md file that gives your AI assistant deep, domain-specific health capabilities — from daily vitals logging to clinical genomics interpretation.
VitaClaw is built on three pillars:
Health Memory System — Daily wellness tracking under memory/health/ plus a structured clinical record archive under ~/.openclaw/patients/. Your data lives as plain Markdown files on your machine, versioned by git, owned by you.
Modular Skills — 222 self-contained SKILL.md files, each defining its own prompt, tools, and data format. Drop in only what you need, edit freely, extend without limits.
Scenario Orchestration — 7 scenario apps (e.g., diabetes-control-hub, hypertension-daily-copilot) that chain multiple skills into end-to-end clinical workflows, from data capture through longitudinal analysis to care recommendations.
Why VitaClaw?
Feature
Generic Health App
VitaClaw
Data ownership
Cloud / vendor lock-in
Local files — you own everything
Customization
Fixed features
Edit anySKILL.md
Clinical depth
Consumer-grade
Research-grade (PubMed, ClinVar, GWAS)
Integration
Siloed
Skills chain together via health-memory
AI model
Single vendor
Any model via OpenClaw (Claude, GPT, Gemini, Llama …)
Installation
Option A — Git Clone (recommended)
# Clone into OpenClaw shared skills directory
git clone https://github.com/vitaclaw/vitaclaw.git ~/.openclaw/skills/vitaclaw
# Or clone into a workspace
git clone https://github.com/vitaclaw/vitaclaw.git ./skills/vitaclaw
Option B — Cherry-pick individual skills
# Copy only the skills you need
cp -r vitaclaw/skills/blood-pressure-tracker ~/.openclaw/skills/
cp -r vitaclaw/skills/diabetes-control-hub ~/.openclaw/skills/
No build step, no dependencies, no configuration wizard. OpenClaw automatically discovers SKILL.md files in its skills directories.
Structured medical record archive — auto-classifies PDFs, scans, and lab reports into a per-patient directory (imaging, labs, pathology, genomics, discharge summaries). Builds navigable INDEX.md with one-line summaries.
Manages comprehensive diabetes control by coordinating blood glucose tracking, nutrition analysis, exercise correlation, kidney function monitoring, and complication risk assessment.
Provides daily mental health support by coordinating PHQ-9/GAD-7 assessment, crisis detection, sleep-mood correlation, exercise prescription, and behavioral activation.
Manages allergy profiles including food, environmental, and drug allergies. Tracks reactions, identifies cross-reactivity risks, provides seasonal allergy forecasts, and warns about allergen exposure.
Records and classifies blood pressure readings per ACC/AHA 2017 guidelines, detects morning surge, analyzes diurnal variation, and generates monthly statistics.
Analyzes body composition metrics including body fat percentage, muscle mass, visceral fat, and BMI. Tracks trends and provides training and nutrition recommendations.
Analyzes Google Fit exported data including steps, heart rate, sleep, and activity metrics. Generates health digests and trend reports from Google Fit JSON/CSV exports.
Analyzes health data trends and patterns over time. Correlates medications, symptoms, vital signs, lab results, and other health indicators. Identifies concerning trends, improvements, and provides data-driven insights. Supports interactive HTML visualization reports (ECharts).
Tracks hormone lab results over time, analyzes trends for thyroid, sex hormones, cortisol, and metabolic markers, and provides lifestyle optimization suggestions.
Tracks daily water intake, calculates personalized hydration targets based on body weight, activity level, and weather conditions, and provides reminders.
Aggregates the past 7 days of health data from health-memory into a narrative weekly report with a composite health score (0-100), per-domain summaries, cross-domain correlations, and actionable next-week suggestions.
Time-blind friendly planning, executive function support, and daily structure for ADHD brains. Specializes in realistic time estimation, dopamine-aware task design, and building systems.
Detect crisis signals in user content using NLP, mental health sentiment analysis, and safe intervention protocols. Implements suicide ideation detection, automated escalation, and crisis resource routing.
Handle mental health crisis situations in AI coaching safely. Implements crisis detection, safety protocols, emergency escalation, and suicide prevention features.
Expert in Jungian analytical psychology, depth psychology, shadow work, archetypal analysis, dream interpretation, active imagination, addiction/recovery through Jungian lens, and the individuation process.
Analyzes mental health data, identifies psychological patterns, assesses mental health status, and provides personalized mental health advice. Supports correlation with sleep, exercise, and nutrition data.
Comprehensive knowledge system for addiction recovery environments, supporting both residential and outpatient (IOP/PHP) patients. Expert in evidence-based treatment modalities (CBT, DBT, MI).
Analyzes occupational health data, identifies work-related health risks, assesses occupational health status, and provides personalized occupational health advice.
Chemotherapy side effect tracking — records side effects per CTCAE v5.0 grading, supports cycle comparison, toxicity trend analysis, and comprehensive toxicity reports.
Generates emergency medical information summary cards for quick access. Extracts key information (allergies, medications, emergencies, implants), supports multi-format output (JSON, text, QR code).
