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name: data-and-configs description: "Prepare and validate MMAction2 data annotations, pipelines, config inheritance, overrides, and safe data/config utilities." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

MMAction2 data and configs router

Use this sub-skill when the user needs to prepare, inspect, or debug MMAction2 dataset annotations, dataloader sections, transform pipelines, config inheritance, or --cfg-options overrides before training, testing, or inference.

Owned operating references

• Data and config reference: dataset schemas, data_prefix keys, pipeline patterns, config families, _base_ inheritance, naming, and override examples.
• Tooling reference: safe config inspection, safe data-utility decisions, and bounded validation templates.
• Troubleshooting: annotation, prefix, decode, AVA, localization, audio, video-text, pipeline, and config override errors.
• Config inspector script: parses a user-supplied config and prints a safe summary without building datasets, scanning media, training, testing, or writing outputs.

Fast routing

Handle here:

• Choosing between VideoDataset, RawframeDataset, PoseDataset, AudioDataset, AVADataset, ActivityNetDataset, and VideoTextDataset.
• Writing or validating rawframe/video/skeleton/audio/AVA/localization/video-text annotation files.
• Editing train_dataloader, val_dataloader, test_dataloader, train_pipeline, val_pipeline, and test_pipeline sections.
• Explaining config families, file-name parts, _base_ inheritance, and --cfg-options quoting.
• Safely inspecting a config with python scripts/mmaction2_config_inspector.py --config CONFIG.py --show-dataloaders --show-pipelines.

Route elsewhere:

• Actual training, testing, metrics execution, distributed launch, resume, AMP, work directories, checkpoints: ../training-and-evaluation/SKILL.md.
• Inference APIs, inferencer/demo runtime, checkpoint/config pairing for prediction: ../inference-and-demos/SKILL.md.
• Custom model classes, registry/export/deployment, or new registered transforms/datasets beyond config usage: ../models-and-extension/SKILL.md.

Required safety checks

1. Treat all config files as Python programs. Inspect only trusted user-provided config files, and never run training or dataset builders from this sub-skill.
2. Prefer the bundled config inspector for summaries; it resolves config text and overrides but does not instantiate datasets/models.
3. Before any dataset-scale utility, require explicit user confirmation for input root, output root, write behavior, worker count, expected size, and whether network access is allowed.
4. Never enable destructive delete options while diagnosing data quality unless the user explicitly asks after seeing a dry-run report.
5. Keep evidence self-contained: answer from these references and user-provided files instead of sending future agents to source docs, examples, tests, or config files.