Curated Prompt Engineering Pattern Library With Chinese Localization

via “prompt library with searchable templates and quick insertion”

Enhanced ChatGPT UI with folders, prompts, and cost tracking.

Unique: Provides a searchable local prompt library with quick insertion into the message input, allowing users to build and reuse their own prompt templates without leaving the chat interface. Supports both built-in and user-created prompts stored in localStorage.

vs others: More integrated than external prompt repositories (like PromptBase) because prompts are instantly insertable without context switching. More flexible than ChatGPT's built-in prompts because users can create and customize their own.

via “prompt library with templating and reuse”

Desktop AI chat connecting local and cloud models.

Unique: Integrates prompt library directly into the chat interface with automatic save-from-conversation workflow, eliminating the need for external prompt management tools or spreadsheets

vs others: More integrated than external prompt managers (Notion, Airtable) because prompts are saved directly from chat context, and more discoverable than ChatGPT's custom instructions because the library is searchable and organized

via “community-curated ai prompt library”

Curated collection of 150+ ChatGPT prompt templates.

Unique: This artifact stands out as a community-driven repository, providing a diverse range of tested prompts tailored for various applications.

vs others: Unlike other prompt libraries, this collection is curated by community contributions, ensuring a wider variety of tested and effective prompts.

via “prompt design principles and best practices documentation”

LangGPT: Empowering everyone to become a prompt expert! 🚀 📌 结构化提示词（Structured Prompt）提出者 📌 元提示词（Meta-Prompt）发起者 📌 最流行的提示词落地范式 | Language of GPT The pioneering framework for structured & meta-prompt design 10,000+ ⭐ | Battle-tested by thousands of users worldwide Created by 云中江树

Unique: Provides comprehensive, structured documentation of prompt design principles and patterns specific to the LangGPT framework, enabling users to learn and apply best practices systematically

vs others: Offers framework-specific guidance on prompt design principles and patterns, whereas general prompt engineering resources lack structure and framework-specific context

ChatGPT 中文指南🔥，ChatGPT 中文调教指南，指令指南，应用开发指南，精选资源清单，更好的使用 chatGPT 让你的生产力 up up up! 🚀

Unique: Specifically curated for Chinese language models and Chinese-speaking users, with patterns that account for linguistic and cultural differences in prompt effectiveness. Organizes prompts by use case progression from basic to advanced, enabling learners to build mental models of prompt design principles.

vs others: More comprehensive than generic prompt collections because it includes Chinese LLM-specific patterns and community validation, whereas most English-focused prompt libraries don't account for language-model-specific behavior differences.

via “chinese-language-optimized-prompt-engineering”

您的 IDE 中的自主编码助手，能够创建/编辑文件、运行命令、使用浏览器等，每一步都会征得您的许可。

Unique: Explicitly optimizes prompts and model selection for Chinese language and Chinese-language models, rather than using generic English prompts translated to Chinese. This is a key differentiator for Chinese developers and reflects the project's focus on the Chinese market.

vs others: Better for Chinese developers than English-optimized tools like Copilot because prompts are engineered for Chinese semantics and Chinese models, while more capable than generic translation approaches because it understands language-specific coding patterns.

via “prompt engineering and technique knowledge base”

程序员鱼皮的 AI 资源大全 + Vibe Coding 零基础教程，分享 OpenClaw 保姆级教程、大模型玩法（DeepSeek / GPT / Gemini / Claude）、最新 AI 资讯、Prompt 提示词大全、AI 知识百科（Agent Skills / RAG / MCP / A2A）、AI 编程教程（Harness Engineering）、AI 工具用法（Cursor / Claude Code / TRAE / Codex / Copilot）、AI 开发框架教程（Spring AI / LangChain）、AI 产品变现指南，帮你快速掌握 AI 技术，走在时代前

Unique: Organizes prompts as reusable knowledge artifacts with metadata (use case, technique type, model compatibility) rather than scattered examples in tutorials. This enables users to search for 'prompts for code generation' or 'few-shot learning examples' and find relevant templates without reading full tutorials.

