For agents — source-of-truth: This skill is authored in roboflow/computer-vision-skills and shipped with the Roboflow plugin. If your client has loaded the plugin (you'll see roboflow:<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 at roboflow://skills/<name>/... is a fallback for clients without the plugin and may lag this repo. Don't call ReadMcpResourceTool for roboflow://skills/... URIs when a local roboflow:<name> skill is available.

Roboflow Universe

Open repository of 1M+ computer vision datasets and 50K+ pre-trained models at universe.roboflow.com.

URL Patterns

Searching Universe

MCP app (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.

MCP Tool

Use universe_search to find datasets/models programmatically. Pass a descriptive query (e.g. "hard hat detection construction site").

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

Tips for Effective Queries

• Combine free-text with operators: pothole road damage class:pothole images>100 sort:stars
• Add model to 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:

Licenses

Found in the "Cite this Project" section on the project page. No license listed = all rights reserved.

Forking a Dataset

Fork = copy a Universe dataset into your workspace (no download/re-upload needed).

1. Open dataset on Universe
2. Click Download Dataset button
3. Choose Train a model with this dataset (full fork) or Train from a portion of this dataset (partial clone)
4. 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.

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

1. Create a Workflow in Roboflow
2. Add a model block
3. Switch to Public Models tab
4. Paste the model ID from the Universe model page (copy icon at top)
5. 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)

MCP Tool Reference

universe_search — Search Universe for datasets/models.

Returns: name, url, type, classes, classCount, images, description, tags, license, stars, views, downloads, modelCount, latestVersion.

Related Skills

• roboflow://skills/data-management/SKILL — managing datasets after import
• roboflow://skills/training-and-evaluation/SKILL — training on forked data