name: configuration-and-data
description: "Helps agents create, validate, render, and troubleshoot Ludwig
configs, schemas, feature declarations, datasets, and preprocessing inputs."
disable-model-invocation: true
metadata:
disco-role: operating
license: Apache 2.0
Configuration and Data
Use this sub-skill when the task is about Ludwig YAML/config dictionaries, feature declarations, schema export, preprocessing, synthetic data, data formats, config generation, or dataset-column troubleshooting.
Start here
- Identify the target workflow: config authoring, config validation/rendering, data preparation, synthetic fixture creation, or LLM-backed config generation.
- Read configuration.md for config sections and feature/model-type choices.
- Read data-formats.md for dataset layouts and split-column guidance.
- For a local fixture, run:
python scripts/make_tiny_dataset.py --output-dir /tmp/ludwig-tiny
python scripts/validate_ludwig_config.py /tmp/ludwig-tiny/config.yaml
- If validation fails, use troubleshooting.md before moving to training.
Commands and APIs
- CLI:
ludwig generate_config, ludwig init_config, ludwig render_config, ludwig export_schema, ludwig preprocess, ludwig synthesize_dataset.
- Python:
ludwig.config_generation.generate_config, ludwig.config_generation.get_ludwig_schema_context, ludwig.automl.init_config, LudwigModel.preprocess.
- Config sections:
model_type, input_features, output_features, combiner, trainer, backend, preprocessing, hyperopt, adapter, quantization, prompt.
Route onward
Safety notes
generate_config uses external LLM providers unless mocked or run only with --help; verify API keys before real calls.
- Dataset-zoo and Kaggle/Hugging Face flows can use network and cache. Prefer local CSV/Parquet fixtures for smoke checks.
preprocess writes processed data/metadata; choose an explicit scratch output path when applicable.