kc_ai_skills — MCP Server by KerberosClaw

by KerberosClaw · MCP Server · ★ 76

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About kc_ai_skills

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., ) 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 | [rewrite-tone](rewrite-t

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Quick Facts

Stars76
Forks13
LanguagePython
CategoryMCP Server
LicenseMIT
Quality Score70.1572255237189/100
Last Updated2026-06-24
Created2026-03-18
Platformsclaude-code, mcp, python
Est. Tokens~14k

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Frequently Asked Questions

What is kc_ai_skills?

kc_ai_skills is AI Skills That Actually Do Things — reusable skills for any LLM workflow. It is categorized as a MCP Server with 76 GitHub stars.

What programming language is kc_ai_skills written in?

kc_ai_skills is primarily written in Python. It covers topics such as ai-skills, claude-code, llm.

How do I install or use kc_ai_skills?

You can find installation instructions and usage details in the kc_ai_skills GitHub repository at github.com/KerberosClaw/kc_ai_skills. The project has 76 stars and 13 forks, indicating an active community.

What license does kc_ai_skills use?

kc_ai_skills is released under the MIT license, making it free to use and modify according to the license terms.

What are the best alternatives to kc_ai_skills?

The top alternatives to kc_ai_skills on Agent Skills Hub include sap-skills, mcp-server-code-execution-mode, claudex. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.

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