genpark-personal-agent-unified-autonomous-copilot-skill
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@genpark-personal-agent-unified-autonomous-copilot-skilladd 'pick up dry cleaning' to my agenda and remind me at 5pm"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
genpark-personal-agent-unified-autonomous-copilot-skill
🌐 GenPark MCP Hub • 📦 GenPark Official • 📖 Documentation
📌 Overview & Capability
genpark-personal-agent-unified-autonomous-copilot-skill is a deterministic, high-performance, zero-dependency Python tool and native Model Context Protocol (MCP) server engineered for next-generation personal AI agents (distilling breakthrough capabilities from Today AI, Manus, Cue, Meta, Muse, and Instinct).
Executive Capability: Master cognitive personal copilot integrating Today AI (agenda), Manus (autonomous sandbox), Cue (ambient cues), Instinct (fast reflex), and Muse (episodic memory).
⚡ Key Highlights
🐍 Zero External
pipDependencies: Implemented entirely with pure Python standard library for instant zero-overhead execution.🔌 Native Model Context Protocol (MCP): Plugs directly into any MCP-compliant client via JSON-RPC 2.0 stdio.
🧠 Personal Agent Cognitive Architecture: Fast subconscious intent reflexes, ambient screen/clipboard cues, episodic life memory, and deep autonomous task resolution.
🛡️ Production-Hardened: Comprehensive error handling, boundary validation, and telemetry.
Related MCP server: pasm-mcp-server
🏗️ Architecture
graph LR
User([👤 User / Ambient Environment]) -->|Sensory Signals & Goals| Core[⚡ genpark-personal-agent-unified-autonomous-copilot-skill Engine]
Core --> Memory[(🧠 Episodic & Context Graph)]
Core --> Executor[🤖 Autonomous Action Pipeline]
Executor --> Result[📊 Proactive Action & Telemetry]
Result --> User🚀 Quickstart & Usage
1. Direct Python Client Execution
python example_usage.py2. Programmatic Integration
from client import PersonalAgentUnifiedCopilot
client = PersonalAgentUnifiedCopilot()
result = client.run_unified_copilot_benchmark()
print(result)🔌 Model Context Protocol (MCP) Setup
Connect this skill to Claude Desktop, Cursor, or any MCP-compliant client:
claude_desktop_config.json
{
"mcpServers": {
"genpark-personal-agent-unified-autonomous-copilot-skill": {
"command": "python",
"args": ["/path/to/genpark-personal-agent-unified-autonomous-copilot-skill/mcp_server.py"]
}
}
}Direct MCP Testing
python mcp_server.py --test📊 Technical Specifications
Parameter | Type | Required | Description |
|
| Yes | Sensory inputs, task goals, or ambient telemetry |
|
| No | Cognitive depth, energy profiles, or execution timeouts |
This server cannot be deployed
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