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Kubernetes Deployment

Checked
aj-geddes·vlatest·cloud·MIT

Helps with Kubernetes, deployment as part of deploying and managing cloud infrastructure 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 aj-geddes/useful-ai-prompts --skill kubernetes-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

aj-geddes

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 aj-geddes/useful-ai-prompts at 3f5182cfd7.

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

Commit
3f5182cfd7
Manifest
skills/kubernetes-deployment/SKILL.md
Scanned
6 files, 428 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 kubernetes-deployment skill enables AI agents to manage containerized applications within Kubernetes clusters. It handles the full lifecycle of deployments, including creating, updating, and deleting workloads based on user instructions or configuration files.

When to use it

  • Automating the rollout of new application versions to production environments.
  • Scaling up or down the number of pod replicas in response to traffic changes.
  • Rolling back a failed deployment to a previous stable version quickly.
  • Applying complex Kubernetes manifests that define multiple resources like services and ingress rules.

Key capabilities

  • Creating new deployments from YAML configuration files.
  • Updating existing deployments with new images or resource specifications.
  • Deleting specific deployments or entire namespaces.
  • Managing rollout strategies such as rolling updates and recreate modes.

Example prompts

  • "Create a Kubernetes deployment for my web app using the provided Docker image my-app:v2 with 3 replicas."
  • "Update the existing frontend-service deployment to use the latest stable tag and increase memory limits to 512Mi."
  • "Rollback the api-gateway deployment to the previous revision after the last update caused errors."

Tips & gotchas

Ensure your AI agent has read/write access to the target Kubernetes cluster via kubeconfig or API tokens before attempting operations. Complex manifests involving multiple interdependent resources may require careful validation to prevent breaking existing services during updates.