AI Skills That Actually Do Things

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
job-radar	Your job-hunting autopilot's remote control. Tell your AI "write cover letters" in Telegram and it reads JDs, writes 25 tailored letters, zips them, and sends them back before you finish your coffee. Pairs with kc_job_radar — Docker required, sanity optional
ctf-kit	Battle-tested playbook for bypassing Windows app authentication — VMProtect, Themida, network verification, you name it. Born from 67+ failed attempts so you don't have to repeat them. Includes ready-to-use Frida recon scripts and a zero-dependency PE analyzer. Pairs well with ljagiello/ctf-skills for broader CTF coverage
banini	Track Taiwan's most famous "reverse indicator" investor on Threads, let Claude do the contrarian analysis on the spot. Zero API cost — Playwright scrapes locally, Claude IS the LLM. Rewritten from cablate/banini-tracker to eliminate $11/mo in Apify + LLM API fees. Pair with skill-cron for scheduled runs + Telegram push — setup guide
skill-cron	One manager to schedule them all. Register any skill for crontab execution + Telegram push — because claude -p doesn't support /skill syntax, so somebody had to build the bridge. Config in ~/.claude/configs/, logs auto-rotate, crontab entries self-managed
memory-lint	Your AI's memory directory accumulates duplicate rules, stale "active" projects, and orphan files over time. This skill scans for all of that and reports before Claude confidently quotes the wrong rule back at you. Read-only — finds problems, you decide what to fix
llm-wiki-lint	Karpathy's LLM Wiki pattern has a blind spot — past ~15 pages, stale claims, orphan cross-refs, and missing topics silently rot your knowledge base. This skill is the lint pass: contradictions, source traceability, data gaps, frontmatter completeness, index drift. For three-tier raw/ + wiki/ + schema repos. Read-only. Pair with memory-lint for full-stack AI knowledge hygiene

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.