yaojingang/yao-open-promptsPublic

Practical bilingual AI prompt library for prompt engineering, work, learning, content, marketing, and everyday use.

AI summary: An open-source library of tested bilingual prompts for AI models, categorized by use cases like coding and marketing.

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2.8K
Forks
459
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12
Open issues
0
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0
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~1
Commits
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1

PythonOtherCreated May 6, 2026Last push 2mo ago+10 stars this week+48 this month

Quick answers

What is yao-open-prompts?
An open-source library of tested bilingual prompts for AI models, categorized by use cases like coding and marketing.
What does yao-open-prompts do?
Yao Open Prompts is an extensive repository containing over 110 refined Chinese and English prompts designed for Large Language Models (LLMs) like GPT-4 and Claude. The project acts as a categorized library, organizing prompts into scenarios such as content creation, software development, AI learning, and marketing. It includes advanced tools like a RTF-based meta-prompt system to compile natural language requirements into optimized prompts. The repository strips away promotional material from the original collection to focus entirely on reproducible, copy-pasteable prompt text with structured frontmatter. This allows users to directly deploy proven structures for highly specific tasks.
Who is yao-open-prompts for?
Developers, prompt engineers, content creators, and marketers looking for high-quality, pre-tested prompts to improve their interactions with LLMs.
How do I get started with yao-open-prompts?
Browse the CATALOG.md file to find a relevant prompt.
How popular is yao-open-prompts on GitHub?
yaojingang/yao-open-prompts has 2,833 stars and 459 forks on GitHub, and gained 10 stars in the last 7 days.
What license does yao-open-prompts use?
yaojingang/yao-open-prompts is released under the Other license.

Star history

since Jul 29, 2026
01K2KJul 2026Aug 2026Sep 2026Oct 2026
2.8K stars as of Oct 3, 2026. Measured daily since Jul 29, 2026; GitHub no longer exposes earlier star timestamps.

Contribution activity

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19 commits in the last yearLessMore

Signals and awards

derived from tracked data
  • Continuous integration

    Automated checks passing

What yao-open-prompts does

Yao Open Prompts is an extensive repository containing over 110 refined Chinese and English prompts designed for Large Language Models (LLMs) like GPT-4 and Claude. The project acts as a categorized library, organizing prompts into scenarios such as content creation, software development, AI learning, and marketing. It includes advanced tools like a RTF-based meta-prompt system to compile natural language requirements into optimized prompts. The repository strips away promotional material from the original collection to focus entirely on reproducible, copy-pasteable prompt text with structured frontmatter. This allows users to directly deploy proven structures for highly specific tasks.

Developers, prompt engineers, content creators, and marketers looking for high-quality, pre-tested prompts to improve their interactions with LLMs.

  • Categorized prompt library: Organizes over 100 prompts into distinct domains like AI working, learning, content creation, and programming.
  • Meta-prompt generation system: Features a V1.0 RTF meta-prompt architecture that acts as a compiler to design and optimize other prompts.
  • Bilingual support: Provides synchronized Chinese and English versions of the prompts, maintained in separate directory structures.
  • Standardized file format: Uses consistent YAML frontmatter for each prompt file to track versions, categories, and author metadata.
  • Specialized marketing templates: Includes 25 specific GEO (Generative Engine Optimization) marketing templates for structural data and growth diagnostics.

Where teams use it

Prompt Engineering Learning

Study complex prompt structures like Feynman questioning or critical thinking templates to improve personal AI interaction skills.

Content Creation

Leverage pre-tested templates to generate wide-format infographic copy, short video scripts, or optimized SEO/GEO articles.

Productivity Enhancement

Use ready-made prompts for contract drafting, product prototyping, or creating HTML landing pages within enterprise workflows.

Prompt Optimization

Utilize the RTF meta-prompt system to diagnose, refactor, and improve existing internal prompts.

Getting started: Browse the CATALOG.md file to find a relevant prompt.

