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name: data-and-configuration description: "Author and validate Ultralytics dataset YAMLs, translate Python kwargs to safe YOLO CLI arg=value commands, inspect default config keys, and plan data/config commands before training, prediction, validation, or export." disable-model-invocation: true metadata: disco-role: operating license: AGPL 3.0

Data and Configuration

Use this sub-skill when the task is about Ultralytics dataset YAMLs, data layout checks, CLI/Python argument translation, config defaults, converters, or safe command planning. It is the right entry point before launching expensive train, val, predict, track, benchmark, or export runs.

Route Here For

• Writing or reviewing data.yaml files for detection, segmentation, pose, OBB, semantic segmentation, and classification datasets.
• Converting Python API kwargs like model.train(data="custom.yaml", epochs=10) into canonical yolo TASK MODE arg=value commands.
• Checking whether config keys are known, deprecated, removed, or likely task/mode-specific.
• Planning a command that should be reviewed before it downloads data, opens media streams, trains, exports, or writes output files.
• Choosing safe converter/split APIs such as COCO-to-YOLO, DOTA-to-YOLO-OBB, mask-to-YOLO-seg, or dataset autosplit.

Fast Path

1. Read references/workflows.md for dataset YAML schemas, CLI syntax, converters, and command-planning patterns.
2. Use scripts/validate_dataset_yaml.py --help and run it on local YAMLs before training or validation.
3. Use scripts/plan_yolo_command.py --help to validate kwargs and render a safe CLI command without invoking yolo.
4. If the next step is actual training or validation, hand off to ../training-and-validation/SKILL.md after data/config checks pass.
5. If the next step is prediction/results interpretation, hand off to ../inference-and-results/SKILL.md; for export, hand off to ../export-and-deployment/SKILL.md; for model/task selection, hand off to ../model-families-and-tasks/SKILL.md.

Safe Defaults

• Prefer tiny smoke-test datasets and low-cost arguments while planning: epochs=1, batch=1, imgsz=320, device=cpu, workers=0, save=False where the mode supports it.
• Treat download: entries in dataset YAML as risky until reviewed; they may fetch large archives, run shell commands, or execute Python snippets when autodownload is enabled.
• Do not translate Python kwargs into --flag value syntax. Ultralytics CLI expects space-separated arg=value tokens after optional TASK and required MODE.
• Use relative placeholders such as data=custom.yaml in public examples; avoid machine-specific dataset roots.

Bundled References

• references/workflows.md: dataset YAML templates, config-key groups, Python-to-CLI translation rules, converters, and planning examples.
• references/troubleshooting.md: common data/config failures and safe diagnosis steps.
• scripts/validate_dataset_yaml.py: offline dataset YAML linter with actionable diagnostics.
• scripts/plan_yolo_command.py: offline command planner that validates task, mode, CLI syntax, unknown/deprecated keys, and cost-risk flags.