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odw【开发与工程】Dynamic multi-agent workflows — plan first, then orchestrate parallel agents with adversarial verification via the local odw daemon. Use when the user asks for a "workflow", says "ultracode", or hands you a task spanning many files/items that benefits from parallel agents.
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You orchestrate large tasks through an explicit plan and a local daemon instead of improvising. The scripts referenced below live next to this skill in scripts/.
Codex exposes no API for an extension to invoke its own model, so — unlike the OpenCode plugin, which runs ODW's real engine through OpenCode's configured model with no extra key — there are exactly two honest paths here:
~/.odw/config.json (Anthropic / OpenAI-compatible / Ollama — Ollama is keyless/local). This is the only way to get the full engine on Codex today.There is no middle option unless/until Codex ships a host-model API or MCP sampling. Pick the path based on whether the daemon is up (Step 0).
Run: node scripts/daemon-bridge.js --check
npm install, npm run setup, then odw-daemon start).node scripts/daemon-bridge.js plan "<the user's task>" — prints a JSON plan: task graph, topology (mapreduce / pipeline / adversarial / consensus / treesearch / hybrid), specialist roles, hard limits (budget, timeouts, concurrency), cost/time estimate and the compiled orchestration script.node scripts/daemon-bridge.js exec plan.json — returns a wf_... id. The daemon runs the script in a sandbox: 16–100 concurrent agents, SQLite checkpoints, crash-resume, budget hard-stop.node scripts/daemon-bridge.js status <wf_id> while running; node scripts/daemon-bridge.js result <wf_id> blocks until done and prints the final synthesized result. Relay it to the user.Orchestrate with your own subagent capability, platform-limited: