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# 1. 克隆学习指南
git clone https://github.com/luongnv89/claude-howto.git
cd claude-howto

# 2. 复制你的第一个斜杠命令
mkdir -p /path/to/your-project/.claude/commands
cp 01-slash-commands/optimize.md /path/to/your-project/.claude/commands/

# 3. 在 Claude Code 中尝试输入
# /optimize

# 4. 设置项目记忆
cp 02-memory/project-CLAUDE.md /path/to/your-project/CLAUDE.md

# 5. 安装技能
cp -r 03-skills/code-review ~/.claude/skills/

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# 斜杠命令 (15 分钟)
cp 01-slash-commands/*.md .claude/commands/

# 项目记忆 (15 分钟)
cp 02-memory/project-CLAUDE.md ./CLAUDE.md

# 安装技能 (15 分钟)
cp -r 03-skills/code-review ~/.claude/skills/

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<!-- .claude/commands/optimize.md -->
---
name: Optimize Code
description: Analyze code for optimization opportunities
triggers: ["optimize", "refactor"]
---

## Quick Optimization Analysis

Let's analyze your code for potential optimizations:

1. Review algorithmic complexity
2. Identify redundant operations
3. Suggest memory-efficient alternatives
4. Check for caching opportunities

Run this command to get a structured analysis report.

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# CLAUDE.md - Project Standards

## Coding Standards
- Always use TypeScript strict mode
- Write unit tests for all public functions
- Document with JSDoc comments

## Preferred Patterns
- Use compositional design for utilities
- Implement error boundaries in React components
- Follow semantic versioning for API changes

## Context Rules
When working on auth modules:
- Review security implications
- Check token expiration handling
- Validate input sanitization

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# 通过 Hugging Face Transformers 使用
pip install transformers torch

from transformers import pipeline

# 加载 ASR 模型
asr = pipeline("automatic-speech-recognition", 
               model="microsoft/VibeVoice-ASR")

# 处理音频文件
result = asr("audio_file.wav")
print(result["text"])

# 或使用 Realtime 模型进行低延迟推理
from vibevoice_realtime import VibeVoiceRealtime

tts = VibeVoiceRealtime.from_pretrained("microsoft/VibeVoice-Realtime-0.5B")
with tts.stream() as streamer:
    for audio_chunk in streamer.feed_text("Hello world"):
        play_audio(audio_chunk)

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docker pull ghcr.io/microsoft/vibevoice-asr:latest
docker run -p 8000:8000 -v ./audio:/input ghcr.io/microsoft/vibevoice-asr:latest

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from vibevoice_asr import VibeVoiceASR

# 初始化模型
model = VibeVoiceASR.from_pretrained("microsoft/VibeVoice-ASR-7B")

# 处理长达 1 小时的会议录音
transcription = model.transcribe(
    "meeting_recording.mp3",
    hotwords=["product", "launch", "Q2"],  # 自定义热词
    language="zh-cn"  # 指定语言
)

# 输出结构化数据
for segment in transcription.segments:
    print(f"[{segment.start:.2f}s - {segment.end:.2f}s]")
    print(f"Speaker: {segment.speaker}")
    print(f"Text: {segment.text}\n")

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import sounddevice as sd
from vibevoice_realtime import VibeVoiceRealtime

# 初始化实时 TTS
tts = VibeVoiceRealtime.from_pretrained("microsoft/VibeVoice-Realtime-0.5B")

def voice_assistant():
    streamer = tts.stream()
    
    while True:
        text = get_user_input()  # 获取用户输入
        for audio in streamer.feed_text(text):
            sd.play(audio, tts.sample_rate)
            sd.wait()
        
        break  # 等待下一轮输入

voice_assistant()

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# 在 Claude Code 中
/plugin marketplace add https://github.com/Yeachan-Heo/oh-my-claudecode
/plugin install oh-my-claudecode
/setup

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npm i -g oh-my-claude-sisyphus@latest

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// ~/.claude/settings.json
{
  "env": {
    "CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS": "1"
  }
}

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# 3 个 executor 智能体一起修复所有 TypeScript 错误
/team 3:executor "fix all TypeScript errors"

# 使用 OMC CLI 启动 tmux 工作进程
omc team 2:codex "review auth module for security issues"
omc team status auth-review
omc team shutdown auth-review

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/ccg Review this PR — architecture (Codex) and UI components (Gemini)

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# .omc/skills/fix-proxy-crash.md
---
name: Fix Proxy Crash
description: aiohttp proxy crashes on ClientDisconnectedError
triggers: ["proxy", "aiohttp", "disconnected"]
source: extracted
---
Wrap handler at server.py:42 in try/except ClientDisconnectedError...

