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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.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
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.
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.
| name | roboflow-api-reference |
| description | 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. |
For agents — source-of-truth: This skill is authored in
roboflow/computer-vision-skillsand shipped with the Roboflow plugin. If your client has loaded the plugin (you'll seeroboflow:<name>skills in your available skills list), use those local skills — they're read fresh from disk every session. The same content served as MCP resources atroboflow://skills/<name>/...is a fallback for clients without the plugin and may lag this repo. Don't callReadMcpResourceToolforroboflow://skills/...URIs when a localroboflow:<name>skill is available.
Tip: If you're connected to the Roboflow MCP server, prefer its tools (
projects_*,versions_*,models_*,workflows_*,images_*, …) over raw REST calls — they handle auth, pagination, and typed responses for you. The REST patterns below stay relevant if you're not using MCP.
| Host | Base URL | Purpose |
|---|---|---|
| Platform API | https://api.roboflow.com | CRUD for projects, images, versions, training, upload |
| Serverless Inference | https://serverless.roboflow.com | Model inference + Workflow execution |
| Dedicated Deployment | https://<name>.roboflow.cloud | Private GPU inference (same API as serverless) |
| Self-hosted Inference | http://localhost:9001 | Local inference server via inference package |
Use the inference-sdk Python package as the preferred client for all inference hosts. It handles auth, retries, and response parsing.
| Method | Where | Format |
|---|---|---|
| Query parameter | All hosts | ?api_key=YOUR_KEY |
| Request body | Platform API + Workflow inference | "api_key": "YOUR_KEY" in JSON body |
| Header | MCP server (mcp.roboflow.com) | x-api-key: YOUR_KEY (handled automatically by MCP) |
API keys are workspace-scoped. Get yours from Workspace Settings > API Keys in the Roboflow dashboard (app.roboflow.com/{workspace}/settings/api). Personal API keys are at /settings/account → API Keys tab.
| SDK | Install | Primary Use |
|---|---|---|
Python (inference-sdk) | pip install inference-sdk | Inference via InferenceHTTPClient |
Python (roboflow) | pip install roboflow | Upload, training, project management |
JavaScript (roboflow.js) | Browser script tag | Real-time on-device web inference |
| iOS (Swift) | CocoaPods/SPM | On-device mobile inference |
from inference_sdk import InferenceHTTPClient
CLIENT = InferenceHTTPClient(
api_url="https://serverless.roboflow.com", # or dedicated URL, or localhost
api_key="YOUR_KEY"
)
result = CLIENT.infer("image.jpg", model_id="your-project/1")
import roboflow
rf = roboflow.Roboflow(api_key="YOUR_KEY")
project = rf.workspace("my-workspace").project("my-project")
# Upload
project.upload(image_path="image.jpg", split="train")
# Inference
model = project.version(1).model
result = model.predict("image.jpg", confidence=40).json()
| Task | Host to Use |
|---|---|
| Run model inference | serverless.roboflow.com |
| Run Workflows | serverless.roboflow.com |
| Upload images | api.roboflow.com |
| Manage projects/versions | api.roboflow.com |
| Start training | api.roboflow.com |
| High-throughput / SLA inference | Dedicated deployment URL |
| Air-gapped / on-prem inference | Self-hosted localhost:9001 |
| Real-time video / webcam / RTSP | WebRTC via inference_sdk.webrtc against serverless or local — see roboflow://skills/inference/workflows ("Video Stream" section). Not a plain HTTP call. |
roboflow://skills/api-reference/inference — inference URL patterns, request/response formatsroboflow://skills/api-reference/rest-api — platform REST API endpoints (CRUD, upload, training)roboflow://skills/api-reference/api-key-management — creating and managing API keys via REST, MCP tools, and Python CLI. When creating a key, scope it to least privilege if the workspace has Advanced API Keys; if not, tell the user scoped keys are an Enterprise feature.