Prompt Engineering

This project builds an executable thinking framework rather than a simple collection of quotes, based on Zhang Xuefeng's books, interviews, and decision records. It provides users with in-depth analysis and advice by distilling core mental models, decision heuristics, and communication DNA. Users can install it into Claude Code to perform professional selection and career planning analysis from Zhang Xuefeng's perspective.

This project constructs an AI analysis tool capable of simulating his straightforward, anti-cliché style by deeply distilling approximately 200,000 words of Tong Jincheng's original video content. Users can invoke this Skill via Claude Code to obtain deep insights into dating, interpersonal relationships, and personal growth. It does not simply repeat quotes, but instead utilizes Tong Jincheng's unique cognitive framework to help users analyze and solve practical problems.

Khazix Skills is an open-source collection of AI tools designed to transform the author's accumulated methodologies into reusable Prompts and Skills. The project includes lightweight prompt templates and structured instruction sets that adhere to the Agent Skills open standard. Users can integrate these tools into supported AI Agents via direct installation or manual configuration to enhance work efficiency.

This project provides a set of structured guidelines designed to improve the performance and reliability of AI coding agents. By implementing four core principles, it helps developers mitigate common LLM pitfalls like overcomplication, unnecessary code changes, and poor assumption management. Users can easily integrate these rules into their workflows via a Claude Code plugin or a project-specific CLAUDE.md file.

Agent Skills provides a collection of structured, production-grade engineering workflows designed to guide AI coding agents through the entire software development lifecycle. These 20 modular skills enforce best practices like test-driven development, security hardening, and rigorous code reviews to ensure consistent, high-quality output. By integrating these workflows, developers can transform AI agents from prototype-focused tools into reliable partners capable of maintaining professional engineering standards.

Caveman is a specialized plugin for AI coding agents that forces responses into a concise, telegraphic style to significantly reduce token usage. It maintains full technical accuracy while stripping away filler words, pleasantries, and unnecessary prose. The tool supports multiple intensity levels, including a classical Chinese mode, and provides utilities for compressing project documentation.

Evolver is a protocol-constrained engine designed to facilitate the self-evolution of AI agents through auditable and reusable assets. It scans runtime logs to generate structured GEP prompts, enabling teams to manage agent improvements with clear traceability. The system operates either as a standalone tool or within a host runtime, offering configurable strategies to balance innovation, stability, and repair.

get-shit-done is a spec-driven development system designed to maintain high code quality by preventing context rot in AI coding assistants. It orchestrates subagents to handle project planning, research, and execution while maintaining clean git history and atomic commits. The system provides a structured workflow for developers to build complex features consistently without the overhead of enterprise project management.

The Claude Cookbooks provide a comprehensive collection of code snippets and guides to help developers integrate Claude into their own applications. The repository covers a wide range of topics including tool use, multimodal capabilities, and advanced techniques like prompt caching. These resources are designed to be easily adaptable for various programming languages and project requirements.