Query cBioPortal for cancer genomics data including somatic mutations, copy number alterations, gene expression, and survival data across hundreds of cancer studies.
Query ClinicalTrials.gov via API v2. Search trials by condition, drug, location, status, or phase. Retrieve trial details by NCT ID for clinical research and patient matching.
Access ClinPGx pharmacogenomics data (successor to PharmGKB). Query gene-drug interactions, CPIC guidelines, allele functions, for precision medicine and genotype-guided dosing.
Query NCBI ClinVar for variant clinical significance. Search by gene/position, interpret pathogenicity classifications, access via E-utilities API or FTP, annotate VCFs.
Access and analyze comprehensive drug information from DrugBank including drug properties, interactions, targets, pathways, chemical structures, and pharmacology data.
Generate professional clinical decision support (CDS) documents for pharmaceutical and clinical research settings, including patient cohort analyses and treatment recommendation reports. Supports GRADE evidence grading, statistical analysis, biomarker integration.
Generates personalized nutrition reports from consumer genetic data (23andMe, AncestryDNA). Translates nutritionally-relevant SNPs into dietary and supplementation guidance.
AI-powered analysis of cancer metabolic reprogramming including Warburg effect, glutamine addiction, lipid metabolism, and metabolic vulnerabilities for therapeutic targeting.
AI-powered analysis of chromosomal instability (CIN) signatures for cancer prognosis, immunotherapy response prediction, and therapeutic vulnerability identification.
AI-powered patient digital twin creation for clinical trial simulation, treatment outcome prediction, and personalized medicine using real-world data and multi-omics integration.
AI-powered analysis for predicting optimal immune checkpoint inhibitor combinations based on tumor microenvironment, biomarkers, and molecular profiling.
AI-powered analysis of tumor clonal architecture, subclonal dynamics, and evolutionary trajectories from multi-region sequencing and longitudinal liquid biopsy data.
AI-powered cytokine release syndrome (CRS) and cytokine storm analysis for prediction, monitoring, and management in immunotherapy and infectious disease.
AI-powered analysis of hemoglobin disorders including sickle cell disease, thalassemias, and variant hemoglobins using HPLC, electrophoresis, and molecular data.
AI-powered myeloproliferative neoplasm monitoring for disease progression prediction, treatment response tracking, and transformation risk assessment in PV, ET, and myelofibrosis.
Score and prioritize neoantigens and epitopes for immunogenicity using multi-factor models combining MHC binding, processing, expression, and sequence features.
AI-powered circulating tumor DNA dynamics analysis for molecular residual disease detection, treatment response monitoring, and early relapse prediction.
Ultra-sensitive AI-powered molecular residual disease detection using MRD-EDGE deep learning for sub-0.001% VAF ctDNA detection and early relapse prediction.
Perform statistical tests, hypothesis testing, correlation analysis, and multiple testing corrections using scipy and statsmodels. Works with ANY LLM provider.
Provides step-by-step first aid instructions for common emergencies including burns, fractures, choking, poisoning, and bleeding. Includes CPR guidance.
Comprehensive sexual health data analysis including IIEF-5 scoring, STD screening management, contraception assessment, and cross-domain correlation analysis.
Tracks social interactions, assesses loneliness via UCLA Loneliness Scale, maps Dunbar social circles, generates social health scores, and provides social prescriptions.
Analyzes travel health data, assesses destination health risks, provides vaccination recommendations, and generates multilingual emergency medical information cards. Integrates WHO/CDC data.
Production-ready PDF processing with forms, tables, OCR, validation, and batch operations.
Scenario Applications
The 7 scenario skills orchestrate multiple sub-skills into complete clinical workflows, turning a single user prompt into a coordinated multi-agent health session.
The diabetes-control-hub scenario illustrates how sub-skills are chained end-to-end:
graph LR
A[User Input] --> B[chronic-condition-monitor]
B --> C[nutrition-analyzer]
C --> D[food-database-query]
D --> E[fitness-analyzer]
E --> F[health-trend-analyzer]
F --> G[health-memory]
G --> H[Daily Briefing]
Daily logs aggregate multi-skill data into one file per day. Each skill writes its own ## Section [skill-name · HH:MM] block.
Item files maintain 90-day rolling histories for longitudinal metrics (blood pressure, weight, glucose, etc.) with trend summaries.
Format contract — all skills follow the same Markdown schema with YAML frontmatter, so any skill can consume data produced by any other skill with no transformation needed.
Medical Record Archive
For clinical documents — imaging reports, lab results, pathology, genomics, and discharge summaries — the medical-record-organizer skill provides a structured per-patient archive under ~/.openclaw/patients/:
While health-memory tracks daily wellness metrics (vitals, sleep, nutrition), medical-record-organizer manages clinical documents (hospital reports, scans, lab sheets). Together they form a complete personal health data layer — daily self-tracking plus longitudinal medical records.
VitaClaw is for health management reference only and does not constitute medical diagnosis or treatment advice.
Always consult qualified healthcare professionals for medical decisions.
In emergencies, call your local emergency number (120/911/999) immediately.