genpark-enterprise-meeting-action-item-extractor-skill

Enterprise Multi-Speaker Meeting Action Item Extractor & WeChat Work Task Dispatcher. Analyzes conversational meeting transcripts from Tencent Meeting, Zoom, and Teams, extracts explicit commitments, dates, and assignees, performs Eisenhower urgency-importance matrix prioritization, and formats collaborative task cards.

🌟 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

"""Example usage for EnterpriseMeetingActionItemExtractor."""
import sys
import json
from client import EnterpriseMeetingActionItemExtractor

sys.stdout.reconfigure(encoding='utf-8')

def main():
    print("=== Enterprise WorkBuddy Meeting Action Item Extractor Demo ===")
    extractor = EnterpriseMeetingActionItemExtractor()

    transcript = [
        "David: Good morning everyone, let's review the Q4 cloud infrastructure roadmap.",
        "ZhangSan: I will prepare the Tencent Cloud compute reservation forecast by Friday.",
        "LiSi: Please ensure the Merkle audit verification connector is deployed today, this is an urgent blocker for finance.",
        "WangWu: I'll coordinate the WeChat Work notification bot endpoints before tomorrow EOD."
    ]

    print("\n--- 1. Extracting Structured Tasks from Utterances ---")
    items = extractor.extract_action_items(transcript, "Tencent Cloud Infrastructure Sync")
    print(f"Discovered {len(items)} action items:")
    for item in items:
        print(f"[{item['item_id']}] ({item['eisenhower_matrix']}) @{item['assignee']} -> {item['task_description']} (Due: {item['deadline']})")

    print("\n--- 2. Generating WeChat Work Collaborative Card ---")
    card = extractor.generate_task_card(items, "Tencent Cloud Infrastructure Sync")
    print(card["card_markdown"])

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

Licensed under the MIT License. Copyright © 2026 GenPark AI.