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name: data-and-training description: "Prepare pix2code paired datasets, convert screenshots to arrays, and plan or troubleshoot the legacy training workflow." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Data and Training

Use this sub-skill for the preprocessing side of pix2code: verifying paired .gui and image files, splitting the archive into training and evaluation sets, converting screenshots to compressed arrays, and understanding the legacy training command lines.

Read first

• references/data-formats.md for directory layouts, file naming, and the .npz feature contract.
• references/training-workflows.md for command patterns, memory mode, and what the legacy training script expects.
• references/troubleshooting.md for pairing errors, archive issues, OpenCV problems, and training-environment warnings.
• scripts/prepare_pix2code_dataset.py for portable validation, splitting, and conversion.

Quick workflow

1. Validate the input directory first:

python sub-skills/data-and-training/scripts/prepare_pix2code_dataset.py validate --input datasets/web/all_data

2. Split a directory of paired .gui/.png files into train/eval sets:

python sub-skills/data-and-training/scripts/prepare_pix2code_dataset.py split --input datasets/web/all_data --distribution 6

3. Convert training images to .npz features when you want smaller files or faster uploads:

python sub-skills/data-and-training/scripts/prepare_pix2code_dataset.py convert --input datasets/web/training_set --output datasets/web/training_features

4. If the user wants to train, use the legacy command patterns from training-workflows.md. Treat training as expensive and dataset-specific.

Boundaries

This sub-skill owns dataset preparation and training planning. It does not own screenshot-to-DSL generation; route that to ../sampling-and-generation/SKILL.md. It does not own .gui compilation; route that to ../dsl-compilation/SKILL.md.

Validation checklist

• Input directories contain paired .gui and .png files with matching basenames.
• Split counts are integral for the chosen distribution.
• Duplicate .gui content is kept out of the evaluation set when possible.
• Converted .npz files contain a features array alongside copied .gui files.
• Training advice names the legacy dependency stack and warns when OpenCV or TensorFlow pins are unavailable.