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genpark-workplace-goal-to-artifact-dag-orchestrator-skill

mcp
Guvenlik Denetimi
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SUMMARY

Enterprise Work Agent Goal-to-Artifact DAG Pipeline (inspired by WorkBuddy, Tencent Docs, WeChat Work Multi-Agent). Decomposes high-level business objectives into topological sub-tasks and directly synthesizes final deliverables: financial spreadsheets, executive presentations, and analytical reports.

README.md

genpark-workplace-goal-to-artifact-dag-orchestrator-skill

Python 3.9+
License MIT
MCP Compatible
GenPark AI
Zero Dependencies

Production-Grade Agentic Commerce & Work Agent Infrastructure Skill • 100% Standard Library Python • Native Model Context Protocol (MCP)

🌐 GenPark MCP Hub • 📦 GenPark Official • 📖 Documentation

🌟 Overview

genpark-workplace-goal-to-artifact-dag-orchestrator-skill delivers robust, industrial-grade capabilities bridging Consumer Agentic Commerce and Enterprise Workplace Execution. Built exclusively on the Python standard library with zero external runtime dependencies, it integrates seamlessly as a native Model Context Protocol (MCP) server or an importable Python module.

Enterprise Work Agent Goal-to-Artifact DAG Pipeline (inspired by WorkBuddy, Tencent Docs, WeChat Work Multi-Agent). Decomposes high-level business objectives into topological sub-tasks and directly synthesizes final deliverables: financial spreadsheets, executive presentations, and analytical reports.

💡 Key Capabilities

  • Zero-Dependency Architecture: Runs anywhere Python 3.9+ is installed without pip install overhead or supply-chain vulnerabilities.
  • Model Context Protocol (MCP) First: Compatible with Claude Desktop, Cursor, GenPark Engine, Meta Muse, and enterprise work agent frameworks.
  • Deterministic & Safe: Designed with cryptographic authorization tokens, role-based boundary validation, and structured telemetry.
  • High Concurrency & Low Latency: In-memory caching, transactional validation, and optimized execution loops.

🚀 Quickstart

1. Direct Python Usage

from client import WorkplaceGoalToArtifactDAGOrchestrator

client = WorkplaceGoalToArtifactDAGOrchestrator()
result = client.decompose_goal_to_dag()
print(result)

2. Standalone MCP Server Execution

Run the MCP server via standard JSON-RPC 2.0 stdio:

python mcp_server.py

Verify standard compliance and self-tests:

python mcp_server.py --test

3. Claude Desktop / Cursor MCP Configuration

Add this tool to your claude_desktop_config.json or Cursor MCP settings:

{
  "mcpServers": {
    "genpark-workplace-goal-to-artifact-dag-orchestrator-skill": {
      "command": "python",
      "args": ["/absolute/path/to/genpark-workplace-goal-to-artifact-dag-orchestrator-skill/mcp_server.py"]
    }
  }
}

🛠️ Verification & Testing

Run the included verification suite:

python example_usage.py

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

Developed with ❤️ by the GenPark Autonomous Agent Ecosystem Team.

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