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genpark-personal-agent-information-diet-entropy-pruner-skill

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Python
Remote

genpark-personal-agent-information-diet-entropy-pruner-skill

Tests License Python Dependencies Coverage

GenPark Personal Agent Information Diet Entropy Pruner applies Shannon information theory, Jaccard lexical novelty metrics, and decibel SNR modeling to optimize personal reading diets.

flowchart TD
    A[Daily Ingested Reading & Media Stream] --> B[Category Distribution Histogram]
    B --> C[Shannon Information Entropy H]
    D[Pairwise Article Token Sets] --> E[Jaccard Redundancy & Echo Chamber Auditor]
    F[Source Insight vs Noise Densities] --> G[Decibel SNR Ranking Solver]
    C & E & G --> H[Pruning Recommendations & High-Signal Curation]

Features

  • Shannon Entropy Scoring: Quantifies intellectual breadth and flags doomscroll concentration.

  • Echo Chamber Jaccard Metric: Detects repackaged duplicate ideas across different media outlets.

  • Decibel SNR Filtering: Surfaces high-density thought leadership while pruning low-signal noise.

Related MCP server: Parkour MCP

Quickstart

from client import PersonalAgentInformationDietEntropyPruner

pruner = PersonalAgentInformationDietEntropyPruner()
res = pruner.rank_content_sources_snr([{"name": "Research", "insight_density": 0.9, "noise_density": 0.1}])
print("SNR:", res["sources_ranked"][0]["snr_db"], "dB")

FastMCP Integration

python -m genpark-personal-agent-information-diet-entropy-pruner-skill.mcp_server

License

MIT

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Maintenance

ActivityMaintained
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