Mlops Monitoring Drift
CheckedDetects and alerts on data drift in ML models, ensuring ongoing performance and reliability within DevOps pipelines.
Install on your platform
Showing Claude Code. One command installs this skill into any of the agents below.
- 1
Run this in your project
terminal$ npx skills add kentoshimizu/sw-agent-skills --skill mlops-monitoring-drift -a claude-code Uses the open-source
skillsCLI and needs Node.js. Add-gto install it for your user instead of this project.
What we know about this skill
Publisher
kentoshimizu
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Install target
Live repository
Not pinned. Installing fetches whatever the repository holds at the time you run the command.
Automated safety checks
This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.
Declares what it does: passed
SKILL.md parses and declares a name and a description.
SKILL.md exists, its frontmatter parses, and it declares a name and a description.
No undeclared network calls: passed
Makes no network calls.
Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.
No obfuscated payloads: passed
No encoded or obfuscated payloads.
No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.
No credential access: passed
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Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.
No pipe-to-shell installers: passed
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No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.
Pinned to a commit: passed
Scanned kentoshimizu/sw-agent-skills at 5b7bafb626.
The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.
- Commit
- 5b7bafb626
- Scanned
- 3 files, 73 lines
About this skill
What it does
This skill monitors machine learning models for data drift, a change in the input data that can degrade model performance. It automatically detects and reports drift using statistical measures, allowing proactive intervention before significant accuracy loss occurs. The skill provides alerts when drift exceeds defined thresholds, enabling timely retraining or adjustments to the model pipeline.
When to use it
- Post-deployment monitoring: After deploying a machine learning model to production, continuously monitor for data drift and performance degradation.
- A/B testing analysis: Compare data distributions between different versions of a model during A/B tests to identify potential biases or unexpected behavior.
- Automated retraining triggers: Use drift detection as a trigger to automatically initiate model retraining pipelines when significant changes are detected in the input data.
- Data quality assessment: Integrate with existing data quality checks to ensure that incoming data remains consistent and reliable for model predictions.
Key capabilities
- Drift detection using statistical measures (unspecified)
- Threshold-based alerting
- Automated reporting of drift metrics
- Integration with ML pipelines (implied)
Example prompts
- "Monitor the 'customer_churn' model for data drift and alert me if it exceeds a threshold of 0.2."
- "Show me the latest drift scores for all deployed models."
- "Compare the feature distributions between training data and current production data for the 'fraud_detection' model."
Tips & gotchas
This skill requires access to both historical training data and real-time production data for accurate drift comparison. Proper configuration of drift thresholds is crucial to avoid false positives or missed critical changes in data distribution.