linxiaotu

Repository Overview

This repository contains multiple projects including a quantitative trading system and AI engineering resources.

AI Development Guidelines

All AI-assisted development in this repository must follow the structured Task Workflow documented in:

📚 AI Rules and Guidelines

The workflow consists of 6 mandatory phases:

1. Questions - Requirements gathering
2. Design - Solution planning
3. Models - Data structure definition
4. Tests - Test creation (TDD)
5. Implementation - Code changes
6. Verification - Testing and validation

For detailed guidance on each phase, see the Execution Library.

Projects

• Quantitative Trading System - Automated futures trading system
• AI Engineering Resources
• Reference Materials

Uncommitted Changes Detection

This repository includes tools to detect uncommitted changes before deployments or commits:

Scripts

• check_uncommitted.py: Python script to check for uncommitted changes
• check-uncommitted.sh: Bash script to check for uncommitted changes

Usage

Run the check manually:

# Using Python script
python3 check_uncommitted.py

# Using Bash script
./check-uncommitted.sh

GitHub Actions

The repository includes a GitHub Actions workflow that automatically checks for uncommitted changes:

• Runs on pushes to main and develop branches
• Runs on pull requests to main and develop branches
• Fails if uncommitted changes are detected after running tests/builds

Exit Codes

• 0: No uncommitted changes detected (success)
• 1: Uncommitted changes detected (failure) Personal Learning and Development Repository

Contents

📚 Context Engineering for Claude Code

Comprehensive guide on Context Engineering methodology for AI programming assistants.

• Complete Guide - Full documentation with bilingual content (中文/English)
• Quick Start - Get started in 5 minutes
• Templates - Ready-to-use templates for your projects

Key Topics Covered:

• What is Context Engineering and why it matters
• Three-Phase Workflow (RPI: Research, Plan, Implement)
• Context files and project documentation
• Subagents and specialized workflows
• Advanced optimization techniques
• Practical implementation guide

🔧 Quantitative Trading System

Python-based quantitative trading system with multi-factor strategies.

• Location: ./quant-trading-system/
• Documentation

🤖 AI Engineering Projects

Various AI engineering projects and experiments.

• AI Podcast Generation: ./ai-engineering-hub-main/ai-podcast-generation/
• Paralegal Agent Crew: ./ai-engineering-hub-main/paralegal-agent-crew/

📖 Reference Materials

• Awesome Mac Tools: ./awesome-mac-master/
• North Star Framework: ./northstar-master/
• AI Tools System Prompts: ./system-prompts-and-models-of-ai-tools-main/

Quick Links

• Context Engineering Guide
• Trading System

License

MIT