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From ml-engineering
Prevent training-serving skew: compute features once via a feature store or shared transformation so training and serving use identical logic, with point-in-time correctness for temporal features and no leakage of future data.
How this skill is triggered — by the user, by Claude, or both
Slash command
/ml-engineering:feature-store-consistencyThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
'Great offline, bad online' is almost always **training-serving skew** — features computed differently at serving time.
'Great offline, bad online' is almost always training-serving skew — features computed differently at serving time.
Compute features once (feature store / shared transform); training and serving read the same logic.
For temporal features, join as-of the event time — no future leakage into training. Otherwise the offline metric is a lie.
npx claudepluginhub mcorbett51090/ravenclaude --plugin ml-engineeringGuides completion of development work by verifying tests, detecting environment, and presenting structured options for merge, PR, or cleanup.
Enforces test-driven development: write failing test first, then minimal code to pass. Use when implementing features or bugfixes.
Guides creation and editing of skills using test-driven development with pressure scenarios and subagents to verify agent compliance.