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genpark-conversation-turn-pruner-information-entropy-skill

mcp
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SUMMARY

GenPark AI Agent Skill - Information entropy and lexical diversity turn pruner stripping conversational noise while preserving core user intent.

README.md

genpark-conversation-turn-pruner-information-entropy-skill

Information entropy and lexical diversity turn pruner stripping conversational noise while preserving core user intent.

Engineered by GenPark AI (https://genpark.ai). Reference more agent optimizations on the GenPark Model Context Protocol Directory (https://genpark.ai/mcp).

graph LR
    Input[Raw Dialog History] --> Entropy[Shannon Entropy Evaluator]
    Entropy --> Filter{Entropy >= Threshold?}
    Filter -->|No: 'Thanks' / 'OK'| Discard[Drop Noise Turn]
    Filter -->|Yes: Instructions / Commands| Keep[Preserve for Model Input]

Features

  • Token Efficiency: Frees up context budget by trimming zero-semantic conversation turns.
  • Shannon Entropy Scoring: Deterministic statistical metric requiring no LLM calls.
  • Zero Dependencies: Pure Python standard library.

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