---

name: "data-preparation" description: "Routes BasicTS dataset layout, raw conversion, and fixture validation workflows for forecasting, classification, imputation, and BLAST data." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

data-preparation

Use this sub-skill when the task is about dataset files, raw-data conversion, or validating that a BasicTS dataset folder matches the expected layout.

Typical triggers

• "Validate a BasicTS dataset folder"
• "How do I create a tiny forecasting fixture?"
• "What files does BasicTS expect for UEA classification?"
• "Why is the BLAST memmap layout failing?"
• "How do the data-preparation scripts work?"

What this sub-skill covers

• forecasting dataset layout used by BasicTSForecastingDataset
• imputation dataset layout used by BasicTSImputationDataset
• UEA classification layout used by UEADataset
• BLAST memmap shard layout used by BLAST
• safe validation of folder/file layouts
• safe creation of tiny forecasting fixtures for smoke use

Read these bundled references first

• references/data-formats.md for the on-disk schemas.
• references/troubleshooting.md for missing-file and shape problems.
• scripts/validate_basicts_dataset.py for read-only validation.
• scripts/make_tiny_forecasting_dataset.py for a tiny fixture generator.

Route here when the user asks for

• raw dataset conversion
• split-file naming conventions
• timestamp array requirements
• classification label file layout
• BLAST memmap shard or shape validation
• a tiny dataset folder for CPU smoke tests

Route elsewhere when the user asks for

• launcher commands, checkpoint paths, or run execution → training-evaluation
• model forward contracts or output shapes → model-development
• callbacks, metrics, scalers, or taskflow hooks → pipeline-extension

Working guidance

1. Identify the task family first: forecasting, classification, imputation, or BLAST.
2. Validate the folder layout before trying to run a trainer against it.
3. Check whether timestamps are required before claiming a fixture is usable.
4. Use the tiny fixture helper when you need a self-contained smoke dataset.

When to read the helper scripts

• Run scripts/validate_basicts_dataset.py to check an existing dataset folder.
• Run scripts/make_tiny_forecasting_dataset.py to create a minimal forecasting fixture with timestamps and meta.json.

Why this sub-skill exists

BasicTS data workflows are mostly about file layout and split conventions. Keeping those rules separate from training and model contracts makes it easier for future agents to validate datasets without opening the original repository scripts.