roboflow

Use when uploading images, labeling, organizing datasets, creating Roboflow projects (detection/segmentation/keypoint/classification), tags, splits, versions, or RoboQL search.

Use when searching for or using public datasets/models on Roboflow Universe (universe.roboflow.com), the open repository of 1M+ computer vision datasets and 50K+ pre-trained models.

Use when training Roboflow models or improving accuracy - covers architecture selection, model IDs, checkpoints, evaluation metrics, and the iterative improvement playbook.

Deployment option comparison (serverless, dedicated, self-hosted, batch) and Workflow execution patterns. For raw API URL patterns, auth, and request/response formats, see roboflow-api-reference.

Protocol-level facts for Roboflow REST and Inference APIs — URL patterns, auth, parameters, error codes, and SDK quick-start. For deployment strategy and Workflow execution patterns, see roboflow-inference.

Use when answering questions about Roboflow plans, credit usage, or cost estimation; directs users to roboflow.com/pricing for current dollar amounts.

Use when explaining where Roboflow features live in the app.roboflow.com web app, mapping intents like upload, annotate, train, deploy to specific page URLs.

Add a new core (pre-trained, non-user-fine-tuned) model to the inference repos. Covers the inference_models class + registry entry + tests, weight-zip preparation, the registration script against the Roboflow model registry, and the optional surfaces (workflow block, legacy-endpoint adapter). Trigger when the user asks to "add a model", "port a new model from HuggingFace", "wrap a transformers model", "expose X as a workflow block / model", or similar. This is a living skill — iterate on it each time a new model ships.