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Model Deployment

Checked
secondsky·vlatest·cloud·MIT

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 secondsky/claude-skills --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

secondsky

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

    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 secondsky/claude-skills at 8837836131.

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

Commit
8837836131
Manifest
plugins/model-deployment/skills/model-deployment/SKILL.md
Scanned
5 files, 2,347 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 secondsky-model-deployment skill streamlines the process of deploying machine learning models to production environments. It automates complex infrastructure tasks, allowing developers to push trained models directly to scalable hosting services with minimal manual configuration. This tool bridges the gap between model training and real-world application by handling containerization, orchestration, and service exposure.

When to use it

  • You have a trained model (e.g., from Hugging Face or local scripts) that needs to serve predictions via an API endpoint.
  • Your team requires a standardized workflow to move models from development notebooks to production servers without rewriting code.
  • You need to deploy models to cloud providers like AWS, GCP, or Azure using managed Kubernetes or serverless functions.
  • You want to automate the entire lifecycle, including model versioning and health checks, within your CI/CD pipeline.

Key capabilities

  • Automated containerization of ML models into Docker images ready for deployment.
  • Seamless integration with popular orchestration platforms like Kubernetes and Docker Swarm.
  • Support for multiple cloud providers including AWS SageMaker, Google Cloud AI Platform, and Azure ML.
  • Built-in monitoring hooks to track model latency, throughput, and error rates post-deployment.

Example prompts

  • "Deploy my sentiment analysis model trained on Hugging Face to a Kubernetes cluster using this skill."
  • "Set up an automated pipeline to push my image classification model to AWS SageMaker with health checks enabled."
  • "Containerize my NLP inference script and deploy it to Google Cloud Run with auto-scaling configured."

Tips & gotchas

Ensure your model includes proper input/output specifications and environment variables for the deployment target, as missing metadata can cause runtime failures. While the skill handles infrastructure setup, you must still validate model performance in production to avoid drift or latency issues that automated checks might miss.