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name: data-preparation description: "Route Helios dataset metadata, latent, and prompt-preparation workflows." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

Data preparation

Use this sub-skill when the user needs to prepare the data that Helios training consumes or when they want to validate that a dataset matches the expected layout before a distributed preprocessing job.

Typical triggers

• "check my Helios JSON metadata"
• "prepare the training data"
• "validate the video list"
• "build latents"
• "prepare prompt embeddings"
• "make sure my dataset matches the toy format"

What this sub-skill covers

• Metadata validation for the video/JSON format used by the toy examples.
• The expected naming convention for generated latent and embedding files.
• The relationship between clip metadata, frame counts, resolutions, and prompt strings.
• Preflight checks before the distributed GPU extraction jobs are launched.

What it does not own

• The full Stage 1/2/3 training launch.
• Inference or demo generation.
• Metric evaluation and benchmarking.

Read next

• references/workflows.md for the preparation sequence and file roles.
• references/data-formats.md for the JSON and .pt structures.
• references/troubleshooting.md for metadata and path-layout failures.
• scripts/validate_toy_filter.py for a bundled layout check.

Working rule

Treat the heavy extraction jobs as environment-specific and cluster-specific. This sub-skill focuses on the data contract and the validation layer that makes those jobs safe to launch.