auto-sklearn

Use this repo skill when the task names auto-sklearn, autosklearn, AutoSklearnClassifier, AutoSklearnRegressor, AutoSklearn2Classifier, autoML/HPO search, auto-sklearn metrics, component extension, or auto-sklearn metadata maintenance. Start here, then load the narrowest sub-skill for the requested workflow.

Quick install and import check

auto-sklearn is a Linux-oriented Python package with compiled dependencies. In a clean environment, install the public package or the user's selected source distribution, then verify the same Python that will run the task:

python -m pip install auto-sklearn
python -m pip check
python -I -c "import autosklearn; print(autosklearn.__version__)"
python -I -c "import autosklearn.classification, autosklearn.regression, autosklearn.metrics"

If pip builds dependencies from source, the environment may need a C++11 compiler and SWIG. For import errors, compiled dependency failures, unsupported OS/Python, pkg_resources warnings, or ConfigSpace/NumPy ABI problems, read cross-cutting troubleshooting before routing deeper.

Read repository provenance when checking whether this generated skill is current for a checkout or installed package version. The router metadata consumed by managed imports is in repo-routing-metadata.json.

Workflow lanes

User need	Read
Choose classifier vs regressor vs AutoSklearn2, call fit, predict, predict_proba, refit, fit_ensemble, inspect sprint_statistics, or run a bounded smoke helper	estimators
Prepare pandas/NumPy/sparse/list inputs, diagnose feat_type, target encoding, allow_string_features, dataset_compression, built-in/custom metrics, scoring_functions, holdout/CV/custom splitters, or refit-after-CV rules	data-metrics-validation
Configure search budgets, include/exclude, n_jobs, Dask clients, thread limits, SMAC/random/successive-halving callbacks, ensembles, leaderboard, show_models, cv_results_, or performance_over_time_	search-and-parallelism
Implement or repair custom classifiers, regressors, feature/data preprocessors, ConfigSpace hyperparameters, component property dictionaries, registry calls, or custom component IDs	custom-components
Maintain meta-learning metadata, metadata_directory, AutoSklearn2 selector/portfolio context, ASLib files, metadata-generation scripts, automl_common submodule checks, or focused repository tests	metadata-maintenance

First-response patterns

• Ordinary model-fitting request: load estimators; if inputs or metrics are underspecified, also load data-metrics-validation before writing code.
• Validation or scorer error: load data-metrics-validation first. Do not start an AutoML fit just to validate containers or scorer flags.
• Slow, memory-heavy, parallel, or dummy-only run: load search-and-parallelism; use estimators for high-level dummy-only triage if the user only needs fit/predict recovery.
• Search-space filtering: load search-and-parallelism for built-in IDs and budget trade-offs; load custom-components if the user is adding a new component class.
• AutoSklearn2 issue: load estimators for use-level ASKL2 behavior; load metadata-maintenance for selector/portfolio/cache/metadata internals.
• Repository contributor task: load metadata-maintenance for metadata scripts, submodule state, and focused test guidance; load custom-components only for component API changes.

Common signals and what they usually mean

Signal	Likely lane
feat_type, pandas categories, datetime columns, target NaNs, custom scorers, dataset_compression, or custom splitters	data-metrics-validation
n_jobs, Dask, include/exclude, ensemble_class, max_models_on_disc, leaderboard, show_models, performance_over_time_, or main-guard/resource problems	search-and-parallelism
AutoSklearnClassifier, AutoSklearnRegressor, AutoSklearn2Classifier, predict_proba, refit, fit_ensemble, sprint_statistics, or dummy-only output	estimators
add_classifier, add_regressor, add_preprocessor, get_properties, ConfigSpace, or custom class IDs	custom-components
metadata_directory, selector cache, ASLib outputs, scripts/01_create_commands.py, automl_common, or repo test selection	metadata-maintenance

Minimal operating habits

1. Keep the task-specific lane narrow. If a task crosses lanes, resolve data/metric questions before search/fit questions and search questions before component-extension questions.
2. Verify the public package version and import surface before using version-sensitive arguments. This skill was built from auto-sklearn 0.16.0.dev0 / 0.16.0dev evidence.
3. Use the bundled helper scripts under the relevant sub-skill for safe checks or command planning. Do not run the original repository's large examples or metadata scripts as the default runtime path.
4. Treat short smoke runs as plumbing checks only. They prove import, fit, inspect, and predict behavior, not model quality.
5. For source-maintenance work, check the autosklearn/automl_common submodule and dirty-tree state before changing metadata scripts or focused tests.

Guardrails for future agents

• Keep autosklearn package guidance version-aware. Recheck live signatures before using parameters not shown in this skill, especially output_directory.
• Do not run original repo examples or metadata scripts as normal skill usage. Use bundled helper scripts under the relevant sub-skill, or ask before expensive/network/long-running native workflows.
• Do not treat a short AutoML smoke as model-quality evidence. It proves import, fit, inspect, and predict plumbing only.
• Keep data validation, metric design, and resampling decisions explicit before expensive fitting.
• For parallel runs, plan for n_jobs * memory_limit and require a Python main guard in scripts.
• For source-maintenance tasks, check submodule state and dirty-tree effects before and after focused tests.