genpark-cognitive-load-budget-optimizer-skill

🌟 Overview

genpark-cognitive-load-budget-optimizer-skill delivers robust, industrial-grade capabilities for autonomous agents, copilot frameworks, and personal assistant architectures. Built exclusively on the Python standard library with zero external runtime dependencies, it integrates seamlessly as a native Model Context Protocol (MCP) server or an importable Python module.

Dynamic Cognitive Load, Latency & Token Budget Router. Monitors running agent loops, estimates token consumption and monetary cost, prunes verbose thinking chains when nearing limits, and routes sub-tasks between fast/cheap models and deep reasoning engines.

💡 Key Capabilities

• Zero-Dependency Architecture: Runs anywhere Python 3.9+ is installed without pip install overhead or supply-chain vulnerabilities.
• Model Context Protocol (MCP) First: Compatible with Claude Desktop, Cursor, GenPark Engine, and custom agentic frameworks.
• Deterministic & Safe: Designed with strict validation, graceful error handling, and structured telemetry.
• High Concurrency & Low Latency: In-memory caching and optimized data structures for real-time agent execution loops.

🚀 Quickstart

1. Direct Python Usage

from client import CognitiveLoadBudgetOptimizer

client = CognitiveLoadBudgetOptimizer()
result = client.optimize_cognitive_budget()
print(result)

2. Standalone MCP Server Execution

Run the MCP server via standard JSON-RPC 2.0 stdio:

python mcp_server.py

Verify standard compliance and self-tests:

python mcp_server.py --test

3. Claude Desktop / Cursor MCP Configuration

Add this tool to your claude_desktop_config.json or Cursor MCP settings:

{
  "mcpServers": {
    "genpark-cognitive-load-budget-optimizer-skill": {
      "command": "python",
      "args": ["/absolute/path/to/genpark-cognitive-load-budget-optimizer-skill/mcp_server.py"]
    }
  }
}

🛠️ Verification & Testing

Run the included verification suite:

python example_usage.py

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

Developed with ❤️ by the GenPark Autonomous Agent Ecosystem Team.