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name: generated-artifacts description: "Helps agents work inside a completed automl-gs artifact folder to train, predict, inspect encoders, and troubleshoot generated runtime files." disable-model-invocation: true metadata: disco-role: operating license: MIT

Generated Artifacts

Use this sub-skill when you already have a timestamped folder created by automl_grid_search and need to work with the generated runtime files.

This route covers the generated model.py, pipeline.py, requirements.txt, encoders/, metadata/, and framework-specific model files inside the artifact folder.

Use this sub-skill for

• Running train or predict in a completed generated folder.
• Inspecting the frozen CSV schema, encoder files, or metadata outputs.
• Checking whether a generated folder is ready for XGBoost or TensorFlow runtime use.
• Diagnosing missing encoder, metadata, model, or prediction files.
• Comparing the generated XGBoost artifact path with the legacy TensorFlow artifact path.

Do not use this sub-skill for

• Choosing the search space, framework, or hyperparameters.
• Starting a new search run from CSV + target.
• Explaining the package-level automl_gs CLI or grid-search loop.

If you need to generate a new timestamped folder, use the sibling grid-search sub-skill instead: grid-search.

Read first

• references/generated-artifacts.md for the folder layout, generated modes, CSV loading contract, encoders, metadata, prediction formats, and framework differences.
• references/troubleshooting.md for the common runtime failures and recovery steps.
• scripts/check_generated_folder.py to validate a generated folder and print the expected modes and files without importing the original repository.

What to expect in a valid folder

A usable generated folder usually contains:

• model.py with -d/--data, -m/--mode, -s/--split, -e/--epochs, -c/--context, and -t/--type flags.
• pipeline.py with helper functions for encoder loading, data processing, prediction, and training.
• requirements.txt for the generated runtime, not the search loop.
• encoders/ with JSON encoder state.
• metadata/ with the per-epoch metrics log.
• model.bin for XGBoost or model_weights.hdf5 for TensorFlow after train.

Quick workflow

1. cd into the generated folder before running anything.
2. Use scripts/check_generated_folder.py to confirm the folder layout.
3. Run python model.py -d <csv> -m train to refresh encoders and model files.
4. Run python model.py -d <csv> -m predict -t csv or -t json to write predictions.

Mode summary

• train rebuilds encoders, writes metadata/results.csv, and saves the framework model artifact.
• predict loads the saved encoders and model, then writes a predictions file.
• --context automl-gs is for the search loop; standalone use normally leaves the default context alone.

Common checks

• If files are missing, confirm the working directory is the generated folder, not the parent search directory.
• If the runtime package does not match the artifact framework, read the troubleshooting guide before retrying.
• If the CSV schema no longer matches the generated model.py, do not rename the CSV headers to normalized Python names; use the original raw headers that appear in the generated script.
• If an XGBoost train run fails on label conversion, read the troubleshooting guide: this artifact path expects a numeric-compatible target column in the frozen dtype map.