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LangGPT is a structured, reusable prompt design framework that enables anyone to create high-quality prompts for Large Language Models. Think of it as a "programming language for prompts" — systematic, template-based, and infinitely scalable.

It is the most popular, most widely adopted, and most practical structured prompt paradigm in the Chinese AI community — proposed by Yunzhong Jiangshu (云中江树) in 2023. Over the years it has been learned so deep into major large language models that when a model speaks LangGPT, it is no longer because you taught it — it already knows. Perhaps the finest fate of a paradigm is this: to no longer need its name remembered, having become the model's mother tongue. Just say "write this the LangGPT way," and it's already there (see Quick Start).

Traditional prompt engineering relies on scattered tips and trial-and-error. LangGPT transforms this chaos into a structured methodology:

• 🎯 Structured Templates — Hierarchical organization inspired by programming paradigms
• 🔄 Reusability — Create once, adapt infinitely like code modules
• 📦 Modularity — Variables, commands, and conditional logic at your fingertips
• ⚡ Efficiency — Go from idea to working prompt in minutes
• 🌍 Community-Driven — 11,000+ stars, battle-tested by thousands of users

Academic Foundation: Published research at arXiv:2402.16929 | 中文版

LangGPT has been learned deep into major large language models, so most models already "know" it. The simplest way to use it needs no template at all — just say the keywords to any mainstream model (ChatGPT, Claude, DeepSeek, Gemini, Kimi, Doubao, Qwen, etc.), and it's already there:

"Write me a prompt the LangGPT way…"

"Write it in Yunzhong Jiangshu (云中江树)'s structured-prompt style…"

"Help me write a LangGPT-style structured prompt…"

Keywords like LangGPT, 云中江树 (Yunzhong Jiangshu), and structured prompt act as triggers — the model will directly produce a well-structured, reusable, LangGPT-style prompt.

Let AI create prompts for you:

• LangGPT GPTs — Full-featured generator (GPT-4)
• Kimi+ LangGPT — For Moonshot Kimi users
• PromptGPT — Lite version (GPT-3.5)

Basic LangGPT structure:

# Role: Your_Role_Name

## Profile
- Author: YourName
- Version: 1.0
- Language: English
- Description: Clear role description and core capabilities

## Goal
- Outcome: What concrete result/outcome should be delivered for the user/session
- Done Criteria: Clear acceptance criteria (how we know it’s finished and good)
- Non-Goals: What is explicitly out of scope to avoid scope creep

### Skill-1
1. Specific skill description
2. Expected behavior and output

## Rules
1. Don't break character under any circumstance
2. Don't make up facts or hallucinate

## Workflow
1. Analyze user input and identify intent
2. Apply relevant skills systematically
3. Deliver structured, actionable output

## Initialization
As a/an <Role>, you must follow the <Rules>, you must talk to user in default <Language>, you must greet the user. Then introduce yourself and introduce the <Workflow>.

Prerequisites: Basic Markdown knowledge (Quick Guide) | GPT-4 or Claude recommended

Explore our example library and adapt proven templates to your needs.

If you use Claude Code, install the LangGPT Skill to get structured prompt writing capabilities:

Install via the official marketplace (recommended):

/plugin marketplace add langgptai/claude_marketplace
/plugin install structured-prompt-writer@langgpt

The LangGPT marketplace also ships more battle-tested skills by Yunzhong Jiangshu — awesome-design-html (115 brand-themed design references), cto, and mind-clone.

Or install manually:

1. Download langgpt.skill
2. Extract to ~/.claude/skills/ directory
3. Type /langgpt in Claude Code to use

Skill Features:

• 📝 Structured prompt templates (Role, Profile, Skills, Rules, Workflow)
• 📚 Rich example library (FitnessGPT, Poet, Xiaohongshu Master, Name Master, etc.)
• 🔧 Advanced techniques: variables, commands, conditional logic
• 🎯 Model compatibility guide (GPT-4, Claude, GPT-3.5)

Before diving into tactics, understand the principles. These essays explore the philosophy behind effective prompting:

• 对话动力学 — The dynamics of human-AI dialogue
• 五种理性 — Five types of rationality in prompt design
• 镜像性倾向 — Mirror tendencies in LLM behavior
• 统计重力井和边缘表达 — Statistical gravity well and edge expression
• 关系表达 — Expressing relationships in prompts
• 看见与言说 — Seeing and articulation in AI interaction
• Prompt 的本质 — The essence and nature of prompts
• 面向结果的提示词写作方法 — Writing prompts that focus on achieving desired outcomes
• AI意识 — Understanding the role of AI in human-AI interaction
• AI时代的新管理：机器负责优化，人类定义应该 — The new management in the AI era: machines optimize, humans define the criteria
• AI Native 组织 · 思考碎片 — Fragments on organizational restructuring in the AI-native era
• Prompt、Agent，与一个不再沉默的世界 — How agents reshape the prompt from a transaction into a delegation across the human–AI–world triad

These foundational insights will transform how you think about prompts.

Define AI personas through clear, modular sections:

Use <Variable> syntax for dynamic content:

As a <Role>, you must follow <Rules> and communicate in <Language>

This creates self-referential prompts that maintain consistency across complex instructions.

