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Research Dataset Shift Early Warning
Detect input-distribution drift that invalidates established model evaluation assumptions before a release.
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About this skill
Detect input-distribution drift that invalidates established model evaluation assumptions before a release.
What it checks:
- compare sampling and feature coverage
- separate instrumentation drift from population shift
- localize affected segments and labels
- propose holdout and monitored retraining gates
Deliverables:
- shift diagnostics
- affected-cohort report
- release gate recommendation
Typical case: A forecast's error rises after a new vendor changes how missing delivery timestamps are encoded.
Runtime: Cursor Skill
Find more skills like this on the Claude Skills Marketplace, the Codex Skills Marketplace and the Gemini Skills Marketplace, or browse Claude Skills for Testing & Debugging.
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