Route data analysis and machine learning work to the right skill in this suite. Use when starting any analysis, modeling, validation, or reproducibility task and the matching specialized skill is not yet clear; not needed when one specific skill already clearly applies.
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Pick the narrowest skill that covers the current task. When the work spans multiple lifecycle stages, start from the orchestrator and let it call the others.
| Situation | Skill |
|---|---|
| An end-to-end project or an ambiguous modeling request | running-decision-grade-data-science |
| Data meaning, joins, labels, or ground truth may be untrustworthy | auditing-data-and-ground-truth |
| Designing splits, feature eligibility, baselines, or comparisons | designing-leakage-safe-experiments |
| A metric dropped, results disagree, or training-serving mismatch | diagnosing-ml-failures |
| Reviewing whether results support a claim or launch decision | validating-models-and-claims |
| Packaging finished work for independent reproduction | shipping-reproducible-results |
Each skill states its own non-goals in its description; respect them. The
orchestrator running-decision-grade-data-science already routes to the
other five at the right lifecycle stage, so do not stack it with them
manually for the same step.
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Audit datasets, joins, labels, and ground truth before analysis or modeling. Use when data meaning, row grain, time semantics, source-of-truth reliability, or label construction may invalidate conclusions; not for general model evaluation after the evidence base is already trusted.
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Design leakage-safe machine learning experiments that mirror real deployment and support fair model comparisons. Use when defining prediction timing, feature eligibility, train-validation-test splits, baselines, metrics, or controlled model iterations; not for auditing whether raw labels are trustworthy.
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Isolate the root cause of ML performance drops, inconsistent evaluations, prediction errors, and training-serving mismatches across data, labels, splits, pipelines, models, metrics, and runtime behavior. Use when investigating a reproducible failure or regression, not routine model selection or general performance validation.
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Orchestrate an end-to-end data analysis or machine learning project from decision framing through reproducible handoff. Use when a request spans multiple lifecycle stages or an ambiguous modeling request must become a decision-ready result; use narrower audit or experiment-design skills for isolated reviews.
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Package completed data analysis and ML work so an independent recipient can reproduce the claimed results, verify artifact lineage, and operate the handoff within its stated scope. Use when finalizing a project, study, model package, or review bundle; not for deploying to a live system.
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Validate trained models and analytical claims against their intended decision, independent evidence, and human-reviewed ground truth. Use when reviewing model performance, analysis conclusions, launch claims, or evaluation reports; use failure diagnosis instead when the main task is locating a known defect.
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