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-t
genpark-cognitive-load-budget-optimizer-skill
Production-Grade AI Agent Infrastructure Skill • 100% Standard Library Python • Native Model Context Protocol (MCP)
🌐 GenPark MCP Hub • 📦 GenPark Official • 📖 Documentation
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.
- 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.
Run the MCP server via standard JSON-RPC 2.0 stdio:
Verify standard compliance and self-tests:
From the project README.
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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-t
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