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/skill list | add | remove | edit | search
/learner extracts reusable patterns with strict quality gates

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# 克隆仓库
git clone https://github.com/shanraisshan/claude-code-best-practice.git
cd claude-code-best-practice

# 查看功能矩阵
cat README.md | grep -A 20 "Feature Table"

# 复制到项目中使用
cp -r best-practice/ .claude/

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# .claude/commands/weather-orchestrator.md
---
name: Weather Update
description: Get current weather with recommendations
---

## Weather Report

Let me check the weather for you:

1. Get current conditions via API
2. Analyze forecast trends
3. Provide activity recommendations

Would you like me to integrate this into your daily briefings?

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# orchestration-workflow.yaml
study:
  - research_requirements
  - gather_examples
  
plan:
  - define_specification
  - break_down_tasks

execute:
  - parallel_execution
  - checkpoint_creation

review:
  - code_review
  - test_validation

ship:
  - documentation_update
  - deployment_prep

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# 一键安装（适用于 Linux、macOS、WSL2）
curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bash

# 重新加载 shell
source ~/.bashrc  # 或 source ~/.zshrc

# 开始使用
hermes  # 交互式 CLI
hermes model  # 选择 LLM 提供商和模型
hermes tools  # 配置启用的工具
hermes config set  # 设置配置值
hermes gateway  # 启动即时通讯网关
hermes setup  # 运行完整设置向导

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hermes claw migrate  # 交互式迁移
hermes claw migrate --dry-run  # 预览将要迁移的内容
hermes claw migrate --preset user-data  # 仅迁移用户数据

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# 启动交互式 CLI
hermes

# 切换模型
/model openrouter/nousresearch/hermes-2-pro-mixtral-8x7b

# 检查上下文使用
/compress
/usage --days 7

# 查看可用技能
/skills

# 中断当前工作
Ctrl+C

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from hermes_sdk import HermesAgent

agent = HermesAgent(model="openrouter:gpt-4o")

# 发送邮件摘要任务
task = agent.create_task(
    name="Daily Email Summary",
    schedule="0 9 * * *",  # 每天早上 9 点
    prompt="Summarize today's unread emails by priority"
)

agent.run_async(task)

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/plugin install superpowers@claude-plugins-official

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/plugin marketplace add obra/superpowers-marketplace
/plugin install superpowers@superpowers-marketplace

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/add-plugin superpowers
# 或在插件市场中搜索 "superpowers"

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# 当智能体检测到你在构建新功能时，自动触发 brainstorming
"Help me plan this feature"

# 设计批准后自动进入 writing-plans 阶段
"go ahead with implementation"

# 执行过程中自动启用 TDD
running tasks... (RED-GREEN-REFACTOR loop activated)

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# skills/writing-plans/SKILL.md
---
name: Writing Plans
trigger: "After design approval"
workflow: plan → task_breakdown → verification
---

## Implementation Plan Template

Every task includes:
1. Exact file paths
2. Complete code snippets
3. Verification steps
4. Expected outcomes

Follow these principles:
- true red/green TDD
- YAGNI (You Aren't Gonna Need It)
- DRY (Don't Repeat Yourself)

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# 通过 pip 安装
pip install paddleocr>=3.3.0

# 导入并使用
from paddleocr import PaddleOCR, PPStructure

# 初始化 OCR 引擎
ocr = PaddleOCR(use_angle_cls=True, lang="ch")

# 识别图像
result = ocr.ocr("document.jpg", cls=True)

# 结构化解析
structure = PPStructure(table=True, layout=True)
result = structure("complex_document.pdf")

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docker pull docker.paddlepaddle.org/paddleocr/paddleocr:3.3-latest
docker run -v ./data:/data docker.paddlepaddle.org/paddleocr/paddleocr:3.3-latest
python -u app.py --use_gpu --gpu_id=0

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from paddleocr import PaddleOCR, PPStructure

# 初始化
ocr = PaddleOCR(lang="multi")
structure = PPStructure(table=True, layout=True)

# 处理 PDF 文档
pdf_path = "invoice_20260331.pdf"
img_list = convert_pdf_to_images(pdf_path)

results = []
for img in img_list:
    ocr_result = ocr.ocr(img, det=True, rec=True)
    structure_result = structure(img)[0] if isinstance(img, str) else structure(img[0])
    
    results.append({
        "ocr": ocr_result,
        "layout": structure_result.layout,
        "tables": structure_result.tables
    })