README

main branch

Yao Open Prompts

网页导航 · 完整目录 · English README · 英文提示词 · GitHub 仓库

《姚金刚提示词合集》的开源版本,一个覆盖提示词工程、工作、学习、内容、营销和生活场景的中英双语 AI 提示词库。

本仓库从原始合集文档和后续提示词目录中整理出 118 个中文提示词文件,并按场景重新分类。每个提示词保留可复制的正文,去除了原文中不适合放入开源仓库主体的教程推广、效果截图、视频附件说明和 HTML 样式残留。

系列型内容会优先合并成主题合集,例如 50个 Nano Banana 创意提示词 和 Nano Banana PPT 场景提示词合集,避免仓库目录被大量零散短提示词打散。

近期补充重点来自 05-Prompts/Generators、05-Prompts/Meta、05-Prompts/GEO、05-Prompts/Scenarios/Learning、05-Prompts/Products 和《AI营销:从SEO到GEO》提示词合集:智能元提示词系统升级到 V1.0,新增了合同生成、产品原型、网页 PPT V3、公众号 HTML、Schema.org GEO、GEO文章AI友好化改造、网页逆向、费曼提问学习、批判思维、36 个内容与运营类实战提示词、25 个 GEO 营销实战模板,以及宽幅知识插画信息图生成器。

重点推荐

智能元提示词设计与优化系统 V1.0:基于 RTF 增强架构,将自然语言需求编译为可直接使用的提示词,也能诊断、重构和优化已有提示词。系统固定交付证据化质量报告与正式提示词,并覆盖部署适配、边界控制和设计级静态测试。查看 V1.0 升级说明。

宽幅知识插画信息图生成器:把一段文案提炼为适合社交媒体和 PPT 展示的宽幅知识插画信息图,强调结构提炼、视觉分层和极简中文表达。

GEO文章AI友好化改造提示词:面向已有文章做 GEO 优化改造,通过结构化重构、语义优化和证据强化,提升 AI 搜索可见度和引用率。

专题入口

36 个内容与运营提示词:新增提示词已直接并入 prompts/06-ai-content/,采用与仓库其他提示词一致的文件命名方式,覆盖短视频文案、人设风格、平台运营、行业内容、直播转化、私域成交、AI 绘画、数据复盘和爆款重构。

25 个 GEO 营销实战模板:从《AI营销:从SEO到GEO》提示词合集拆分,覆盖机会判断、原理分析、内容工程、信源建设、数据监测、增长诊断和合规风险。

English README:英文说明文档入口,对应导航到 118 个英文提示词,英文提示词按 prompts/ 的相同路径完整镜像在 prompts-en/ 下。

仓库结构

prompts/                # 按场景分类的提示词正文
prompts-en/             # 英文同步版提示词
references/             # 推荐资源、第三方内容和外部项目索引
templates/              # 新增提示词模板
maintenance/            # 维护、评审、发布检查清单
scripts/                # 目录生成和仓库质量检查脚本
docs/                   # 提示词类型与代表提示词网页
README.en.md            # 英文说明文档
CATALOG.md              # 全量提示词索引
CHANGELOG.md            # 更新记录
CONTRIBUTING.md         # 贡献和持续迭代规则

分类

分类 数量 说明
AI方法 8 元提示词、反编译、网页逆向和提示词工程方法。
AI工作 10 面向企业、合同、销售、客服、产品原型、PPT、网页等生产力场景。
AI学习 11 学习方法、记忆术、费曼提问、习惯养成和学习助理。
AI生活 2 健康、亲子歌曲等生活场景。
AI教育 4 儿童教育、互动学习页面和小游戏创作。
AI内容 50 写作、润色、标题、公众号 HTML、短视频、内容运营、图像和 PPT 创意。
AI编程 1 架构设计和编程协作。
AI营销 29 GEO 内容生成、文章改造、结构化数据、信源建设、数据监测、增长诊断和合规风险。
AI思考 3 批判思维、记忆、标题和思维类灵感提示词。

