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Model Deployment

Listed
pluginagentmarketplace·vlatest·devops

Helps with data modeling, deployment 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.

  1. 1

    Run this in your project

    terminal
    $ npx skills add pluginagentmarketplace/custom-plugin-ai-engineer --skill model-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 failed 2 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 1 host that is neither well-known infrastructure nor declared in the skill's frontmatter: ollama.ai.

    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

    ollama.aiundeclared

    What it found

    • skills/model-deployment/SKILL.md:56contacts ollama.ai
      curl -fsSL https://ollama.ai/install.sh | sh

  • No pipe-to-shell installers: failed

    Runs code fetched at install time in 1 place.

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

    What it found

    • skills/model-deployment/SKILL.md:56remote script piped into a shell
      curl -fsSL https://ollama.ai/install.sh | sh

  • 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.

  • Pinned to a commit: passed

    Scanned pluginagentmarketplace/custom-plugin-ai-engineer at 5db0287caa.

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

Commit
5db0287caa
Manifest
skills/model-deployment/SKILL.md
Scanned
4 files, 1,065 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 automates the deployment of AI models to various platforms. It handles tasks like containerization, infrastructure provisioning, and model serving configuration. The goal is to streamline the process of making trained models accessible for inference and real-world applications.

When to use it

  • Deploying a newly trained machine learning model to a production environment.
  • Scaling existing AI services by automating deployment processes.
  • Creating reproducible deployments across different environments (e.g., development, staging, production).
  • Managing multiple models and their versions in a consistent manner.

Key capabilities

  • Automated containerization of models.
  • Infrastructure provisioning for model serving.
  • Configuration management for deployment targets.
  • Model versioning and rollback support.

Example prompts

  • "Deploy my 'sentiment-analysis' model to AWS SageMaker."
  • "Create a Kubernetes deployment for the 'image-classifier' model, using Docker image version 2.1."
  • “Rollback the ‘fraud-detection’ model to version 1.3.”

Tips & gotchas

This skill requires familiarity with cloud platforms and containerization technologies like Docker and Kubernetes. Ensure your model is packaged correctly for deployment before initiating a deployment process.