# 转换为 Markdown
markdown_content = markdownify(results)
save_to_database(markdown_content)

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languages = ["en", "zh", "ja", "ko", "ar", "hi"]
documents = glob.glob("/uploads/documents/*")

for doc in documents:
    detected_lang = detect_language(doc)  # 自动检测语言
    if detected_lang in languages:
        result = ocr.ocr(doc, lang=detected_lang)
        process_and_store(result)

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# 下载配置
curl -O https://raw.githubusercontent.com/vas3k/TaxHacker/main/docker-compose.yml

# 启动服务
docker compose up

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# 克隆仓库
git clone https://github.com/vas3k/TaxHacker.git
cd TaxHacker

# 安装依赖
npm install

# 设置环境变量
cp .env.example .env

# 编辑 .env 配置数据库连接
# DATABASE_URL=postgresql://user@localhost:5432/taxhacker

# 初始化数据库
npx prisma generate && npx prisma migrate dev

# 启动开发服务器
npm run dev

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import requests
from datetime import datetime

API_BASE = "http://localhost:7331/api"

# 上传收据图片
with open("receipt.jpg", "rb") as f:
    response = requests.post(
        f"{API_BASE}/upload",
        files={"file": f},
        headers={"Authorization": f"Bearer {API_KEY}"}
    )
    
document_id = response.json()["id"]

# AI 提取数据
extraction = requests.post(
    f"{API_BASE}/documents/{document_id}/extract",
    json={"provider": "openai", "prompt": "Extract merchant, date, total, items"}
)

# 保存结构化数据
transaction = {
    "date": extraction.json()["date"],
    "merchant": extraction.json()["merchant"],
    "amount": extraction.json()["total"],
    "currency": extraction.json()["currency"],
    "category": extraction.json()["category"],
    "document_id": document_id
}

requests.post(f"{API_BASE}/transactions", json=transaction)

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{
  "custom_fields": [
    {
      "name": "project_code",
      "prompt_template": "Extract project code from invoice header",
      "example_values": ["PRJ-2026-001", "PRJ-2026-002"]
    },
    {
      "name": "department",
      "prompt_template": "Identify billing department",
      "categories": ["Engineering", "Marketing", "Sales"]
    }
  ]
}

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# 环境要求
# OS: macOS / Linux / WSL / Windows
# Python: 3.12+
# Node.js: 18+
# Package Manager: uv

# 克隆仓库
git clone https://github.com/OpenBMB/ChatDev.git
cd ChatDev

# 同步依赖
uv sync

# 前端依赖
cd frontend && npm install

# 环境变量配置
cp .env.example .env

# 同时启动前后端
make dev

# 访问 Web 控制台 http://localhost:5173

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# 确保有 .env 文件和 API 密钥
docker compose up --build

# 后端：http://localhost:6400
# 前端：http://localhost:5173

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from runtime.sdk import run_workflow

# 执行工作流
result = run_workflow(
    yaml_file="yaml_instance/data_analysis.yaml",
    task_prompt="Create 4–6 high-quality PNG charts for my real-estate dataset",
    attachments=["/path/to/housing_data.csv"],
    variables={"API_KEY": "sk-xxxx"}
)

if result.final_message:
    print(f"Output: {result.final_message.text_content()}")

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# custom_workflow.yaml
nodes:
  - id: researcher
    type: llm_agent
    prompt: "Research market trends in ${industry} sector"
    providers: [deep-research, web-search]
    
  - id: analyst
    type: llm_agent
    prompt: "Analyze ${research_results} for insights"
    dependencies: [researcher]
    
  - id: reporter
    type: output_node
    template: |
      ## Market Report
      
      Based on analysis:
      ${analysis_summary}
      
      Key findings:
      ${key_insights}
    
    dependencies: [analyst]

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# 克隆仓库
git clone https://github.com/jwasham/coding-interview-university.git
cd coding-interview-university

# Fork 并跟踪进度
git remote add upstream https://github.com/jwasham/coding-interview-university.git
git checkout -b my-progress

# 完成后标记进度
[x] Read Introduction
[x] Choose Programming Language
[x] Complete Data Structures section

git commit -am "Completed foundational topics"
git push origin my-progress