For agents: This skill is authored in
roboflow/computer-vision-skills. If your client already has it loaded as a skill (for example from a Roboflow plugin), use that copy. Theskill://roboflow/<name>/SKILL.mdMCP resources serve the same skills for clients that don't have them loaded.
Roboflow Universe
Open repository of 1M+ computer vision datasets and 50K+ pre-trained models at universe.roboflow.com.
URL Patterns
| 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 |
Searching Universe
MCP Tool
Use universe_search to find datasets/models programmatically. Pass a descriptive query (e.g. "hard hat detection construction site").
When someone must choose a dataset after seeing it (previews, classes, license, image counts), share the Universe links from the results so they can browse the listings before deciding.
Web Search
Search is hybrid — combines semantic similarity with keyword matching. Use specific, descriptive queries for best results.
Query Operators
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 |
Tips for Effective Queries
- Combine free-text with operators:
pothole road damage class:pothole images>100 sort:stars - Add
modelto only get inference-ready datasets - Include project type keywords to filter:
helmet instance segmentation - Use
class:when you know exactly what classes you need - Use specific object names, not generic terms ("forklift in warehouse" > "vehicle")
Evaluating a Dataset
Before 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 |
Licenses
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 |
Forking a Dataset
Fork = copy a Universe dataset into your workspace (no download/re-upload needed).
- Open dataset on Universe
- Click Download Dataset button
- Choose Train a model with this dataset (full fork) or Train from a portion of this dataset (partial clone)
- Dataset copies into your workspace
After forking you can: rename classes, add/remove images, generate versions, train models.
Requires: Logged-in Roboflow account.
Downloading a Dataset
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.
Using a Universe Model
Direct Inference via Workflows
- Create a Workflow in Roboflow
- Add a model block
- Switch to Public Models tab
- Paste the model ID from the Universe model page (copy icon at top)
- Click Use model ID
Model ID format: {owner}/{project}/{version} (shown on Universe model page).
Checkpoint Training
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.
Inference Metrics (shown on model page)
| Project Type | Metrics Shown |
|---|---|
| Object Detection | mAP, precision, recall |
| Classification | Accuracy |
| Segmentation | mAP, precision, recall |
MCP Tool Reference
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.
Related Skills
skill://roboflow/roboflow-data-management/SKILL.md— managing datasets after importskill://roboflow/roboflow-training-and-evaluation/SKILL.md— training on forked data
