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Mlops Deployment

Checked
pluginagentmarketplace·vlatest·devops

Automates model deployment pipelines, ensuring rapid iteration and reliable release of machine learning models.

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 pluginagentmarketplace/custom-plugin-ai-data-scientist --skill mlops-deployment -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

pluginagentmarketplace

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

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

  • Pinned to a commit: passed

    Scanned pluginagentmarketplace/custom-plugin-ai-data-scientist at eedea7234d.

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

Commit
eedea7234d
Manifest
skills/mlops-deployment/SKILL.md
Scanned
4 files, 897 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

The mlops-deployment skill automates the deployment of machine learning models to various environments. It handles tasks such as containerization (e.g., Docker), infrastructure provisioning, and model serving. This allows for repeatable and efficient model releases, reducing manual effort and potential errors in the deployment pipeline.

When to use it

  • Deploying a newly trained model to a staging environment for testing.
  • Automating the rollout of a production-ready model after validation.
  • Creating reproducible deployments across different cloud providers or on-premise infrastructure.
  • Managing and updating existing machine learning models in a live environment.

Key capabilities

  • Containerization (Docker)
  • Infrastructure provisioning
  • Model serving
  • Automated deployment pipelines

Example prompts

  • "Deploy the 'fraud_detection' model to the staging environment using Docker."
  • "Create an automated pipeline for deploying models trained by the 'data_science_team'."
  • "Update the production version of the 'recommendation_engine' with the latest model."

Tips & gotchas

The skill requires familiarity with containerization technologies like Docker and basic understanding of MLOps principles. Ensure your model is packaged correctly for deployment to maximize efficiency.