drift-detect

name	drift-detect
description	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.
allowed-tools	Bash(uv run * scripts/detect_drift.py *) Bash, Read, Write, Edit, Glob, Grep
argument-hint	reference dataset and current dataset paths (e.g. "data/train.csv data/production.csv")
model	sonnet
effort	medium
compatibility	>=1.0
metadata	{"category":"mlops","tags":["drift-detection","monitoring","psi","ks-test","evidently","nannyml","production"],"phase":"monitor"}

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.