kubectl-ai

1 stars 2 forks

AI-powered Kubernetes operations using kubectl-ai from Google Cloud Platform. This skill should be used when managing Kubernetes clusters with natural language commands, generating manifests, troubleshooting issues, and performing AI-assisted DevOps. Use this skill for Phase IV AIOps integration with Minikube and cloud clusters.

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Installation for Agentic Skill

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Claude Code (CLI) Fast

skilz install mjunaidca/mjs-agent-skills/kubectl-ai

OpenCode (CLI) Fast

skilz install mjunaidca/mjs-agent-skills/kubectl-ai --agent opencode

OpenAI Codex (CLI) Native

skilz install mjunaidca/mjs-agent-skills/kubectl-ai --agent codex

Gemini CLI (Project) Project

skilz install mjunaidca/mjs-agent-skills/kubectl-ai --agent gemini

First time? Install Skilz: pip install skilz

Works with 22+ AI coding assistants

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For Claude Desktop Easy

Download Agent Skill ZIP

Extract and copy to ~/.claude/skills/ then restart Claude Desktop

Manual Installation

1. Clone the repository:

git clone https://github.com/mjunaidca/mjs-agent-skills

2. Copy the agent skill directory:

cp -r mjs-agent-skills/docs/taskflow-vault/skills/engineering/kubectl-ai ~/.claude/skills/

Owner: mjunaidca (GitHub)
Repository: mjs-agent-skills
Stars: 1
Forks: 2
Type: Technical
Meta-Domain: cloud infrastructure
Primary Domain: kubernetes
Market Score: 17

Agentic Skill Grades →

Agent Skill Grade

Score: 86/100 Click to see breakdown

Score Breakdown

Spec Compliance

11/15

PDA Architecture

23/30

Ease of Use

22/25

Writing Style

9/10

Utility

18/20

Modifiers: +3

Areas to Improve

Description needs trigger phrases
Missing TOC for long file
No numbered workflows

Recommendations

Add trigger phrases to description for discoverability
Add table of contents for files over 100 lines

Graded: 2026-01-24

Developer Feedback

Noticed your kubectl-ai skill handles the intersection of CLI tooling and AI pretty elegantly—the way you've structured the disclosure makes it accessible without oversimplifying the complexity. Grabbed a B+ (86) and had some thoughts on the documentation flow if you're interested in feedback.

Links:

The TL;DR

You're at 86/100, solid B territory. This is based on Anthropic's best practices for skill design. Your writing style and examples are genuinely strong (9/10 and 3/3 respectively)—the prompt patterns in references are well-structured. Progressive Disclosure Architecture needs the most work (23/30)—mainly missing a table of contents and some structural consolidation that would improve token efficiency.

What's Working Well

Writing is tight and instructional. You're using imperative mood throughout with zero marketing fluff. That's harder than it sounds.
Examples are actually useful. The kubectl-ai commands paired with explanations in the prompt-patterns.md give people real starting points, not just theory.
Clear metadata and triggers. Your description clearly signals "Phase IV AIOps, natural language commands, DevOps"—someone searching for Kubernetes + natural language will find this.
Good layering. SKILL.md as the overview with references/prompt-patterns.md for templates is the right structure.

The Big One: Missing Table of Contents

Your SKILL.md is 254 lines without a TOC. That's the main thing pulling your PDA score down (23/30). When someone's scanning for "MCP Server Mode" or "Interactive Mode," they're scrolling blind.

Fix: Add a markdown TOC right after the frontmatter:

## Navigation
- [Installation](#installation)
- [Configurati...

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