---

name: feature-column-inputs description: "Build and validate DeepCTR-Torch feature columns and model_input dictionaries from tabular CTR, recommender, dense, sparse, and sequence data." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

feature-column-inputs

Use this sub-skill when an agent must turn raw tabular CTR/recommender data into DeepCTR-Torch feature-column lists and the model_input dictionary consumed by model constructors, fit, predict, and evaluate.

What this sub-skill owns

• Choosing and declaring SparseFeat, DenseFeat, and VarLenSparseFeat columns.
• Building linear_feature_columns, dnn_feature_columns, and feature_names = get_feature_names(...).
• Preparing sparse categorical ids, dense numeric arrays, sequence/multi-value arrays, and optional sequence length columns.
• Validating names, batch sizes, sparse id ranges, dense vector widths, sequence max lengths, padding, length_name, shared embedding_name, and group_name usage before model construction.
• Explaining common input/feature-column errors and safe fixes.

Route away

• Model-family choice, compile/fit/predict loops, metrics, callbacks, save/load, and single-output training belong in ../single-task-modeling/SKILL.md.
• DIN/DIEN behavior histories, behavior_feature_list, attention, GRU options, and negative sampling belong in ../sequence-and-interest-models/SKILL.md; use this sub-skill only for the underlying VarLenSparseFeat input contract.
• Shared inputs for multi-output labels can be built here, but MTL constructors, task names/types, target matrix shape, losses, and prediction interpretation belong in ../multitask-modeling/SKILL.md.

Operating sequence

1. Inventory columns by semantic type: categorical sparse ids, dense numeric/scaled vectors, sequence or multi-value categorical histories, and labels.

2. Preprocess categorical data into integer ids; reserve id 0 for padding when a sequence uses mask-based pooling without length_name.

3. Declare feature columns using the exact constructor signatures and shape rules in feature-columns-and-inputs.

4. Build feature_names = get_feature_names(linear_feature_columns + dnn_feature_columns) and then model_input = {name: data[name] for name in feature_names}.

5. Run the bundled validator before constructing the model:

   python scripts/validate_feature_input.py
   python scripts/validate_feature_input.py --spec feature_input_spec.json
   

6. If validation fails, resolve the matching symptom in troubleshooting before routing to a modeling sub-skill.

References and helper

• Feature columns and model inputs
• Data preprocessing recipes
• Troubleshooting feature inputs
• Self-contained feature-input validator