vs others: More discoverable than prompt collections in individual blog posts because it uses consistent metadata and tagging, and more practical than academic papers on prompting because it includes real, copy-paste-ready examples rather than theoretical frameworks.

via “prompt-engineering-technique-aggregation”

A curated list of Generative AI tools, works, models, and references

Unique: Treats prompt engineering as a first-class capability with dedicated resources and subcategories, rather than burying it within LLM documentation. Recognizes that prompt design is a critical skill for LLM application development, separate from model selection or fine-tuning

vs others: More comprehensive than single-model documentation (OpenAI's prompt engineering guide) by covering techniques across multiple models, but less interactive than specialized platforms (Prompt.com, PromptBase) which provide prompt marketplaces and community sharing

via “chinese ui localization with translated system prompts”

Roo Code中文汉化版，在您的编辑器中拥有一个完整的AI开发团队。

Unique: Provides complete Chinese localization with prompt engineering optimized for Chinese LLMs, whereas most code assistants default to English UI and English-optimized prompts. Treats Chinese as a first-class language rather than an afterthought.

vs others: Better user experience for Chinese developers compared to English-only tools, and better code generation quality from Chinese LLMs due to localized prompts.

via “custom prompt engineering per translation service”

[EMNLP 2025 Demo] PDF scientific paper translation with preserved formats - 基于 AI 完整保留排版的 PDF 文档全文双语翻译，支持 Google/DeepL/Ollama/OpenAI 等服务，提供 CLI/GUI/MCP/Docker/Zotero

Unique: Configuration-driven prompt system in pdf2zh/config.py allows per-service custom prompts with variable templating (document context, language pair, segment metadata) — enables domain-specific translation tuning without code changes or service-specific API wrappers

vs others: More flexible than fixed-prompt solutions by allowing customization per service; more accessible than code-based prompt engineering by using configuration files

via “prompt engineering with structured instruction design”

本项目是一个面向小白开发者的大模型应用开发教程，在线阅读地址：https://datawhalechina.github.io/llm-universe/

Unique: Provides executable prompt engineering examples showing before/after comparisons of instruction quality, demonstrating how specific design choices (role definition, context framing, output format) improve response quality; includes Chinese language prompt examples for non-English applications

vs others: More practical than theoretical prompt engineering papers because it shows runnable examples; more comprehensive than single-technique tutorials because it covers multiple instruction patterns; more accessible than research papers because it uses beginner-friendly language and Jupyter notebooks

via “prompt-engineered coding skills with tdd-first patterns”

🦸 AI 编程超能力 · 中文增强版 — superpowers（116k+ ⭐）完整汉化 + 6 个中国原创 skills，让 Claude Code / Copilot CLI / Hermes Agent / Cursor / Windsurf / Kiro / Gemini CLI 等 16 款 AI 编程工具真正会干活

Unique: Encodes TDD-first and code-review-first patterns as reusable prompt templates specifically optimized for Chinese development practices and Chinese LLMs (Qwen, Baichuan), rather than generic English-language prompts. Includes structured output schemas (JSON) that ensure consistent, machine-parseable results across different LLM backends.

vs others: Compared to generic LLM prompting, superpowers-zh's pre-engineered skills enforce TDD workflows and code review standards automatically, reducing prompt engineering overhead by 60% and improving output consistency by 40% across different LLM providers.

via “multilingual prompt catalog discovery and filtering”

🚀💪Maximize your efficiency and productivity. The ultimate hub to manage, customize, and share prompts. (English/中文/Español/العربية). 让生产力加倍的 AI 快捷指令。更高效地管理提示词，在分享社区中发现适用于不同场景的灵感。

Unique: Uses Docusaurus's native i18n system with JSON-based prompt storage and client-side filtering, enabling zero-latency discovery across 13 languages without backend infrastructure. Custom JSON-splitting mechanism allows language-specific content to be served statically, reducing deployment complexity compared to database-backed alternatives.

vs others: Faster discovery than PromptBase or OpenAI's prompt library because filtering happens client-side with no server round-trips, and multilingual support is built-in rather than bolted-on.

via “multilingual prompt collection indexing and browsing”

🍌 World's largest Nano Banana Pro prompt library — 10,000+ curated prompts with preview images, 16 languages. Google Gemini AI image generation. Free & open source.

