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Monitor Model Drift

Listed
pjt222·vlatest·ai-ml·MIT

Helps with monitoring, data modeling as part of agent workflows workflows.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add pjt222/agent-almanac --skill monitor-model-drift -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

pjt222

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill failed 1 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

  • Declares what it does: failed

    No SKILL.md was found in the skill's directory.

    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

    Reads no keys, tokens, keychains or credential files.

    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

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned pjt222/development-guides at bcca822c1c.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
bcca822c1c
Manifest
skills/monitor-model-drift/SKILL.md
Scanned
0 files
Copies in repo
3 — all of them scanned
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill, monitor-model-drift, allows an AI agent to track changes in a deployed machine learning model's performance over time. It identifies when the model’s predictions diverge from its original training data distribution, indicating potential issues like data drift or concept drift. This enables proactive intervention and prevents degraded accuracy in real-world applications.

When to use it

  • Automated Performance Alerts: Set up automated alerts when drift exceeds a defined threshold, triggering investigations into underlying causes.
  • A/B Testing Analysis: Monitor the performance of different model versions during A/B testing to determine which performs best in production.
  • Data Quality Monitoring: Correlate detected drift with data quality checks to identify potential issues in incoming data streams.
  • Retraining Triggering: Automatically trigger retraining pipelines when significant drift is detected, ensuring the model remains accurate and relevant.

Key capabilities

  • Drift detection
  • Performance monitoring
  • Automated alerting (implied)
  • Model version comparison (implied)

Example prompts

  • "Monitor the performance of my pricing prediction model."
  • "Alert me if drift exceeds 10% on the customer churn model."
  • "Compare the current version of the fraud detection model with the previous release."

Tips & gotchas

The effectiveness of this skill depends heavily on having access to both production data and a baseline dataset representing the original training distribution. Ensure proper logging and data collection infrastructure is in place before deploying this skill.