---

name: ultralytics description: "Use this skill for Ultralytics YOLO package workflows: CLI/Python model usage, data/config setup, train/val, prediction/results, export/deployment, tracking/solutions, model-family selection, and repo development." disable-model-invocation: true metadata: disco-role: operating license: AGPL 3.0

Ultralytics Repo Skill

Use this skill when a user asks for help with Ultralytics YOLO workflows, the ultralytics Python package, or this repository's public APIs and maintainer tasks. Ultralytics covers detection, instance segmentation, semantic segmentation, classification, pose, oriented boxes, tracking, model export, deployment helpers, and analytics solutions.

Start Here

• Read references/repo-provenance.md when deciding whether this skill matches the current checkout or needs refresh.
• Read references/routing-map.md when a request spans more than one workflow or could route to multiple sub-skills.
• Read references/version-and-capability-notes.md for version-sensitive items such as YOLO26, semantic segmentation, SAM3, downloads, optional extras, and backend requirements.
• Read references/shared-cli-config-keys.md before validating yolo TASK MODE arg=value syntax or translating Python kwargs to CLI args.
• Run scripts/check_ultralytics_env.py --json to inspect an active environment without downloads, training, export, or media processing.

Route by User Goal

• Data and configuration: use sub-skills/data-and-configuration/SKILL.md for dataset YAMLs, label layout, config defaults, CLI/Python arg translation, converters, and safe command planning.
• Training and validation: use sub-skills/training-and-validation/SKILL.md for model.train(), model.val(), model.tune(), yolo train, yolo val, resume, devices, metrics, and tuning.
• Inference and results: use sub-skills/inference-and-results/SKILL.md for model.predict(), model(source), yolo predict, source types, streaming, batching, Results extraction, saving, and thread-safe inference.
• Export and deployment: use sub-skills/export-and-deployment/SKILL.md for model.export(), yolo export, benchmark, ONNX/OpenVINO/TensorRT/CoreML/TFLite and deployment-format troubleshooting.
• Tracking and solutions: use sub-skills/tracking-and-solutions/SKILL.md for model.track(), yolo track, tracker YAMLs, ReID/deep trackers, object counting, heatmaps, speed/queue/region workflows, Streamlit, and yolo solutions.
• Model families and tasks: use sub-skills/model-families-and-tasks/SKILL.md for choosing YOLO, YOLOWorld, YOLOE, NAS, SAM, FastSAM, or RTDETR, and for mapping detect/segment/semantic/classify/pose/OBB tasks to outputs.
• Repo development: use sub-skills/repo-development/SKILL.md for editing this repository, selecting focused tests, docs/style checks, optional extras, CI-like verification, and maintainer-safe native checks.

Common First Decisions

• CLI shape: Ultralytics uses yolo TASK MODE arg=value; avoid normal --flag value syntax for YOLO config arguments.
• Downloads: names such as yolo26n.pt, sam3.pt, or coco8.yaml may download weights or datasets. Prefer explicit local paths for offline or deterministic work.
• Task outputs: detection uses boxes, segmentation uses boxes and masks, semantic segmentation uses dense semantic_mask, classification uses probs, pose uses keypoints, and OBB uses rotated geometry.
• Side effects: training, validation, prediction, export, tracking, and solutions can write runs, labels, media, or exports. Set project, name, exist_ok, save=False, or dry-run helper scripts when deterministic output matters.
• Optional dependencies: install extras narrowly. Use export extras only for export workflows, solutions extras for analytics apps, logging extras for integrations, and dev extras for repository checks.
• Hardware: GPU acceleration is optional for many inspections, but TensorRT, CUDA export, large training, and some ReID/deep trackers need compatible GPU packages and drivers.

Safe Baseline

pip install ultralytics
python - <<'PY'
import ultralytics
print(ultralytics.__version__)
print("YOLO" in dir(ultralytics))
PY
yolo help

For local repository development, use editable install only in a disposable or project-specific environment and keep optional extras narrow. Do not install broad extras such as dev, export, solutions, or logging unless the selected workflow actually needs them.

Bundled Helpers

• scripts/check_ultralytics_env.py: reports package versions, CLI availability, and optional backend modules in the active Python environment.
• Sub-skill helpers are dry-run planners or inspectors. They do not train, infer, export, download weights, open media, or run native tests unless their help text explicitly says so.