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name: feature-inspection-and-selection description: "Inspect Featuretools installation and primitive catalogs, recommend primitives, and prune feature matrices." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

Feature Inspection And Selection

Use this sub-skill when the task is about understanding what Featuretools is installed, which primitives are available, which primitives are recommended, or which generated columns should be removed from a feature matrix.

Route Here For

• Printing package and environment information with show_info.
• Listing or summarizing primitive catalogs.
• Recommending primitives for a single-table entityset.
• Removing low-information, highly null, single-value, or highly correlated columns.
• Cleaning a feature matrix after DFS or before model training.

Start With These References

• ../../references/api-reference.md: the discovery and selection signatures.
• references/workflows.md: when to inspect first and when to prune.
• references/troubleshooting.md: threshold, missing-column, and recommendation-limit recovery notes.
• scripts/selection_smoke.py: a tiny self-check for catalog discovery and matrix pruning.

Boundaries

• Stay inside package info, primitive discovery, primitive recommendation, and matrix pruning.
• Route raw EntitySet setup to ../entitysets-and-data/.
• Route DFS, feature-matrix creation, and encoding to ../deep-feature-synthesis/.
• Route custom primitive authoring, feature descriptions, graphs, and persistence to ../primitives-and-feature-definitions/.

Minimal Workflow

1. Start with show_info or list_primitives when the user asks what is installed.
2. Use summarize_primitives when a compact primitive table would be easier to read than the raw list.
3. Use get_recommended_primitives only on a single-table entityset.
4. Prune the matrix with the selection helper that matches the failure mode: null-heavy, low-information, single-value, or highly correlated.

Common Decision Points

• Keep the single-table requirement in mind for get_recommended_primitives.
• Use features=None when you only need a pruned matrix; pass the feature list when you need the definitions aligned with the matrix.
• Use features_to_check and features_to_keep only when you need a more targeted correlated-feature pass.
• Treat replace_inf_values as a cleanup helper for matrices that contain infinities before model training.

Quality Bar

Future agents should be able to explain what is installed, discover useful primitives, and prune a noisy feature matrix without reopening the original repository.