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Mlops Workflows

Checked
manutej·vlatest·devops

Automates & orchestrates ML model deployment pipelines (MLOps workflows) for streamlined development and faster time-to-market.

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 manutej/luxor-claude-marketplace --skill mlops-workflows -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

manutej

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: mlflow-model-default.default.svc.cluster.local.

    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-model-default.default.svc.cluster.locallocal

  • 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 manutej/luxor-claude-marketplace at 1861cc9720.

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

Commit
1861cc9720
Manifest
plugins/luxor-data-engineering/skills/mlops-workflows/SKILL.md
Scanned
3 files, 4,141 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 automate and orchestrate machine learning (ML) workflows using MLflow. It covers the entire ML lifecycle, including experiment tracking, model packaging, registry management, deployment, monitoring, A/B testing, feature stores, CI/CD for ML models, and versioning. Ultimately, this helps streamline development processes and accelerate time-to-market for ML solutions.

When to use it

  • When you need a structured way to track different experiments during model development.
  • To package your ML code in a reproducible format for sharing and deployment.
  • For managing the lifecycle of deployed models, including versioning and A/B testing.
  • To streamline CI/CD pipelines for machine learning models.
  • When you need to centralize model storage and management across different platforms.

Key capabilities

  • Experiment Tracking: Log parameters, metrics, and artifacts during ML experiments.
  • MLflow Projects: Package ML code with reproducible environments.
  • Model Packaging: Standardize models for deployment on various platforms.
  • Model Registry: Centralized storage and versioning of ML models.
  • Deployment Patterns: Facilitates the deployment of ML models across different environments.

Example prompts

  • "Log this experiment's accuracy metric as 0.95."
  • "Package my training script into an MLflow project with a conda environment defined in 'conda.yaml'."
  • "Register the model from run ID XXXXXX as 'CustomerChurnModel'."

Tips & gotchas

  • Requires familiarity with MLflow concepts and APIs.
  • The skill focuses on utilizing MLflow; you'll need to have an MLflow tracking server set up.
  • To get the most out of this skill, ensure your ML code is structured in a way that aligns with MLflow’s components (Tracking, Projects, Models, Registry).