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claude-flow

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Ruflo - Enterprise AI agent orchestration for Claude Code. Deploy 60+ specialized agents in coordinated swarms with self-learning, fault-tolerant consensus, vector memory, and MCP integration

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--- name: dual-spawn description: Spawn headless Codex workers from Claude Code for parallel execution --- # Dual Spawn Skill Spawn multiple headless Codex workers to run tasks in parallel while you continue working interactively. ## Usage ``` /dual-spawn "<task>" --workers <count> [--type <worker-type>] ``` ## Parameters | Parameter | Default | Description | |-----------|---------|-------------| | `task` | required | Task description for workers | | `--workers` | 3 | Number of parallel workers | | `--type` | coder | Worker type: coder, tester, docs, reviewer | | `--wait` | false | Wait for completion | ## Examples ### Spawn Implementation Workers ``` /dual-spawn "Implement user authentication" --workers 2 --type coder ``` ### Spawn Test Writers ``` /dual-spawn "Write comprehensive tests for auth module" --workers 2 --type tester ``` ### Spawn Documentation Workers ``` /dual-spawn "Document all API endpoints" --workers 1 --type docs ``` ## How It Works 1. Initializes shared swarm coordination 2. Spawns headless Codex workers with `claude -p` 3. Each worker searches memory for relevant patterns 4. Workers execute in parallel 5. Results stored in shared memory ## Generated Commands ```bash # Initialize coordination npx claude-flow swarm init --topology hierarchical --max-agents {{workers}} # Spawn workers {{#each workers}} claude -p " You are worker-{{@index}}. TASK: {{task}} 1. Search: memory_search(query='{{task_keywords}}') 2. Execute your assigned work 3. Store: memory_store(key='result-{{@index}}', namespace='results', upsert=true) " --session-id task-{{@index}} & {{/each}} echo "Spawned {{workers}} headless workers" ``` ## After Spawning Use `/dual-collect` to gather results: ``` /dual-collect --namespace results ``` Or check manually: ```bash npx claude-flow memory list --namespace results ```