damionrashford Drift Detect Skill
Detect data drift, concept drift, and model degradation in production.
Quick start
# 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
Drift types
| 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 |
Thresholds
- PSI > 0.2: significant drift — investigate immediately
- PSI 0.1-0.2: moderate drift — monitor closely
- KS p-value < 0.05: statistically significant distribution shift
- Chi-squared p-value < 0.05: categorical feature drift
Integration
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.