Best use case
<!-- is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
# COPYRIGHT NOTICE
Teams using <!-- should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/wearable-analysis-agent/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How <!-- Compares
| Feature / Agent | <!-- | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
# COPYRIGHT NOTICE
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
<!-- # COPYRIGHT NOTICE # This file is part of the "Universal Biomedical Skills" project. # Copyright (c) 2026 MD BABU MIA, PhD <md.babu.mia@mssm.edu> # All Rights Reserved. # # This code is proprietary and confidential. # Unauthorized copying of this file, via any medium is strictly prohibited. # # Provenance: Authenticated by MD BABU MIA --> --- name: wearable-analysis-agent description: Analyzes longitudinal wearable sensor data (heart rate, activity, sleep) to detect anomalies and provide personalized health insights. keywords: - wearable - sensor-data - health-monitoring - anomaly-detection - longitudinal-analysis measurable_outcome: Detects atrial fibrillation and sleep anomalies with >90% accuracy using continuous PPG and accelerometer data. license: MIT metadata: author: Biomedical AI Team version: "1.0.0" compatibility: - system: Python 3.9+ allowed-tools: - run_shell_command - read_file --- # Wearable Analysis Agent The **Wearable Analysis Agent** processes data from consumer health devices (Apple Watch, Fitbit, Oura) to monitor vital signs, detect arrhythmias, and analyze lifestyle patterns. ## When to Use This Skill * When analyzing raw export data from wearables (XML, JSON, CSV). * To detect irregular heart rhythms (AFib) from PPG data. * For longitudinal sleep quality and circadian rhythm analysis. * To correlate activity levels with biomarkers or symptom logs. ## Core Capabilities 1. **Arrhythmia Detection**: Algorithms to identify Atrial Fibrillation burdens from irregular tachograms. 2. **Sleep Staging**: classifying wake/REM/deep sleep from movement and heart rate variability. 3. **Activity Recognition**: Categorizing physical activities and calculating intensity (METs). 4. **Trend Analysis**: Detecting significant deviations in resting heart rate or HRV over weeks/months. ## Workflow 1. **Ingest**: Parse standardized health exports (e.g., Apple Health XML). 2. **Preprocess**: Clean noise, handle missing data, align timestamps. 3. **Analyze**: Apply specific detection algorithms (e.g., `arrhythmia_detector.py`). 4. **Report**: Generate summary of anomalies and trends. ## Example Usage **User**: "Analyze my Apple Health export for signs of irregular heart rhythm last month." **Agent Action**: ```bash python3 Skills/Consumer_Health/Wearable_Analysis/arrhythmia_detector.py --input apple_health_export.xml --window "last_month" ``` <!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->
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