roboflow

Trigger when the user asks to "add a model", "add a core / pre-trained model", "port a model from HuggingFace", "wrap a transformers model", or "expose X as a workflow block / model". Also when a diff adds a subfolder under inference_models/inference_models/models/<family>/, a new (architecture, task, BackendType) key in auto_loaders/models_registry.py, an adapter in inference/core/models/inference_models_adapters.py, or a block under core_steps/models/foundation/<family>/.

Author a new Roboflow Workflows block under `inference/core/workflows/core_steps/**`. Trigger on a new/changed `vN.py` defining a `WorkflowBlockManifest` + `WorkflowBlock` pair; new `load_blocks()`/`KINDS_SERIALIZERS` entries in `core_steps/loader.py`; new `Kind(...)` in `execution_engine/entities/types.py`; or a request to "create/add a workflow block" or "expose X as a workflow step" (model / transform / sink / visualization / flow control).

PRs changing inference_cli/ except server.py, container_adapter.py, tunnel_adapter.py, infer_adapter.py. Diff signals — typer.Option/typer.Argument, cloud_app/rf_cloud_app/benchmark_app/enterprise_app, get_workspace/handle_response_errors, @backoff.on_exception(..., RetryError), serialization_alias/alias= on pydantic Fields, REQUEST_TIMEOUT config constants, cloud_adapter.py (SkyPilot deploy), inference_cli/lib/enterprise/**, tests/inference_cli/**, docs/inference_helpers/cli_commands/**.

Load when a PR touches inference_models/inference_models/** (models/base/, models/common/, models/auto_loaders/**, weights_providers/**, runtime_introspection/**, errors.py, configuration.py), inference_models/pyproject.toml, inference_models/uv.lock, or inference_models/docs/**. Diff signals: a REGISTERED_MODELS entry, LazyClass/RegistryEntry, from_pretrained, to_supervision, a BaseInferenceModelsError subclass, an INFERENCE_MODELS_* env var, torch.jit.load/script.

Review guidance for PRs touching .github/ (workflows, actions, prompts/claude-pr-review.md, CODEOWNERS), docker/ (dockerfiles, publish, config, entrypoint), requirements/*.txt, .release/pypi/*.setup.py, Makefile, inference/core/version.py, and inference_models/{pyproject.toml,uv.lock}. Diff signals: new/edited GitHub Actions workflow or action.yml, Dockerfile edits, requirements-pin bumps, version bumps, tag/publish logic.

Load when a PR changes a user/dev-visible contract or is release-bound. Triggers on diffs to `inference/core/version.py`, `EXECUTION_ENGINE_V1_VERSION`, `inference/core/interfaces/http/http_api.py` or `inference/core/entities/**`, a Workflow block manifest under `inference/core/workflows/core_steps/**` (`type` Literal, `kinds`, `get_execution_engine_compatibility`), a public `inference_models` API, `inference_models/pyproject.toml`, a persisted/cache format, or `requirements/*.txt` / `uv.lock`.

Review PRs touching inference/core/workflows/core_steps/** (Workflow blocks — {category}/{block}/vN.py, common/, common/query_language/**, loader.py) or inference/core/workflows/execution_engine/entities/types.py (new kind). Diff signals — new block package, new/changed vN.py, new type Literal or legacy alias, describe_outputs()/run() changes, load_blocks()/load_kinds()/KINDS_SERIALIZERS edits, new *_KIND, sv.Detections.data writes, serializers/deserializers changes.

Review guidance for PRs touching inference/core/workflows/execution_engine/**, inference/core/workflows/errors.py, inference/core/workflows/prototypes/block.py, docs/workflows/execution_engine_changelog.md, or .cursor/rules/execution-engine-version-changelog.mdc. Diff signals: EXECUTION_ENGINE_V1_VERSION, REGISTERED_ENGINES, COMPILATION_CACHE, contains_future/resolve_futures, ClientCausedStepExecutionError, get_output_dimensionality_offset. NOT core_steps block implementations.

Use when uploading locally trained model weights (YOLO, RF-DETR, YOLO-NAS, PaliGemma, Florence-2) to Roboflow — the client-side Python SDK upload flow, safe API key handling, per-family packaging requirements, and verifying the upload.

Use when training Roboflow models, improving accuracy, or setting up a production feedback loop — covers architecture selection, model IDs, checkpoints, evaluation metrics, the iterative improvement playbook, and active learning via the Dataset Upload workflow block.

Use when connecting cloud storage (AWS S3 / S3-compatible or Google Cloud Storage) to Roboflow to mirror images into a workspace — creating storage credentials, defining datasources (bucket-mirror configs), selecting objects with glob rules, validating access, and running/scheduling mirror jobs.

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

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.

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.

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.

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.