Stats
Actions
Tags
Help us improve
Share bugs, ideas, or general feedback.
From mlx
Detect data drift, concept drift, and model performance degradation in production. Uses PSI, KS-test, and chi-squared for statistical drift, plus evidently and nannyml for automated reports. Use when monitoring a deployed model or comparing training vs production data distributions.
npx claudepluginhub damionrashford/mlx --plugin mlxHow this skill is triggered — by the user, by Claude, or both
Slash command
/mlx:drift-detectsonnetThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
Detect data drift, concept drift, and model degradation in production.
Detects data drift and concept drift in production ML models using Evidently AI, PSI, KS tests, and custom metrics. Sets up automated alerts and reports to catch model degradation before it impacts business metrics.
Monitors deployed model performance, detects data drift, and manages model health using the DataRobot Python SDK. Use for tracking prediction accuracy, feature drift, and prediction anomalies.
Evaluate model performance — check for accuracy drops, data drift, and error patterns. Use when asked about "model accuracy dropped", "evaluate the model", "check for drift", or "model performance".
Share bugs, ideas, or general feedback.
Detect data drift, concept drift, and model degradation in production.
# Run full drift analysis
uv run ${CLAUDE_SKILL_DIR}/scripts/detect_drift.py data/train.csv data/production.csv
# Output: stdout report + drift_report.html
| Type | What it measures | Tool |
|---|---|---|
| Data drift | Input feature distribution shift | PSI, KS-test, chi-squared |
| Concept drift | P(y | x) relationship change |
| Target drift | Label distribution shift | evidently TargetDriftPreset |
| Model degradation | Performance drop in production | nannyml CBPE |
Add drift check to inference pipeline:
from scripts.detect_drift import compute_psi
psi = compute_psi(reference_col, current_col)
if psi > 0.2:
alert("Significant drift detected")
See references/drift-guide.md for complete PSI formula, evidently, nannyml, and alert patterns.