# Prompt-Library: Open-Source Repository of High-Quality Prompts and AI Workflows

> A community-driven open-source project dedicated to collecting, organizing, and benchmarking high-quality AI prompts across multiple large language models, covering fields such as programming, research, writing, business, and image generation.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-06-10T18:14:50.000Z
- 最近活动: 2026-06-10T18:24:18.191Z
- 热度: 150.8
- 关键词: 提示工程, Prompt Engineering, ChatGPT, Claude, 大语言模型, 开源项目, AI工作流, 提示词库
- 页面链接: https://www.zingnex.cn/en/forum/thread/prompt-library-ai-6f21bac2
- Canonical: https://www.zingnex.cn/forum/thread/prompt-library-ai-6f21bac2
- Markdown 来源: floors_fallback

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## [Introduction] Prompt-Library: Open-Source Repository of High-Quality Prompts and AI Workflows

A community-driven open-source project dedicated to collecting, organizing, and benchmarking high-quality AI prompts across multiple large language models, covering fields such as programming, research, writing, business, and image generation. The original author/maintainer is sharjeelx03, released on GitHub (link: https://github.com/sharjeelx03/Prompt-Library) under the MIT License, with a release date of 2026-06-10. The project not only provides ready-to-use prompts but also aims to teach the principles and methods of prompt engineering to help users improve their AI usage efficiency.

## Project Background and Vision

With the popularity of large language models (LLMs) like ChatGPT, Claude, and Gemini, prompt engineering has become a critical skill. However, many users find that even with the most advanced models, the output quality is significantly compromised if prompts are poorly designed. Prompt-Library emerged as a free, open-source collection of high-quality prompts, aiming to provide developers, researchers, students, marketers, and entrepreneurs with battle-tested AI prompts and workflows—offering both the 'fish' (ready-to-use prompts) and the 'fishing method' (principles of prompt engineering).

## Core Features and Classification System

### Core Features
- **Free and Open-Source**: MIT License, completely free to use, modify, and distribute.
- **Multi-Model Compatibility**: Cross-model tested, supporting ChatGPT (GPT-4o, GPT-4), Claude (Sonnet, Opus), Gemini (Pro, Ultra), DeepSeek, Grok, Mistral/LLaMA, etc.
- **Structured and Searchable**: Categorized by domain, use case, and technology for quick location.
- **Community-Driven**: Co-built by AI practitioners, continuously updated.
- **Prompt Engineering Guide**: In-depth explanation of principles to help users advance from using to designing prompts.
- **Real Workflows**: Provides end-to-end multi-step workflows to solve complex tasks.

### Classification System
#### By Domain
| Category | Description | Example Scenarios |
|----------|-------------|-------------------|
| coding/ | Programming-related | Debugging, code review, architecture design, refactoring |
| research/ | Academic research | Literature review, abstract generation, data analysis |
| writing/ | Writing and creation | Blog posts, papers, copywriting, story creation |
| education/ | Education and training | Lesson plans, study guides, Socratic teaching |
| business/ | Business applications | Strategic planning, marketing, sales, pitch materials |
| productivity/ | Productivity improvement | Task planning, decision-making, time management |
| robotics/ | Robotics technology | ROS, motion planning, sensor fusion, simulation |
| image-generation/ | Image generation | Prompts for Midjourney, DALL·E, Stable Diffusion |
| video-generation/ | Video generation | Prompts and techniques for Sora, Runway, Kling |

#### By Technology
- Zero-shot and few-shot prompts
- Chain-of-Thought (CoT)
- Role prompting
- Structured output
- Task decomposition
- Context window management
- Iterative prompt optimization
- Retrieval-Augmented Generation (RAG) prompts
- Agent prompt patterns

## Workflow Examples and Benchmarking

### Workflow Examples
The project provides multiple end-to-end workflows:
1. **Research Paper Workflow**: Topic exploration → Literature retrieval → Outline construction → Section writing → Polishing and refinement
2. **Startup Validation Workflow**: Idea generation → Market research → Business model → Pitch materials
3. **Content Creation Workflow**: Requirement analysis → Outline generation → First draft writing → Editing and optimization
4. **Learning Workflow**: Knowledge map → Step-by-step learning → Interactive exercises → Summary and consolidation

### Benchmarking
The project offers cross-model benchmarking to help users choose the right model:
#### Testing Dimensions
- Output quality (accuracy, creativity, coherence)
- Compliance (degree of following instructions)
- Format correctness (accuracy of structured output)
- Reasoning depth (ability to analyze complex problems)
- Response speed

#### Compared Models
Covers OpenAI series, Anthropic Claude series, Google Gemini series, DeepSeek, xAI Grok, Mistral/LLaMA open-source models, etc.

## Contribution Guidelines and Roadmap

### Contribution Guidelines
Various contributions are welcome:
1. Add new prompts
2. Optimize existing prompts
3. Share workflows
4. Write guides
5. Conduct benchmarking
6. Report issues

Quality control requirements:
- Prompts must be tested and effective
- Provide usage scenarios and expected output examples
- Follow classification and formatting standards
- Workflows must explain the purpose of each step and connection methods

### Roadmap
| Phase | Status | Goal |
|-------|--------|------|
| Phase1 | ✅ Completed | Repository setup, structure definition, documentation improvement |
| Phase2 | 🔄 In progress | Core prompt collection, covering all categories |
| Phase3 | 📅 Planned | Searchable website + prompt rating system |
| Phase4 | 📅 Planned | Benchmarking + case studies |
| Phase5 | 📅 Planned | Global community + industry-standard resource |

## Target Audience and Usage Recommendations

### Target Audience
- **Developers**: Build AI applications, optimize code workflows, learn prompt engineering
- **Researchers**: Assist academic research, literature reviews, data analysis
- **Students**: Learn prompt engineering, improve writing and learning efficiency
- **Marketers and content creators**: Accelerate content production, enhance copy quality
- **Entrepreneurs**: Formulate business strategies, generate pitch materials, conduct market research
- **Robotics and engineering teams**: Apply LLMs to technical tasks (e.g., ROS)

### Usage Recommendations
#### Beginner Path
1. Browse categories → 2. Try prompts →3. Understand principles →4. Iterate and optimize →5. Contribute feedback

#### Advanced Usage
1. Combine workflows →2. Model selection →3. Customize frameworks →4. Participate in the community

## Conclusion and Community Invitation

Prompt-Library is not just a collection of prompts but a systematic organization of prompt engineering knowledge. In today's rapidly evolving AI landscape, prompt engineering has become a core skill. The project's open-source nature allows it to grow and enrich with the community. For those looking to improve their AI usage efficiency, it is a resource worth bookmarking and participating in.

If you benefit from it, feel free to star the project or contribute your prompt experience, so that the repository can benefit more people.