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Deploy Ml Model Serving

Listed
pjt222·vlatest·devops·MIT

Helps with deployment, machine learning, data modeling as part of agent 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 pjt222/agent-almanac --skill deploy-ml-model-serving -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

pjt222

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.

  • Declares what it does: failed

    No SKILL.md was found in the skill's directory.

    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

    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 pjt222/development-guides at bcca822c1c.

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

Commit
bcca822c1c
Manifest
skills/deploy-ml-model-serving/SKILL.md
Scanned
0 files
Copies in repo
3 — all of them scanned
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 allows AI agents to deploy machine learning models for serving. It handles the complexities of setting up infrastructure and making predictions available via an API endpoint. The agent can manage model versions, scale deployments based on traffic, and monitor performance metrics.

When to use it

  • You've trained a machine learning model and need to make its predictions accessible through an API.
  • You want to automate the deployment process for your models, reducing manual effort and potential errors.
  • Your application requires real-time or near real-time predictions from a machine learning model.
  • You need to manage multiple versions of a model and easily switch between them.

Key capabilities

  • Model Deployment: Deploys trained ML models for serving.
  • API Endpoint Creation: Automatically creates API endpoints for accessing model predictions.
  • Version Management: Handles different versions of the same model.
  • Scaling: Adapts deployment resources based on traffic load.
  • Monitoring: Tracks performance metrics to ensure optimal operation.

Example prompts

  • "Deploy my 'sentiment-analysis' model version 1.2."
  • "Create an API endpoint for the 'fraud-detection' model."
  • "Scale up the deployment of the 'image-classification' model due to increased traffic."

Tips & gotchas

The agent requires access to your machine learning environment and appropriate permissions to deploy models. Ensure that necessary dependencies and configurations are provided during the initial setup for successful deployments.