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