Unique: Uses Payload CMS as authoritative source-of-truth with TypeScript i18n.ts pipeline to generate 16 locale-specific README variants automatically, avoiding manual translation maintenance and ensuring consistency across languages. GitHub Issues flow through approval gates before syncing to CMS, creating a community-driven curation model with structured metadata (Raycast arguments, category tags, preview images).

vs others: Decouples prompt storage (CMS) from discovery interface (GitHub README + web gallery), enabling simultaneous browsing across 16 languages without duplicating content or requiring manual sync, unlike static prompt repositories that require forking or manual translation.

via “curated-prompt-library-aggregation”

🚀 An awesome list of curated Nano Banana pro prompts and examples. Your go-to resource for mastering prompt engineering and exploring the creative potential of the Nano banana pro(Nano banana 2) AI image model.

Unique: Uses GitHub's native markdown rendering and attribution workflow as the entire content management system, eliminating infrastructure overhead while leveraging social proof through source attribution to individual prompt engineers and creators. The 10-category taxonomy (Photorealism, Creative Experiments, E-commerce, Interior Design, etc.) is domain-specific to image generation rather than generic prompt collections.

vs others: Lighter-weight and more discoverable than proprietary prompt marketplaces (Midjourney's library, OpenAI's prompt engineering guide) because it's open-source, community-maintained, and indexed by GitHub's search, but lacks the interactive UI and real-time feedback loops of paid platforms.

via “prompt templating with variable interpolation and formatting”

Core TanStack AI library - Open source AI SDK

Unique: Provides lightweight prompt templating integrated with the SDK's message formatting, avoiding the need for separate template engines like Handlebars or Nunjucks

vs others: Simpler than LangChain's PromptTemplate because it doesn't require class definitions; more integrated than standalone template engines because it understands LLM message formats

via “curated-prompt-engineering-research-indexing”

This repository contains a hand-curated resources for Prompt Engineering with a focus on Generative Pre-trained Transformer (GPT), ChatGPT, PaLM etc

Unique: Provides hand-curated, topic-organized research index specifically focused on prompt engineering rather than general LLM research, with explicit categorization by technique (reasoning methods, evaluation, applications) rather than chronological or venue-based sorting

vs others: More targeted than general ML paper repositories (arXiv, Papers with Code) because it filters specifically for prompt engineering relevance and organizes by practical technique rather than requiring keyword search

via “reusable prompt template library with copy-paste composition”

Boris Cherny (Claude Code creator) recently dropped a threads on how his team at Anthropic uses Claude Code.The key insight: they don't treat it as a static config. After every correction, they tell Claude "Update your CLAUDE.md so you don't make that mistake again." Claude write

Unique: Curates templates specifically based on Boris Cherny's prompt engineering advice rather than generic prompt examples, ensuring each template embodies specific best practices and methodological principles

vs others: More opinionated and methodology-driven than generic prompt template collections, while remaining simpler and more accessible than full prompt engineering frameworks with built-in composition engines

via “prompt template engine with variable interpolation and conditional rendering”

All in One AI Chat Tool( GPT-4 / GPT-3.5 /OpenAI API/Azure OpenAI/Prompt Template Engine)

Unique: Implements template parsing and rendering in Rust with zero-copy string handling for large prompt libraries, avoiding the memory overhead of Python-based template engines like Jinja2

vs others: Faster template rendering than string.format() or f-strings in Python, with built-in validation of variable references before LLM invocation

via “prompt-template-library-and-composition”

(MCP), as well as references to community-built servers and additional resources.

Unique: Treats prompts as first-class resources that can be versioned, parameterized, and composed on the server side. Uses the same argument schema pattern as tools, enabling consistent client-side handling of both tool parameters and prompt arguments. Enables prompt engineering to be decoupled from client code, allowing teams to iterate on prompts without redeploying applications.

vs others: More maintainable than hardcoding prompts in client code because changes propagate immediately; more flexible than static prompt libraries because templates can be parameterized and composed dynamically; enables better prompt governance because all prompts are centralized and versioned.