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name: tabular-modeling description: "Use Libra for tabular regression, classification, clustering, recommendation, tuning, and model-inspection workflows on structured datasets." disable-model-invocation: true metadata: disco-role: operating license: MIT

Tabular Modeling with Libra

Load this sub-skill when the task centers on CSV/XLSX/JSON data, target-column guessing, classical machine learning, feed-forward ANNs, clustering, content recommendations, tuning, model inspection, or dashboard launch for structured datasets.

What this sub-skill owns

• client(...).regression_query_ann, classification_query_ann, and neural_network_query
• svm_query, nearest_neighbor_query, decision_tree_query, and xgboost_query
• kmeans_clustering_query, content_recommender_query, and recommend
• tune, predict, info, model, operators, accuracy, losses, target, plots, plot_names, and analyze
• structured-data dashboard launch and EDA caveats

Trigger phrases

Use this route when a user asks to:

• predict a numeric or categorical column from a table
• compare ANN, SVM, decision tree, KNN, XGBoost, or k-means on a spreadsheet-like dataset
• inspect client.models, model(), info(), accuracy(), losses(), target(), or operators() after a run
• tune a trained ANN or CNN model
• build recommendations from a movie/catalog-style table
• open the Streamlit dashboard for tabular data

Bundled references

• references/api-reference.md for the tabular method map and return shapes
• references/workflows.md for end-to-end tabular recipes
• references/data-formats.md for accepted file types and column assumptions
• references/troubleshooting.md for target matching, model-key, and dashboard failures
• references/compatibility.md for the verified modern/legacy stack notes

Bundled scripts

• scripts/inspect_tabular_surface.py prints the tabular client surface with signatures.
• scripts/smoke_tabular_decision_tree.py creates a synthetic CSV and runs a tiny CPU decision-tree smoke test.

Operating notes

1. Keep the instruction phrase close to the target column name. Libra uses text parsing plus column similarity, so vague prompts are more likely to misroute.
2. neural_network_query auto-selects classification or regression from the target cardinality; use the specific ANN method when you already know the task family.
3. tune() defaults to latest_model when model_to_tune is omitted. Train the model you want to tune last or pass the key explicitly.
4. content_recommender_query and recommend() depend on a stable indexer column and matching feature names.
5. analyze() and plots() operate on stored model dictionaries; inspect c.models[key].keys() before assuming the same fields exist for every algorithm.
6. For dashboard and EDA work, keep the path and generated files isolated; the source dashboard helper uses a hardcoded source-layout path that is not portable.

Cross-links

• Use the root skill for installation, compatibility shims, and global troubleshooting.
• Route text-heavy tasks, summarization, NER, and GPT-2 generation to sub-skills/nlp-and-generation.
• Route image-layout and export tasks to sub-skills/vision-and-generative.