# Drift Detect

> 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.

- Skill: `damionrashford/drift-detect` (Agent Skill, multi-file: 6 files)
- Install (CLI): `npx skillmds@latest add damionrashford/drift-detect`
- Raw SKILL.md: https://api.skillmd.com/api/skills/damionrashford/drift-detect/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: damionrashford (https://skillmd.com/u/damionrashford)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/damionrashford/drift-detect

---


# Drift Detect Skill

Detect data drift, concept drift, and model degradation in production.

## Quick start

```bash
# 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 | DDM, ADWIN |
| 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:
```python
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.