yolo-auto

name	yolo-auto
description	YOLO (You Only Live Once) full-stack automation skill with OpenClaw MCP bridge and GPU model selection.

YOLO Auto Agent Skill

Overview

YOLO (You Only Live Once) full-stack automation skill with OpenClaw MCP bridge and GPU model selection. Enables autonomous multi-agent orchestration with intelligent task distribution across different GPU models.

Capabilities

• GPU Model Selection: Select optimal GPU model (claude-3-opus, gpt-4, gpu-5.3-codex)
• Multi-Agent Orchestration: Coordinate multiple agents for complex tasks
• OpenClaw Integration: Use OpenClaw MCP bridge for tool access
• Task Distribution: Distribute work across agents intelligently
• Result Aggregation: Collect and synthesize results from multiple sources
• Autonomous Execution: Execute complex workflows with minimal input

Tools Required

MCP Tools

• yolo_select_gpu - Select GPU model for task
• yolo_distribute_task - Distribute task to agents
• yolo_aggregate_results - Aggregate results from agents
• yolo_execute_workflow - Execute multi-step workflow
• yolo_monitor_progress - Monitor workflow progress
• openclaw_execute - Execute via OpenClaw bridge

File System Access

• Read: Full project access
• Write: ./yolo-workflows/, ./artifacts/, ./results/

Network Access

• None required - Local OpenClaw operations

Shell Commands

• openclaw - OpenClaw CLI
• python - Python for orchestration scripts
• git - Version control

Usage Examples

Basic Workflow

codex $yolo-auto "Execute full-stack development workflow"

GPU Selection

codex $yolo-auto "Select claude-3-opus for complex reasoning task"

Multi-Agent Task

codex $yolo-auto "Distribute tasks: 3 agents for frontend, 2 for backend"

YOLO Architecture

┌─────────────────────────────────────────┐
│           YOLO Orchestrator              │
├─────────────────────────────────────────┤
│  ┌─────────────┐    ┌─────────────┐    │
│  │ GPU Router  │───▶│ Task Queue   │    │
│  └─────────────┘    └─────────────┘    │
│         │                  │            │
│         ▼                  ▼            │
│  ┌─────────────┐    ┌─────────────┐    │
│  │ Agent Pool  │◀───│ Work Stealer │    │
│  │ - claude-3  │    └─────────────┘    │
│  │ - gpt-4     │                        │
│  │ - gpu-5.3   │                        │
│  └─────────────┘                        │
│         │                                │
│         ▼                                │
│  ┌─────────────┐                        │
│  │ Result       │                        │
│  │ Aggregator  │                        │
│  └─────────────┘                        │
└─────────────────────────────────────────┘

GPU Models

Model	Strengths	Use Cases
claude-3-opus	Complex reasoning, coding	Architecture, planning
gpt-4	Fast iteration, creativity	Prototyping, exploration
gpu-5.3-codex	Local processing	Security, privacy tasks

OpenClaw Integration

OpenClaw provides MCP bridge for external tools:

{
  "mcpServers": {
    "openclaw": {
      "command": "openclaw",
      "args": ["serve"]
    }
  }
}

Workflow Definition

name: full-stack-web
stages:
  - name: frontend
    agents: 2
    gpu: claude-3-opus
    tasks:
      - Create React components
      - Implement state management
  - name: backend
    agents: 2
    gpu: gpt-4
    tasks:
      - Design API schema
      - Implement endpoints
  - name: integration
    agents: 1
    gpu: claude-3-opus
    tasks:
      - Connect frontend to backend
      - Test end-to-end

Output Format

The yolo-auto agent provides:

• Workflow execution reports
• Agent task distributions
• GPU utilization metrics
• Result aggregations
• Performance statistics

References

• OpenClaw GitHub
• MCP Protocol
• Claude Code
• Multi-Agent Systems

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Installation: $ codex $skill-install https://github.com/zapabob/codex-yolo-auto-skill Version: 1.0.0 Compatibility: Codex v2.14.0+