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.

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

• 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

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

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

• 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

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.