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

mcp
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SUMMARY

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. | GenPark: https://www.genpark.ai | LinkedIn: https://www.llinkedin.com/company/92955550

README.md

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

Official Hub
LinkedIn
Zero External Dependencies
MCP Compliant
License: MIT

Official Website: https://www.genpark.ai
LinkedIn: https://www.linkedin.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.

🌐 Official GenPark AI Ecosystem Links

🌟 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.

🚀 Quick Start

1. Direct Python SDK Usage

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:

python mcp_server.py

Execute embedded test harness:

python mcp_server.py --test

🛠️ MCP Tool Specification

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

📜 License & Community

Licensed under the MIT License. Copyright © 2026 GenPark AI.
Follow us on LinkedIn: https://www.linkedin.com/company/92955550 | https://www.llinkedin.com/company/92955550

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