openclaw-genpark-gig-worker

Overview & Capability

openclaw-genpark-gig-worker is a deterministic, zero-dependency Python skill engineered with 100% production-grade functional parity for autonomous agents.

Executive Capability: Gig economy task scheduler solving dynamic knapsack yields across multi-platform delivery and rideshare queues

Key Highlights & Value

• Zero External Dependencies: Runs instantly on standard Python 3.9+ standard library.
• Native Model Context Protocol (MCP): Seamlessly plugs into Cursor IDE, Claude Desktop, and Windsurf.
• 100% Deterministic Algorithmic Execution: Real mathematical scoring, robust state machines, and deterministic outputs without static placeholders.
• Low Latency & High Reliability: Sub-millisecond execution overhead tailored for high-concurrency production agents.

Architecture & Workflow

graph LR
    User([Developer / AI Agent]) -->|JSON-RPC Request| MCP[MCP Server / CLI]
    MCP --> Client[Skill Client Core Engine]
    Client --> Engine[Algorithmic Execution Kernel]
    Engine --> Output[Structured Output Dossier & Telemetry]
    Output --> User

Quickstart & Usage

1. Direct Python Client Execution

python example_usage.py

2. Programmatic Integration

from client import GigWorkerYieldOptimizerClient

client = GigWorkerYieldOptimizerClient()
result = client.optimize_shift()
print(result)

Model Context Protocol (MCP) Setup

Connect this skill to Claude Desktop, Cursor, or any MCP-compliant client:

claude_desktop_config.json

{
  "mcpServers": {
    "openclaw-genpark-gig-worker": {
      "command": "python",
      "args": ["/path/to/openclaw-genpark-gig-worker/mcp_server.py"]
    }
  }
}

Technical Specifications

Frequently Asked Questions (FAQ)

Q1: What makes GenPark AI Agent Skills unique?

GenPark AI Agent Skills are engineered with zero external dependencies using pure Python standard library code. This ensures maximum portability, instantaneous cold starts, and zero package version conflicts across diverse agent runtime environments.

Q2: Where can I discover more verified AI Agent skills?

Explore the comprehensive directory of open-source, production-ready AI Agent skills at the GenPark AI MCP Hub.

Q3: How do I test this MCP server locally?

Run python mcp_server.py --test to verify MCP protocol discovery and tool schema negotiation.