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Agent Evaluation

Checked
mlflow·vlatest·agents·Apache-2.0

Automates MLflow agent performance evaluation using predefined metrics and datasets for consistent benchmarking.

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 mlflow/skills --skill agent-evaluation -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

mlflow

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

    Only contacts known or declared hosts: 127.0.0.1.

    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.

    Hosts contacted

    127.0.0.1local

  • 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 mlflow/skills at 51a42ea2b0.

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

Commit
51a42ea2b0
Manifest
agent-evaluation/SKILL.md
Scanned
21 files, 5,690 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 automatically evaluate model performance and generate detailed reports based on MLflow tracking data. It allows agents to query metrics, compare runs, and assess experiment outcomes directly within the MLflow ecosystem.

When to use it

  • Automated Experiment Review: Trigger an agent to analyze a completed training run and summarize key metrics like accuracy or loss.
  • Model Comparison: Ask an agent to compare multiple model versions stored in MLflow to identify the best performer for deployment.
  • Quality Assurance: Use the skill to validate that new experiments meet specific performance thresholds before human review.
  • Debugging Performance Drops: Have an agent investigate why a recent run underperformed compared to historical baselines.

Key capabilities

  • Query and retrieve specific metrics from MLflow runs.
  • Generate structured evaluation reports summarizing experiment results.
  • Compare performance across multiple tracked runs.
  • Access data stored within the MLflow tracking server.

Example prompts

  • "Evaluate the latest model run in my MLflow project and summarize the accuracy and loss metrics."
  • "Compare the performance of run 'v1-experiment' against 'v2-experiment' and tell me which one performed better."
  • "Generate a report on all experiments from last week that failed to meet the 90% accuracy threshold."

Tips & gotchas

Ensure your MLflow tracking server is accessible and contains the relevant run data before invoking this skill. The agent's ability to generate reports depends on the granularity of metrics logged during the original experiment execution.