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name: cloud-api-workflows description: "Use the T-Rex2 DeepDataSpace cloud API wrapper for visual prompts, custom visual embeddings, and embedding-based detection." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Cloud API Workflows

Use this sub-skill when a task needs the T-Rex2 DeepDataSpace cloud API wrapper rather than local model inference. It covers interactive visual prompts, generic multi-reference visual prompts, visual embedding creation, and embedding-based detection.

Use this when

• Calling TRex2APIWrapper or explaining its request/response behavior.
• Building a visual prompt payload from local target/reference images and interaction rectangles or points.
• Running visual-prompt detection where the prompt image is the target image (interactive) or one/more separate reference images (generic).
• Creating a reusable visual prompt embedding from references.
• Running detection from a previously saved base64 visual embedding.
• Debugging token, network, API status, payload, or embedding-file failures.

Do not use this for

• Rendering-only work after detections already exist. Route to visualization-and-demo.
• Gradio server/UI behavior, prompt drawing widgets, or UI dependency details. Route to visualization-and-demo.
• Installation, editable install repair, setup.py/Torch build isolation, or Gradio dependency repair. Route to the root skill troubleshooting reference.

Required boundaries

• Live cloud calls require a DeepDataSpace token supplied as --token or T_REX_API_TOKEN; never print, persist, or commit the token.
• --dry-run on bundled scripts validates images and JSON, builds the converted payload summary, and makes no network call.
• The package is a cloud API wrapper. Local GPU/accelerator hardware is not required for the API workflows.
• TRex2APIWrapper.convert_visual_prompt mutates prompt dictionaries by replacing each prompt image with a base64 data URI; copy prompts first if callers still need original paths.
• Detection dictionaries use scores, labels, and boxes. If they need drawing, use this sub-skill's optional script flags or route to visualization-and-demo.

Entry points

• API contracts and schemas: references/api-reference.md
• Operator recipes: references/workflows.md
• API-specific failures: references/troubleshooting.md
• Visual/generic prompt CLI: scripts/run_visual_prompt_inference.py
• Visual embedding creation CLI: scripts/create_visual_embedding.py
• Embedding inference CLI: scripts/run_embedding_inference.py

Fast routing

1. If the user has images plus rectangle/point prompts, validate or create a prompt JSON and use run_visual_prompt_inference.py.
2. If the user wants a reusable category embedding from one or more prompted images, use create_visual_embedding.py.
3. If the user already has a base64 embedding text file, use run_embedding_inference.py.
4. If the user only wants to draw, threshold, or inspect an existing detection JSON, route to visualization-and-demo.