Use Gizmo from your agent

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The 60-second setup

1. Create an API key at gizmo.antimlabs.com/developers (gzm_k1_…).
2. Paste the prompt below into Claude Code, Codex, or any agent with shell access.

You have access to the Gizmo API (3D simulation authoring). Base URL:
https://api.gizmo.antimlabs.com — auth with header "Authorization: Bearer $GIZMO_API_KEY".

- Generate a scene:  POST /v1/scenes  {"prompt": "...", "reference_image_urls": ["https://..."]}
- Generate an asset: POST /v1/assets  {"prompt": "..."}  (scene auto-created if omitted)
- Both return 202 with {job_id, estimated_seconds, estimate_note} — TELL THE USER the
  estimate, then poll GET /v1/jobs/{job_id} at a sensible interval (or stream
  GET /v1/jobs/{job_id}/events, SSE). Job statuses: queued → running → succeeded | failed | cancelled.
- When succeeded: fetch the scene (GET /v1/scenes/{id}?include_graph=true) or export it —
  POST /v1/scenes/{id}/export {"format": "mjcf" | "usd" | "usdz" | "sdf"} for MuJoCo / Isaac Sim / Gazebo.
- Build method is automatic: structured/jointed objects → prim-built articulated (~15–20 min);
  organic objects → mesh scan (~2–4 min). Reference images ground the build in real photos.
- Full reference: https://docs.gizmo.antimlabs.com/llms.txt

Task: generate {describe what you want}, wait for it, then export it for {simulator} and
report the download link.

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What your agent should know

• Everything is async. Generation returns a job_id immediately; the estimated_seconds in the 202 response is honest (minutes, not seconds) — relay it to your human and size polling accordingly. Don’t hot-poll: every 30–60s is plenty.
• No knobs needed. The API triages each request itself (build method, structure engine). A prompt and optionally reference images are the whole contract.
• Exports are the outcome. The point isn’t the chat — it’s an MJCF/USD/SDF bundle your robotics stack can load. Pipe the export into your repo, CI, or simulator as part of the loop.

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Loop ideas

• “Keep my sim assets fresh”: a scheduled Claude loop that reads your task list, generates missing assets via POST /v1/assets, and commits exports to your repo.
• Scene-per-scenario CI: your agent generates an environment per test scenario (POST /v1/scenes with a reference photo of the real space), exports MJCF, and runs your policy against it.
• Digital-twin refresh: when the real space changes, drop new photos in a bucket; an agent loop regenerates the scene from the latest reference images.