完整目录见 CATALOG.md。

使用方式

  1. 在 CATALOG.md 中按场景找到提示词。
  2. 打开对应 Markdown 文件,复制 Prompt 区域。
  3. 将 {{变量}}、[占位符] 或示例内容替换为你的真实任务信息。
  4. 在目标模型中测试输出,并根据结果记录版本迭代。

提示词文件规范

每个提示词文件包含统一 frontmatter:

title: 提示词标题
category: 一级分类
subcategory: 子类
source_section: 原合集章节号
author: 作者或来源
version: 提示词版本
created: 创建日期
status: active | draft | third-party-review
tags: 标签列表

正文只保留三部分:标题、简介、Prompt。需要展示案例、评测截图、教程链接或长说明时,优先放到 references/ 或后续的案例目录,不和可复制提示词正文混在一起。

持续更新机制

  • 新增提示词:复制 prompt-file-template.md,放到对应分类目录。
  • 更新提示词:优先在原文件内提升 version,并在 CHANGELOG.md 记录变更。
  • 调整分类:同步更新 frontmatter 的 category/subcategory/tags,然后重新生成目录。
  • 质量检查:运行 python3 scripts/check_repo.py。
  • 重建目录:运行 python3 scripts/generate_catalog.py。
  • 重建网页:运行 python3 scripts/generate_webpage.py。
  • 发布节奏:建议使用日期版本,如 v2026.05.1,每次发布前走 release-checklist.md。

开源与来源策略

本仓库采用 CC BY 4.0 作为提示词内容许可;如果后续加入脚本或工具代码,可单独使用 MIT。明显第三方或转载内容不直接并入主提示词库,先放在 references/ 或标记为 third-party-review,确认授权后再发布。

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19 commits in the last 52 weeks.

