Featuretools Repo Skill

Use this skill when a coding agent needs to work with the featuretools package as a library for automated feature engineering. The core flows are EntitySet modeling, Deep Feature Synthesis, feature-matrix generation, feature inspection, primitive authoring, and feature selection.

This generated skill is self-contained. It does not depend on the original checkout at runtime.

Install And Sanity Check

Install the base package:

python -m pip install featuretools

Add optional extras only when the task needs them:

• python -m pip install "featuretools[dask]" for parallel feature-matrix calculation.
• python -m pip install "featuretools[premium]" for premium primitives.
• python -m pip install "featuretools[nlp]" for NLP primitives.
• python -m pip install "featuretools[sql]" for the external SQL add-on.
• python -m pip install "featuretools[complete]" for the bundled convenience meta-extra.

For graph rendering, install the Graphviz Python package and the system Graphviz binary separately.

Quick import check:

python - <<'PY'
import featuretools as ft
print(ft.__version__)
PY

For a broader smoke test, run scripts/featuretools_smoke.py.

Route By Task

• Use sub-skills/entitysets-and-data/ for EntitySet, Relationship, demo datasets, relationships, time indexes, serialization, and EntitySet.plot.
• Use sub-skills/deep-feature-synthesis/ for dfs, DeepFeatureSynthesis, calculate_feature_matrix, cutoff times, training windows, encode_features, and optional Dask-backed matrix generation.
• Use sub-skills/feature-inspection-and-selection/ for show_info, primitive catalog discovery, primitive recommendations, and feature-matrix pruning helpers.
• Use sub-skills/primitives-and-feature-definitions/ for custom primitives, feature objects, describe_feature, graph_feature, and save_features / load_features.

Shared References

• references/installation-and-compatibility.md: supported Python versions, base dependencies, extras, and optional system requirements.
• references/workflows.md: high-level route map and end-to-end workflow order.
• references/api-reference.md: grouped public API signatures and parameter notes.
• references/troubleshooting.md: cross-cutting install/import, Graphviz, Dask, parquet, network, and add-on issues.
• references/repo-provenance.md: source snapshot and staleness baseline.
• references/repo-routing-metadata.json: router placement metadata for the managed repo skill registry.

Shared Script

• scripts/featuretools_smoke.py: run this small cross-cutting smoke when you need a quick install/import/DFS/selection/serialization sanity check.

Common Decisions

• Use load_mock_customer(return_entityset=True) as the default demo dataset; treat the other demo loaders as optional or network-backed.
• Treat graph_feature and EntitySet.plot as optional visualization workflows when Graphviz is not installed.
• Treat n_jobs > 1 and dask_kwargs as optional Dask-backed flows; base CPU DFS remains the default path.
• Treat to_parquet as optional and pyarrow-gated; use to_pickle or to_csv when parquet support is unavailable.
• Treat the SQL, premium, NLP, autonormalize, and sklearn extras as separate add-on surfaces unless the user explicitly asks for them.
• There is no separate CLI entry point to memorize; the package is used through Python APIs and bundled smoke scripts.

Safety And Self-Containment

Do not reference the original repository checkout from runtime guidance. Keep instructions, examples, and helper scripts inside this skill tree so future agents can use it after the source checkout is gone.