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Kubectl Debugging

Checked
laurigates·vlatest·testing·MIT

Troubleshoot Kubernetes deployments using kubectl commands, analyze logs, and identify errors with automated debugging assistance.

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 laurigates/claude-plugins --skill kubectl-debugging -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

laurigates

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 laurigates/claude-plugins at 45d1d7dcf6.

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

Commit
45d1d7dcf6
Manifest
kubernetes-plugin/skills/kubectl-debugging/SKILL.md
Scanned
2 files, 783 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 enables AI agents to execute kubectl commands directly within Kubernetes clusters for troubleshooting and inspection. It allows agents to retrieve detailed pod status, inspect container logs, and view resource definitions without manual CLI intervention.

When to use it

  • Diagnosing why a specific pod is stuck in CrashLoopBackOff or Pending states.
  • Retrieving recent error logs from containers to identify application failures.
  • Verifying the current configuration of Deployments, Services, or ConfigMaps during deployment issues.
  • Checking node resource utilization when cluster performance degrades unexpectedly.

Key capabilities

  • Executes standard kubectl subcommands like get, describe, logs, and exec.
  • Filters output to focus on specific namespaces, labels, or pod names.
  • Interprets complex Kubernetes status messages into actionable insights.
  • Supports context switching between different cluster configurations.

Example prompts

  • "Describe the 'production-frontend' pod in the 'prod-us-east' namespace and explain why it is not running."
  • "Show me the last 50 lines of error logs for any container named 'api-server' across all namespaces."
  • "Get the YAML definition for the 'database-service' and check if the port mapping matches the deployment spec."

Tips & gotchas

Ensure your AI agent has valid credentials and access to the target Kubernetes cluster context before running diagnostic commands. Be mindful of rate limits on large clusters when requesting logs from many pods simultaneously, as this can trigger API throttling.