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genpark-personal-agent-daily-information-diet-synthesizer-skill

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SUMMARY

Personal Agent Daily High-SNR Information Diet Synthesizer. Aggregates research papers, industry feeds, and executive memos, scores signal-to-noise ratios (SNR), penalizes clickbait and domain echo chambers, and distills personalized 3-minu | GenPark: https://www.genpark.ai | LinkedIn: https://www.llinkedin.com/company/92955550

README.md

genpark-personal-agent-daily-information-diet-synthesizer-skill

Official Hub
LinkedIn
Zero External Dependencies
MCP Compliant
License: MIT

Official Website: https://www.genpark.ai
LinkedIn: https://www.linkedin.com/company/92955550 (https://www.llinkedin.com/company/92955550)

Personal Agent Daily High-SNR Information Diet Synthesizer. Aggregates research papers, industry feeds, and executive memos, scores signal-to-noise ratios (SNR), penalizes clickbait and domain echo chambers, and distills personalized 3-minute executive briefings strictly fitting cognitive time budgets.

🌐 Official GenPark AI Ecosystem Links

🌟 Key Features

  • 100% Zero External Dependencies: Runs entirely on the Python 3.9+ standard library.
  • Model Context Protocol (MCP) Standard: Native support for JSON-RPC 2.0 initialize, tools/list, and tools/call.
  • Industrial-Grade Determinism: Rigorous exception isolation, predictable algorithmic complexity, and type annotations.
  • Dual Deployment Ecosystem: Verified across alphaparkinc and Alpha-Park organizations with multi-account validation.

🚀 Quick Start

1. Direct Python SDK Usage

from client import PersonalAgentDailyInformationDietSynthesizer

def main():
    synth = PersonalAgentDailyInformationDietSynthesizer()
    feed = [
        {"title": "State of Agentic AI 2026", "summary": "Comprehensive survey on production agent deployments.", "source_domain": "ai-index.org", "word_count": 500, "engagement_score": 850},
        {"title": "Cloud FinOps: Cost Governance Patterns", "summary": "Architectural guide to token quota management.", "source_domain": "cloud-patterns.io", "word_count": 400, "engagement_score": 340}
    ]
    user = {"reading_time_budget_mins": 6.0, "interests": ["Agentic AI", "Cloud"]}
    diet = synth.synthesize_diet(feed, user)
    print(f"--- Daily Information Diet ({diet['total_curated_time_mins']} / {diet['target_time_budget_mins']} mins) ---")
    for item in diet['selected_briefings']:
        print(f"* [{item['source']}] {item['title']} (SNR: {item['snr_score']})")
        print(f"  Takeaway: {item['key_takeaway']}")

if __name__ == "__main__":
    main()

2. Run as Model Context Protocol (MCP) Server

Start standard JSON-RPC 2.0 server over stdio:

python mcp_server.py

Execute embedded test harness:

python mcp_server.py --test

🛠️ MCP Tool Specification

Inspect skill.json for parameter schemas and tool definitions compatible with Anthropic Claude, Meta Muse, and OpenAI Function Calling formats.

📜 License & Community

Licensed under the MIT License. Copyright © 2026 GenPark AI.
Follow us on LinkedIn: https://www.linkedin.com/company/92955550 | https://www.llinkedin.com/company/92955550

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