Skillget

kc_ai_skills

mcp
SUMMARY

AI Skills That Actually Do Things — reusable skills for any LLM workflow

README.md

AI Skills That Actually Do Things

License: MIT

正體中文

A collection of AI agent skills that solve real problems — not "summarize this PDF" kind of skills, but "scan my repo for leaked API keys before I push" kind of skills. Works with any LLM client that supports skill/prompt loading, cloud or local.

Skills follow the Claude Code skill convention (SKILL.md + scripts/), but the concepts are framework-agnostic. Think of them as reusable checklists your AI actually follows.

Security Notice: These skills are designed for local development and trusted LAN environments. Skills that interact with external services (e.g., searxng) default to secure settings (TLS verification enabled), but do not implement additional authentication layers. Review each skill's configuration before deploying in sensitive environments.

Skills

Skill What It Actually Does
prep-repo The "did I forget anything?" checklist before pushing to GitHub. README, commits, secrets, broken links — the stuff you always forget at 2 AM
llm-benchmark Find out which Ollama model actually fits in your GPU — before you waste 30 minutes downloading one that doesn't
searxng Give your local LLM the ability to search the web without sending your queries to Google
rewrite-tone Turn your dry technical docs into something people actually want to read. War stories > whitepapers
job-scout Research a company before you waste time applying. Salary, reviews, red flags, financials — the due diligence you should've done before that last interview
repo-scan Security scan a GitHub repo before you install it. Static analysis, dependency audit, supply chain risks, issue-reported vulnerabilities, maintainer health — because npm install random-package shouldn't require a leap of faith
md2pdf Turn your Markdown into a PDF that doesn't look like it was generated by a computer from 2003. Handles Mermaid diagrams, CJK fonts, and ASCII art conversion — because we already mass-debugged all the cursed edge cases so you don't have to
spec Spec-driven development workflow — from fuzzy idea to verified deliverable. One command, auto-detects project state, walks you through: requirements → review → implement → verify → report. Because "just start coding" is how you end up rewriting everything

Installation

Grab what you need, leave what you don't:

git clone https://github.com/KerberosClaw/kc_ai_skills.git

# Example: install for Claude Code (user-level)
cp -r kc_ai_skills/prep-repo ~/.claude/skills/

# Example: install for OpenClaw (workspace-level)
cp -r kc_ai_skills/searxng ~/.openclaw/workspace/skills/

Naming tip: Feel free to rename the skill folder with your own prefix when copying (e.g. my_prep-repo). It won't break anything. Probably.

Other clients: Each SKILL.md is a self-contained markdown instruction file. You can paste its content into any AI chat, system prompt, or custom instruction field. No SDK required, no API key needed — just copy and paste.

Skill Structure

Every skill follows a dead-simple convention. If you can write markdown, you can write a skill:

skill-name/
├── SKILL.md          # Frontmatter (name, description, version) + instructions
└── scripts/          # Executable scripts (optional)
    └── script.py

Related Projects

  • kc_tradfri_mcp — "Turn on the living room lights" — yes, we made an AI do that
  • kc_openclaw_local_llm — We tested 13 local LLMs. Only 2 could reliably call tools. Here's the full report.

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