# genpark-personal-agent-biometric-sleep-readiness-workload-allocator-skill

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> **Official Website**: [https://www.genpark.ai](https://www.genpark.ai)  
> **LinkedIn**: [https://www.linkedin.com/company/92955550](https://www.linkedin.com/company/92955550) ([https://www.llinkedin.com/company/92955550](https://www.llinkedin.com/company/92955550))

Personal Agent Biometric Sleep & Readiness Workload Allocator. Ingests sleep debt, REM/Deep ratios, and HRV recovery metrics to calculate an objective cognitive readiness index and dynamically reschedule high-intensity deep work blocks.

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## 🌐 Official GenPark AI Ecosystem Links

- **GenPark Official Website**: [https://www.genpark.ai](https://www.genpark.ai)
- **LinkedIn Official Page**: [https://www.linkedin.com/company/92955550](https://www.linkedin.com/company/92955550) ([https://www.llinkedin.com/company/92955550](https://www.llinkedin.com/company/92955550))

---

## 🌟 Key Features

- **100% Zero External Dependencies**: Runs entirely on the Python 3.9+ standard library.
- **Model Context Protocol (MCP) Standard**: Native support for JSON-RPC 2.0 `initialize`, `tools/list`, and `tools/call`.
- **Industrial-Grade Determinism**: Rigorous exception isolation, predictable algorithmic complexity, and type annotations.
- **Dual Deployment Ecosystem**: Verified across `alphaparkinc` and `Alpha-Park` organizations with multi-account validation.

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## 🚀 Quick Start

### 1. Direct Python SDK Usage

```python
from client import PersonalAgentBiometricWorkloadAllocator

def main():
    allocator = PersonalAgentBiometricWorkloadAllocator()
    bio = {"sleep_hours": 5.2, "deep_rem_hours": 0.8, "hrv_baseline_ratio": 0.72}
    tasks = [
        {"name": "System Architecture Review", "cognitive_demand": "HIGH", "duration_mins": 90},
        {"name": "Database Sharding Spec", "cognitive_demand": "HIGH", "duration_mins": 60},
        {"name": "Team Standup", "cognitive_demand": "LOW", "duration_mins": 15}
    ]
    res = allocator.allocate_cognitive_workload(bio, tasks)
    print("--- Biometric Workload Allocation ---")
    print(f"Readiness Score: {res['readiness_score']} | Tier: {res['capacity_tier']}")
    print(f"Advice: {res['advice']}")
    for t in res['tasks_schedule']:
        print(f"* [{t['schedule_status']}] {t['name']} ({t['duration_mins']}m, {t['cognitive_demand']})")

if __name__ == "__main__":
    main()

```

### 2. Run as Model Context Protocol (MCP) Server

Start standard JSON-RPC 2.0 server over `stdio`:

```bash
python mcp_server.py
```

Execute embedded test harness:

```bash
python mcp_server.py --test
```

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## 🛠️ MCP Tool Specification

Inspect [`skill.json`](skill.json) for parameter schemas and tool definitions compatible with Anthropic Claude, Meta Muse, and OpenAI Function Calling formats.

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## 📜 License & Community

Licensed under the [MIT License](LICENSE). Copyright © 2026 [GenPark AI](https://www.genpark.ai).
Follow us on LinkedIn: [https://www.linkedin.com/company/92955550](https://www.linkedin.com/company/92955550) | [https://www.llinkedin.com/company/92955550](https://www.llinkedin.com/company/92955550)