القائمة
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.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
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.
| name | roboflow-universe |
| description | 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. |
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.
Open repository of 1M+ computer vision datasets and 50K+ pre-trained models at universe.roboflow.com.
| Page | URL | Content |
|---|---|---|
| Home | universe.roboflow.com | Search, trending projects, categories |
| Project | universe.roboflow.com/{owner}/{project} | Overview, classes, metrics, license, fork |
| Images | universe.roboflow.com/{owner}/{project}/browse | Browse images with annotations |
| Dataset version | universe.roboflow.com/{owner}/{project}/dataset/{version} | Version details, splits, download |
| Model | universe.roboflow.com/{owner}/{project}/model/{version} | Try model, metrics, deploy snippet |
universe_search_app)Use when someone must choose a dataset after seeing it: previews, classes, license, image counts, etc. Pure MCP JSON hits from universe_search are not a substitute for that UX — open the app when the decision needs eyes on the listings.
Use universe_search to find datasets/models programmatically. Pass a descriptive query (e.g. "hard hat detection construction site").
Search is hybrid — combines semantic similarity with keyword matching. Use specific, descriptive queries for best results.
All operators can be mixed with free-text: fire smoke class:fire,smoke images>200 model
| Operator | Example | Effect |
|---|---|---|
model | waste detection model | Only datasets with a trained model |
object detection | helmet object detection | Filter by project type (also: classification, instance segmentation, keypoint detection) |
class:X | class:helmet,person | Must contain these classes |
tag:X | tag:safety | Filter by Universe tag |
model:X | model:yolov8 | Filter by trained model architecture |
images>N | images>500 | Min image count (also >=, <, <=, =) |
stars>=N | stars>=5 | Min star count |
views>N | views>1000 | Min view count |
downloads>N | downloads>100 | Min download count |
updated:Nd | updated:30d | Updated within N days (also h, w, mo, y) |
sort:X | sort:stars | Sort by field (stars, images, updated, downloads, views) |
like:dataset-url | like:coco | Find similar datasets |
pothole road damage class:pothole images>100 sort:starsmodel to only get inference-ready datasetshelmet instance segmentationclass: when you know exactly what classes you needBefore forking, check these signals:
| Criterion | Where to Look | Good Sign |
|---|---|---|
| Class coverage | Classes list on project page | All your target classes present |
| Image count | Project overview | 500+ images per class for detection |
| Annotation quality | Browse > click individual images | Tight bounding boxes, consistent labels |
| Class balance | Project overview / health | No extreme class imbalance |
| Image diversity | Browse images | Varied lighting, angles, backgrounds |
| License | "Cite this Project" section | Compatible with your use case (see below) |
| Model metrics | Model tab (if available) | mAP > 70% suggests decent annotations |
Found in the "Cite this Project" section on the project page. No license listed = all rights reserved.
| License | Commercial Use | Modify | Attribution Required |
|---|---|---|---|
| Public Domain | Yes | Yes | No |
| CC BY 4.0 | Yes | Yes | Yes |
| MIT | Yes | Yes | Yes (in license copy) |
| BY-NC-SA 4.0 | No | Yes (share-alike) | Yes |
| ODbL v1.0 | Yes | Yes (share-alike for DB) | Yes |
| No license specified | Assume No | Assume No | N/A |
Fork = copy a Universe dataset into your workspace (no download/re-upload needed).
After forking you can: rename classes, add/remove images, generate versions, train models.
Requires: Logged-in Roboflow account.
For local/notebook training instead of Roboflow cloud training.
| Method | When to Use |
|---|---|
| Train a model with this dataset (fork) | Training on Roboflow, want full data in workspace |
| Train from a portion (clone) | Want a subset or to combine with other data |
| Download dataset | Local training via code snippet or ZIP file |
Supports all standard export formats (COCO, YOLO, VOC, CreateML, TFRecord, etc.).
Path: Project page > Download Dataset button > choose method.
Model ID format: {owner}/{project}/{version} (shown on Universe model page).
Fork the dataset, then train your own model using the forked data. Use a Universe model's architecture as a starting point via Roboflow Train.
| Project Type | Metrics Shown |
|---|---|
| Object Detection | mAP, precision, recall |
| Classification | Accuracy |
| Segmentation | mAP, precision, recall |
universe_search — Search Universe for datasets/models.
| Param | Type | Default | Notes |
|---|---|---|---|
query | str (required) | — | Search query text |
result_type | "dataset" | "model" | null | null | Filter by result type |
limit | int | 12 | Max results per page |
page | int | 1 | Page number (1-indexed) |
Returns: name, url, type, classes, classCount, images, description, tags, license, stars, views, downloads, modelCount, latestVersion.
roboflow://skills/data-management/SKILL — managing datasets after importroboflow://skills/training-and-evaluation/SKILL — training on forked data