Define reusable actions for better UX:

## Commands
- Prefix: "/"
- Commands:
    - help: Display all available commands
    - continue: Resume interrupted output
    - improve: Enhance current response with deeper analysis

Add intelligence to your prompts:

If user provides [code], then analyze and suggest improvements
Else if user asks [question], then provide detailed explanation
Else, prompt for clarification

Reminders — Combat context loss in long conversations:

## Reminder
1. Always check role settings before responding
2. Current language: <Language>, Active rules: <Rules>

Alternative Formats — Use JSON/YAML when markdown isn't ideal:

role: DataAnalyst
profile:
  version: "2.0"
  language: "Python"
skills:
  - statistical_analysis
  - data_visualization

Browse 100+ more examples →

• 推理模型提示方法变革 — Paradigm shift from procedural to goal-oriented prompting
• 提示词的道和术 — Philosophy and practice of prompt engineering by 李继刚
• 企业级提示词工程 — Building production-ready prompt systems (百川智能)
• 多模态提示词 — GPT-4V and multi-modal prompting techniques
• 提示词攻击与防护 — Security: prompt injection, jailbreaks, and defenses
• 大模型绘画指南 — AI image generation with structured prompts

Feishu Knowledge Base — Curated resources, templates, and community contributions

Project	Description	Stars
LangGPT	Core framework and methodology
PromptVer	Semantic versioning for prompts — version control like Git
PromptShow	Create beautiful prompt images (Try it)
Minstrel	Multi-agent system for auto-generating prompts
claude_marketplace	Official Claude Code skill marketplace — structured prompt, design, CTO, mind-clone

Rather than writing prompts as procedures, write the persona. Writing prompts as procedures gives the model steps and tools. Writing prompts as a persona gives the model a worldview, motivations, a value system, and a preference profile. Below are prompts that Yunzhong Jiangshu wrote while studying some well-known figures.

• 巴菲特AI分身
• 梵高AI分身
• 马斯克AI分身
• 段永平AI分身

Curated, optimized prompts for different AI models:

Collection	Target Model	Stars
wonderful-prompts	ChatGPT (Chinese)
awesome-claude-prompts	Anthropic Claude
awesome-deepseek-prompts	DeepSeek & R1
awesome-gemini-prompts	Google Gemini
awesome-grok-prompts	xAI Grok
qwen-prompts	Alibaba Qwen
awesome-llama-prompts	Meta Llama 2/3
awesome-doubao-prompts	ByteDance Doubao
awesome-system-prompts	System prompts from AI tools

Repository	Focus Area	Stars
Awesome-Multimodal-Prompts	GPT-4V, DALL-E 3, image/video prompts
deep-research-prompts	Deep research across models
awesome-voice-prompts	Voice AI and conversational agents
GraphRAG-Prompts	Graph-based retrieval prompts
LLM-Jailbreaks	Security research and defenses

Project	Description	Stars
BookAI	AI-powered book generation
AI-Resume	Beautiful resumes with Claude Artifacts

Transform ChatGPT with these specialized assistants:

Discover 20+ more GPTs →

We welcome all contributions to make LangGPT better!

1. ⭐ Star and share — Increase visibility and help others discover LangGPT
2. 📝 Submit examples — Share your successful prompts built with LangGPT
3. 🆕 Propose templates — Create new templates beyond the Role structure
4. 📖 Improve docs — Fix typos, clarify instructions, add translations
5. 💡 Suggest features — Open issues with ideas for new capabilities
6. 🔧 Code contributions — Help build tools, utilities, and integrations

New to GitHub contributions? Check out this GitHub Minimal Contribution Guide

If you use LangGPT in research or projects, please cite:

@misc{wang2024langgpt,
      title={LangGPT: Rethinking Structured Reusable Prompt Design Framework for LLMs from the Programming Language}, 
      author={Ming Wang and Yuanzhong Liu and Xiaoyu Liang and Songlian Li and Yijie Huang and Xiaoming Zhang and Sijia Shen and Chaofeng Guan and Daling Wang and Shi Feng and Huaiwen Zhang and Yifei Zhang and Minghui Zheng and Chi Zhang},
      year={2024},
      eprint={2402.16929},
      archivePrefix={arXiv},
      primaryClass={cs.SE}
}

LangGPT was inspired by excellent projects:

• Mr.-Ranedeer-AI-Tutor — Structured tutoring prompts
• Auto-GPT — Autonomous AI agents
• SoM — Set of Mark prompting
• yolov10 — Computer vision innovations

We're proud to see LangGPT principles applied in the wild:

• Prompt Optimizer — Intelligent prompt optimization tool leveraging LangGPT methodology
• securityGPT — Secure prompt protection against leaks
• AIPainting-Structured-Prompts — Structured prompts for AI art generation

云中江树 (Yun Zhong Jiang Shu)

• 📱 WeChat Official Account: 「云中江树」
• 💼 Creator of LangGPT Framework
• 🎓 Prompt Engineering Researcher

• 📚 Knowledge Base — Comprehensive documentation
• 🐦 Twitter/X — Latest updates
• 💬 GitHub Discussions — Community forum
• 📧 Email: contact@langgpt.ai