executing-plans
executing-plans
Use when executing an implementation plan in the current session as the implementer yourself — your human partner chose inline execution, or no subagent tool is available
autokerasUse AutoKeras for Keras-based AutoML task APIs, custom AutoModel graphs, tuner search, and model export workflows.
Independent GitHub snapshot · Automated classification · Original documentation governs
Use this skill when a user asks how to operate AutoKeras: Keras-based AutoML for image, text, structured-data, custom search-space, tuner, and export workflows. This skill is self-contained; do not rely on the original repository checkout at runtime.
AutoKeras 3.0.0 uses Keras 3. Choose and install a Keras backend before importing Keras or AutoKeras. The repository's public install guidance and CI support a PyTorch backend path for ordinary CPU checks.
python -m pip install autokeras
python -m pip install torch --index-url https://download.pytorch.org/whl/cpu
KERAS_BACKEND=torch python -c "import keras, autokeras as ak; print(ak.__version__, keras.backend.backend())"
Run scripts/check_autokeras_env.py when setup, backend, import, or public API visibility is uncertain.
ImageClassifier, ImageRegressor, TextClassifier, TextRegressor, StructuredDataClassifier, and StructuredDataRegressor workflows, including data shapes and task-specific troubleshooting.AutoModel graphs, nodes, blocks, heads, multimodal inputs, multitask outputs, and graph/data ordering failures.max_trials, objectives, callbacks, search directories, overwrite, export_model(), and reloading saved .keras models.refresh-repo-skill.max_trials=1, epochs=1, small synthetic/local data, and an explicit scratch directory.KERAS_BACKEND before importing Keras or AutoKeras.