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name: ultralyticspro description: "Routes UltralyticsPro workflows for YOLO and RT-DETR training, prediction, and preset selection from the repository's wrapper scripts and model notes." disable-model-invocation: true metadata: disco-role: operating license: NO_LICENSE

UltralyticsPro

Use this repo skill when a user asks to train, predict, or adapt the wrapper scripts in this repository for Ultralytics YOLO or RT-DETR workflows.

This repository is a script collection, not an installable Python package. The runtime skill therefore centers on the public ultralytics package and the repo-maintained wrappers in scripts/ and sub-skills/.

Start here

1. Install the public dependency set:
   • python -m pip install ultralytics
   • If you plan to execute GPU training, install a PyTorch build that matches your platform and CUDA driver.
2. Run the smoke helper:
   • python scripts/check_ultralytics_env.py --show-presets
3. Read references/interface-reference.md when you need verified Ultralytics API signatures or CLI syntax.
4. Read references/model-family-map.md when you need to translate one of the source scripts into the bundled training or prediction presets.
5. Read references/troubleshooting.md for cross-cutting import, config, weight, data, and device failures.

Minimal import check

python -c "from ultralytics import YOLO, RTDETR; print(YOLO, RTDETR)"

Route map

sub-skills/training

Use this route for any task that sounds like:

• train, finetune, or resume a YOLO or RT-DETR model
• reproduce train_v8.py, train_yolo11.py, train_yolov10.py, train_yolo12.py, train_cls.py, train_obb.py, train_pose.py, train_seg01.py, or train_rtdetr.py
• choose a model-family preset, dataset YAML, image size, batch size, or device for a training run

Read the sub-skill's workflow and troubleshooting references before launching an actual run, because some presets require packaged config paths or a custom local YAML file.

sub-skills/prediction

Use this route for any task that sounds like:

• predict, infer, or run a single-image YOLO detection example
• reproduce predict_v8.py, predict_yolo11.py, or predict_yolov10.py
• choose a model weight file, source image, confidence threshold, or output directory for a prediction run

Read the sub-skill's workflow reference before executing a real inference, because the first run may download pretrained weights if they are not already cached.

Shared runtime helper

• scripts/check_ultralytics_env.py — run this first when you need to confirm the installed Ultralytics version, packaged config paths, sample assets, or the preset index.

What this skill does not cover

• Modifying the upstream ultralytics source code.
• Exporting this repo into another agent's skill library.
• Large training studies, benchmark sweeps, or long-running evaluation jobs.
• Native dataset preparation beyond the dataset names and YAMLs already used by the wrappers.

For provenance, see references/repo-provenance.md when you need to check whether this skill still matches the current checkout or before refreshing it.