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Mlflow Python

Listed
terrylica·vlatest·backend·MIT

Automates ML experiment tracking, model management, and deployment using Python's mlflow library.

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 terrylica/cc-skills --skill mlflow-python -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

terrylica

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.

  • No undeclared network calls: failed

    Contacts 2 hosts that are neither well-known infrastructure nor declared in the skill's frontmatter: mlflow.eonlabs.com, mlflow.server.com.

    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

    mlflow.eonlabs.comundeclaredmlflow.server.comundeclared

    What it found (3 of 7)

    • plugins/devops-tools/skills/mlflow-python/SKILL.md:44contacts mlflow.eonlabs.com
      MLFLOW_TRACKING_URI=http://mlflow.eonlabs.com:5000
    • plugins/devops-tools/skills/mlflow-python/SKILL.md:49contacts mlflow.eonlabs.com
      export MLFLOW_TRACKING_URI="http://mlflow.eonlabs.com:5000"
    • plugins/devops-tools/skills/mlflow-python/SKILL.md:140contacts mlflow.eonlabs.com
      MLFLOW_TRACKING_URI=http://mlflow.eonlabs.com:5000

  • 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 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 terrylica/cc-skills at 3a1dc29965.

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

Commit
3a1dc29965
Manifest
plugins/devops-tools/skills/mlflow-python/SKILL.md
Scanned
10 files, 1,405 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

The mlflow-python skill enables AI agents to interact with MLflow, a unified platform for managing machine learning workflows. It allows agents to track experiments, log model parameters and metrics, and register models directly from Python code within your backend infrastructure.

When to use it

  • Automating the logging of training runs and hyperparameters during model development cycles.
  • Registering trained models into MLflow's model registry for version control and deployment readiness.
  • Retrieving historical experiment data to compare performance across different model iterations.
  • Integrating MLflow tracking into existing Python-based backend pipelines without manual intervention.

Key capabilities

  • Track experiments with automatic artifact logging.
  • Log parameters, metrics, and code versions per run.
  • Register models with tags and metadata for lifecycle management.
  • Query and retrieve past runs via the MLflow API.

Example prompts

  • "Log this training run's accuracy and loss metrics to MLflow using Python."
  • "Register the latest model version in the 'production' stage of the MLflow registry."
  • "Fetch all experiments from the last week that used the 'bert-base' tokenizer."

Tips & gotchas

Ensure your backend environment has the mlflow and mlflow-skinny packages installed before invoking this skill. For production use, configure a remote tracking server URL to centralize experiment data across multiple agents or services.