Loading…
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.
genpark-cognitive-load-budget-optimizer-skill is an MCP server entry in ChatHome Skills, sourced from GitHub MCP Search and scored 52/100. Dynamic Cognitive Load, Latency & Token Budget Router. Monitors running agent loops, estimates token consumption and monetary cost, prunes verbose thinking chai. As a Model Context Protocol server, it exposes tools that MCP-compatible LLM clients such as Claude, Cursor, and Copilot can call directly, extending an agent's reach beyond its built-in capabilities. ChatHome Skills discovered this entry through its daily ingest of official registries and GitHub discovery queries, and re-verifies repository facts, freshness signals, and install metadata on every run. The quality score weighs official-registry presence, a working install command, license clarity, GitHub star trajectory, and repository freshness — the full formula is on the Sources page. The install command above is ready to copy. Review the last-pushed date and repository status before adding genpark-cognitive-load-budget-optimizer-skill to a production agent workflow; the snapshot facts below are point-in-time values from the most recent ingest.
git clone https://github.com/alphaparkinc/genpark-cognitive-load-budget-optimizer-skill.gitReview the upstream source before running this command.
Scan coverage: Install metadata and repository README.
Automated signals from install metadata and a bounded skill body — not a full code security audit and not a recursive source or dependency review.
{
"fullName": "alphaparkinc/genpark-cognitive-load-budget-optimizer-skill"
}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.