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From ml-engineering
Keep production models honest: monitor input/data drift, prediction/concept drift, and performance decay (when labels arrive); define the retraining trigger up front (schedule/threshold/drop); alert on model health; and close the loop to retraining.
How this skill is triggered — by the user, by Claude, or both
Slash command
/ml-engineering:model-monitoringThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
- **Input/data drift** — the world changed (early warning, no labels needed).
Schedule, drift threshold, or performance drop. Not after accuracy quietly fell for a quarter.
Thresholds + alerts (with observability-sre); a drift alert must trigger a documented response + the retraining loop. Significance of a drop -> applied-statistics.
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.