Borrowing it
Nothing to install: this file belongs to DuqueOM/ML-MLOps-Portfolio. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/DuqueOM/ML-MLOps-Portfolio/main/.devin/skills/drift-detection/SKILL.mdgit clone --depth 1 https://github.com/DuqueOM/ML-MLOps-PortfolioWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/duqueom/ml-mlops-portfolio/drift-detection)<a href="https://agentmods.dev/skills/duqueom/ml-mlops-portfolio/drift-detection"><img src="https://agentmods.dev/badge/skills/duqueom/ml-mlops-portfolio/drift-detection/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/duqueom/ml-mlops-portfolio/drift-detection"><img src="https://agentmods.dev/badge/skills/duqueom/ml-mlops-portfolio/drift-detection.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00027 | $0.02240 |
| Opus 5.5 | $0.00011 | $0.00896 |
| Sonnet 5.5 | $0.00005 | $0.00448 |
| Haiku 4.5 | $0.00003 | $0.00224 |
Grade A, and why
drift-detection scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- Bash(curl:*) How it starts
The opening of the file, as written. The whole thing — 232 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Drift Detection
Two complementary layers (ADR-006):
- Data drift (PSI on feature distributions) — early signal, no labels needed
- Concept drift (sliced AUC/F1 vs baseline, using delayed labels) — ground truth
Always investigate data drift FIRST (cheaper, faster). Escalate to concept drift analysis when (a) PSI alert fires and you need to confirm impact, or (b) a performance alert fires directly (AUC below threshold).
Step 1: Understand the Drift Metric
PSI Interpretation Guide
| PSI Value | Status | Action | Exit Code |
|---|---|---|---|
| < 0.10 | Stable | No action | 0 |
| 0.10 – 0.20 | Warning | Monitor, increase check frequency | 1 |
| > 0.20 | Alert | Trigger retraining | 2 |
ALWAYS use quantile-based bins (not uniform):
breakpoints = np.percentile(reference, np.linspace(0, 100, bins + 1))
Uniform bins can produce empty bins at extremes → PSI dominated by epsilon noise.
Special Cases — When PSI Doesn't Apply
| Feature Type | Problem with PSI | Alternative |
|---|---|---|
| Time series (seasonal) | PSI flags every seasonal change as "drift" | Year-over-Year comparison (same period last year) |
| Text/NLP features | PSI not meaningful for text | OOV (Out-of-Vocabulary) rate: warning > 20%, alert > 35% |
| Low-cardinality categorical | Quantile bins don't work with 3-5 categories | Categorical PSI variant: bins = unique categories |
| Boolean features | Only 2 bins → unstable PSI | Simple proportion test (chi-squared) |
Exit Codes for CronJob Integration
exit 0→ all features stableexit 1→ warning-level drift (monitor)exit 2→ alert-level drift (retraining needed, GitHub Issue created)
Step 2: Run Drift Detection Manually
python src/{service}/monitoring/drift_detection.py \
--reference data/reference/{service}_reference.csv \
--current data/production/{service}_latest.csv \
--output drift_report.json
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2mo ago First seen · 232 lines · 27 tokens per session scan A fa5971133ba1
drift-detection is a skill published in the GitHub repository DuqueOM/ML-MLOps-Portfolio (7 stars, last pushed yesterday), licensed MIT. It adds 27 tokens to every session and 2,240 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
drift-detection compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| drift-detectionDuqueOM/ML-MLOps-Portfolio | Skill | 7 | yesterday | MIT | A | 27 | Claude Code |
| mlopswilliamzujkowski/standards | Skill | 19 | 2mo ago | MIT | A | 45 | Claude Code, Codex |
| setupmlflow/mlflow | Skill | 29k | yesterday | Apache-2.0 | A | 10 | Claude Code |
| ml-expertpersonamanagmentlayer/pcl | Skill | 44 | 11d ago | Apache-2.0 | A | 58 | Claude Code |
| ecs-genaiaws-samples/sample-apex-skills | Skill | 92 | today | MIT-0 | A | 259 | Claude Code, Codex |
| eks-genaiaws-samples/sample-apex-skills | Skill | 92 | today | MIT-0 | A | 275 | Claude Code, Codex |
| mlflowOpenLAIR/dr-claw | Skill | 1.2k | yesterday | none | B | 33 | Claude Code, Codex |