--- name: yolo-auto-d5f62fd1401f 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 ```bash codex $yolo-auto "Execute full-stack development workflow" ``` ### GPU Selection ```bash codex $yolo-auto "Select claude-3-opus for complex reasoning task" ``` ### Multi-Agent Task ```bash codex $yolo-auto "Distribute tasks: 3 agents for frontend, 2 for backend" ``` ## YOLO Architecture ``` ┌─────────────────────────────────────────┐ │ YOLO Orchestrator │ ├─────────────────────────────────────────┤ │ ┌─────────────┐ ┌─────────────┐ │ │ │ GPU Router │───▶│ Task Queue │ │ │ └─────────────┘ └─────────────┘ │ │ │ │ │ │ ▼ ▼ │ │ ┌─────────────┐ ┌─────────────┐ │ │ │ Agent Pool │◀───│ Work Stealer │ │ │ │ - claude-3 │ └─────────────┘ │ │ │ - gpt-4 │ │ │ │ - gpu-5.3 │ │ │ └─────────────┘ │ │ │ │ │ ▼ │ │ ┌─────────────┐ │ │ │ Result │ │ │ │ Aggregator │ │ │ └─────────────┘ │ └─────────────────────────────────────────┘ ``` ## GPU Models | Model | Strengths | Use Cases | | ------------- | -------------------------- | ------------------------ | | claude-3-opus | Complex reasoning, coding | Architecture, planning | | gpt-4 | Fast iteration, creativity | Prototyping, exploration | | gpu-5.3-codex | Local processing | Security, privacy tasks | ## OpenClaw Integration OpenClaw provides MCP bridge for external tools: ```json { "mcpServers": { "openclaw": { "command": "openclaw", "args": ["serve"] } } } ``` ## Workflow Definition ```yaml name: full-stack-web stages: - name: frontend agents: 2 gpu: claude-3-opus tasks: - Create React components - Implement state management - name: backend agents: 2 gpu: gpt-4 tasks: - Design API schema - Implement endpoints - name: integration agents: 1 gpu: claude-3-opus tasks: - Connect frontend to backend - Test end-to-end ``` ## Output Format The yolo-auto agent provides: - Workflow execution reports - Agent task distributions - GPU utilization metrics - Result aggregations - Performance statistics ## References - [OpenClaw GitHub](https://github.com/anomalyco/openclaw) - [MCP Protocol](https://modelcontextprotocol.io/) - [Claude Code](https://claude.com/code) - [Multi-Agent Systems](https://arxiv.org/abs/2308.02790) --- **Installation**: `$ codex $skill-install https://github.com/zapabob/codex-yolo-auto-skill` **Version**: 1.0.0 **Compatibility**: Codex v2.14.0+ --- Source attribution: zapabob Original project: https://github.com/zapabob/clawdbot/blob/ef458153ddced8da7b0ac8a06b58c4ad391488f5/extensions/universal-skills/skills/yolo-auto/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2025 Peter Steinberger Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: zapabob Original project: https://github.com/zapabob/clawdbot/blob/ef458153ddced8da7b0ac8a06b58c4ad391488f5/extensions/universal-skills/skills/yolo-auto/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Development & Engineering, Product Management Roles: Backend Developer, Data Scientist, DevOps / SRE, ML / AI Engineer, Product Manager Specialties: API, Backend Tools: Docker, OpenAI API, Openclaw, Python Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 4b5649713e75103e4efb94d7714c824b94f8cd2bdd5d8733e9a0f380835fa22a