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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.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
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