When work happens

weekday and hour
SunMonTueWedThuFriSat036912151821Sun 0:00 — 0 commitsSun 1:00 — 0 commitsSun 2:00 — 0 commitsSun 3:00 — 0 commitsSun 4:00 — 0 commitsSun 5:00 — 0 commitsSun 6:00 — 0 commitsSun 7:00 — 0 commitsSun 8:00 — 0 commitsSun 9:00 — 0 commitsSun 10:00 — 0 commitsSun 11:00 — 0 commitsSun 12:00 — 0 commitsSun 13:00 — 0 commitsSun 14:00 — 0 commitsSun 15:00 — 0 commitsSun 16:00 — 0 commitsSun 17:00 — 0 commitsSun 18:00 — 0 commitsSun 19:00 — 0 commitsSun 20:00 — 1 commitsSun 21:00 — 0 commitsSun 22:00 — 0 commitsSun 23:00 — 0 commitsMon 0:00 — 0 commitsMon 1:00 — 0 commitsMon 2:00 — 0 commitsMon 3:00 — 0 commitsMon 4:00 — 0 commitsMon 5:00 — 0 commitsMon 6:00 — 0 commitsMon 7:00 — 0 commitsMon 8:00 — 0 commitsMon 9:00 — 0 commitsMon 10:00 — 0 commitsMon 11:00 — 0 commitsMon 12:00 — 0 commitsMon 13:00 — 0 commitsMon 14:00 — 0 commitsMon 15:00 — 0 commitsMon 16:00 — 0 commitsMon 17:00 — 0 commitsMon 18:00 — 0 commitsMon 19:00 — 0 commitsMon 20:00 — 0 commitsMon 21:00 — 0 commitsMon 22:00 — 0 commitsMon 23:00 — 0 commitsTue 0:00 — 0 commitsTue 1:00 — 0 commitsTue 2:00 — 0 commitsTue 3:00 — 0 commitsTue 4:00 — 0 commitsTue 5:00 — 0 commitsTue 6:00 — 0 commitsTue 7:00 — 0 commitsTue 8:00 — 0 commitsTue 9:00 — 0 commitsTue 10:00 — 0 commitsTue 11:00 — 0 commitsTue 12:00 — 0 commitsTue 13:00 — 0 commitsTue 14:00 — 0 commitsTue 15:00 — 0 commitsTue 16:00 — 0 commitsTue 17:00 — 0 commitsTue 18:00 — 0 commitsTue 19:00 — 0 commitsTue 20:00 — 0 commitsTue 21:00 — 0 commitsTue 22:00 — 0 commitsTue 23:00 — 0 commitsWed 0:00 — 0 commitsWed 1:00 — 0 commitsWed 2:00 — 0 commitsWed 3:00 — 0 commitsWed 4:00 — 0 commitsWed 5:00 — 0 commitsWed 6:00 — 0 commitsWed 7:00 — 0 commitsWed 8:00 — 0 commitsWed 9:00 — 0 commitsWed 10:00 — 0 commitsWed 11:00 — 0 commitsWed 12:00 — 0 commitsWed 13:00 — 0 commitsWed 14:00 — 0 commitsWed 15:00 — 0 commitsWed 16:00 — 1 commitsWed 17:00 — 0 commitsWed 18:00 — 0 commitsWed 19:00 — 0 commitsWed 20:00 — 0 commitsWed 21:00 — 5 commitsWed 22:00 — 2 commitsWed 23:00 — 1 commitsThu 0:00 — 0 commitsThu 1:00 — 0 commitsThu 2:00 — 0 commitsThu 3:00 — 0 commitsThu 4:00 — 0 commitsThu 5:00 — 0 commitsThu 6:00 — 0 commitsThu 7:00 — 0 commitsThu 8:00 — 4 commitsThu 9:00 — 0 commitsThu 10:00 — 0 commitsThu 11:00 — 0 commitsThu 12:00 — 0 commitsThu 13:00 — 0 commitsThu 14:00 — 0 commitsThu 15:00 — 0 commitsThu 16:00 — 0 commitsThu 17:00 — 0 commitsThu 18:00 — 0 commitsThu 19:00 — 2 commitsThu 20:00 — 1 commitsThu 21:00 — 0 commitsThu 22:00 — 0 commitsThu 23:00 — 0 commitsFri 0:00 — 0 commitsFri 1:00 — 0 commitsFri 2:00 — 0 commitsFri 3:00 — 0 commitsFri 4:00 — 0 commitsFri 5:00 — 0 commitsFri 6:00 — 0 commitsFri 7:00 — 0 commitsFri 8:00 — 0 commitsFri 9:00 — 0 commitsFri 10:00 — 0 commitsFri 11:00 — 0 commitsFri 12:00 — 0 commitsFri 13:00 — 0 commitsFri 14:00 — 0 commitsFri 15:00 — 0 commitsFri 16:00 — 1 commitsFri 17:00 — 0 commitsFri 18:00 — 0 commitsFri 19:00 — 0 commitsFri 20:00 — 0 commitsFri 21:00 — 0 commitsFri 22:00 — 0 commitsFri 23:00 — 0 commitsSat 0:00 — 0 commitsSat 1:00 — 0 commitsSat 2:00 — 0 commitsSat 3:00 — 0 commitsSat 4:00 — 0 commitsSat 5:00 — 0 commitsSat 6:00 — 0 commitsSat 7:00 — 0 commitsSat 8:00 — 0 commitsSat 9:00 — 0 commitsSat 10:00 — 0 commitsSat 11:00 — 0 commitsSat 12:00 — 0 commitsSat 13:00 — 0 commitsSat 14:00 — 0 commitsSat 15:00 — 0 commitsSat 16:00 — 1 commitsSat 17:00 — 0 commitsSat 18:00 — 0 commitsSat 19:00 — 0 commitsSat 20:00 — 0 commitsSat 21:00 — 0 commitsSat 22:00 — 0 commitsSat 23:00 — 0 commits
Commit volume by weekday and hour (UTC). Larger dots mean more commits.

Who is committing

last 52 weeks
Maintainer commits19 (100%)
Community commits0 (0%)

19 commits in total over the last year.

DateListRankStars gained
May 7, 2026daily#19+53