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name: artifacts-reports description: "Use mljar-supervised AutoML artifacts, reports, structured reports, explainability outputs, persistence, and retrain checks." disable-model-invocation: true metadata: disco-role: operating license: MIT

artifacts-reports

Use this sub-skill after an mljar-supervised AutoML run exists or when a task is primarily about saved run artifacts, report extraction, explainability files, reload/prediction behavior, or deciding whether a saved model needs retraining.

Route here for

• Locating and interpreting an AutoML results_path directory, params.json, leaderboard.csv, top-level report files, and model subdirectories.
• Loading a trained run with AutoML(results_path=...) or automl.load(path) and predicting after reload.
• Calling get_leaderboard(...), report(), and report_structured(format="markdown"|"dict"|"json", model_name=...).
• Explaining why SHAP, permutation-importance, tree, coefficient, or learning-curve artifacts are present or absent.
• Checking preservation/move requirements for saved runs and using need_retrain(...) on new labeled data.

Start with these references

1. Artifact layout and persistence for results_path, params.json, model folders, loading, leaderboard, and prediction-after-load checks.
2. Reporting and explainability for report(), report_structured(...), report payload fields, model_name, and explainability artifacts.
3. Troubleshooting for stale/conflicting result directories, wrong object loading, missing model files, report format mistakes, optional Graphviz/SHAP gaps, and moved directories.

For package assumptions and cross-cutting install/import issues, use the root references: package overview and package troubleshooting.

Boundaries

• Route new training configuration, mode/algorithm/time-limit selection, validation design, and metric choice to training-core.
• Route generated Mercury app workspaces and app(), local_app(), or publish_app() behavior to app-deployment.
• Route the meaning of fairness metrics, sensitive-feature declarations, privileged/underprivileged groups, and mitigation tradeoffs to fairness-workflows. This sub-skill only explains where fairness fields appear in leaderboards and reports.
• Do not treat backend learner files as the public persistence API. Standard users should load the full AutoML directory.

Bundled helper

Run scripts/structured_report_smoke.py to train a tiny synthetic model in a temporary or chosen output directory, reload it, call report_structured in multiple formats, optionally request a model-specific report, and print stable pass/fail signals.