Skip to content
TrustedSkillsbeta
Back to all skills

Model Monitoring

Checked
aj-geddes·vlatest·devops·MIT

Helps with data modeling, monitoring as part of automating DevOps pipelines and CI/CD workflows workflows.

Repository

Install on your platform

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

Installs under a different name. Its SKILL.md names it Model Monitoring, not the name it is listed under, so the command below uses that name.
  1. 1

    Run this in your project

    terminal
    $ npx skills add aj-geddes/useful-ai-prompts --skill "Model Monitoring" -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

aj-geddes

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 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

    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 aj-geddes/useful-ai-prompts at 3f5182cfd7.

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

Commit
3f5182cfd7
Manifest
skills/model-monitoring/SKILL.md
Scanned
3 files, 438 lines
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 enables AI agents to monitor deployed machine learning models in production environments. It focuses on detecting data drift, concept drift, and performance degradation over time. The monitoring system tracks key metrics like accuracy, latency, throughput, and uses tools such as Prometheus, Grafana, MLflow, TensorFlow Data Validation, Evidently, and Great Expectations for data quality assertions and visualization.

When to use it

  • When machine learning models are deployed in production environments serving real users.
  • To detect changes in the distribution of input features (data drift).
  • To track model performance metrics over time.
  • For implementing ML observability and alerting systems.
  • To establish thresholds for when a model requires retraining or intervention.

Key capabilities

  • Performance Metric Tracking: Monitors accuracy, latency, throughput, precision, recall, and F1-score.
  • Drift Detection: Identifies data drift (changes in input feature distributions), concept drift (changes in target variable relationships), output drift, and feature drift.
  • Anomaly Detection: Identifies unusual samples within production data.
  • Integration with Monitoring Tools: Leverages tools like Prometheus, Grafana, MLflow, TensorFlow Data Validation, Evidently, and Great Expectations.

Example prompts

  • "Monitor the deployed model's accuracy and precision."
  • "Alert me if there is significant data drift in feature X."
  • "Show me a graph of the model’s F1-score over the last week."

Tips & gotchas

  • Requires baseline data for initial comparison and drift detection.
  • The system uses Python implementation, so familiarity with Python may be helpful to understand underlying calculations.
  • Consider setting appropriate thresholds for triggering alerts based on observed performance metrics and drift levels.