System Prompts and Models of AI Tools 🤖

The most comprehensive open-source collection of AI coding assistant system prompts, tool definitions, and behavioral configurations

📊 Repository Statistics

• 📁 32 AI Tools & Platforms covered
• 📄 97 Files of documentation
• 📏 30,000+ Lines of prompts and configurations
• 💾 1.7 MB total content size
• 🔄 Regularly Updated (Latest: October 17, 2025)

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🚀 What's Inside

This repository contains the system prompts, tool definitions, and behavioral configurations that power the world's most advanced AI coding assistants. Whether you're researching AI prompt engineering, building your own AI tools, or just curious about how these systems work, this collection provides unprecedented insight into the architecture of modern AI assistants.

🏢 Commercial AI Coding Assistants

Tool	Description	Files
Cursor	Advanced AI pair programming	Agent prompts v1.0-v1.2, memory system, tools
GitHub Copilot	Microsoft's AI code completion	VSCode integration, multiple model variants
Windsurf	Codeium's agentic coding assistant	Wave 11 prompts & tool definitions
Claude Code	Anthropic's official coding tool	System prompts & tool configurations
v0	Vercel's React/Next.js generator	Complete prompt engineering system
Replit Agent	Browser-based AI development	Full system architecture
Devin AI	Autonomous software engineer	Core behavioral prompts

🌐 Development Platforms & Services

• Lovable - AI-powered full-stack development
• Same.dev - Collaborative AI coding
• Manus Agent - Enterprise development tools
• Augment Code - Code enhancement platform
• Trae AI - AI development workflows
• Notion AI - Document-integrated coding
• Perplexity - Research-augmented development

🔓 Open Source Projects

Project	Type	Description
Cline	VS Code Extension	Local AI assistant
Bolt	Web Platform	Browser-based AI development
RooCode	CLI Tool	Terminal AI assistant
Lumo	Framework	AI coding framework
Codex CLI	Command Line	OpenAI Codex integration
Gemini CLI	Command Line	Google Gemini integration

🏢 Enterprise & Specialized Tools

• Xcode (Apple's AI integration)
• Anthropic (Claude 4 Sonnet configurations)
• Amp (Multi-model orchestration)
• Orchids.app, Junie, Kiro, Warp.dev, Z.ai Code

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� Learning Topics & Course Structure

🎯 Topic 1: Fundamentals of AI Prompt Engineering

Learning Objectives: Understanding the basics of AI system prompts and instruction design

• Subtopics:
  • Introduction to System Prompts vs User Prompts
  • Basic Instruction Formatting and Structure
  • Content Policies and Safety Boundaries
  • Model-Specific Prompt Adaptations
• Practical Examples: Simple chat assistants, basic code completion
• Tools to Study: NotionAI, Perplexity basic prompts

🎯 Topic 2: Tool Integration & Function Calling

Learning Objectives: How AI assistants interact with external systems and APIs

• Subtopics:
  • Tool Schema Definition (JSON specifications)
  • Function Call Orchestration
  • Error Handling and Retry Mechanisms
  • Parallel vs Sequential Tool Execution
• Practical Examples: File operations, terminal commands, web searches
• Tools to Study: Claude Code tools, v0 tool definitions, Replit integrations

🎯 Topic 3: Agentic Behavior & Multi-Step Reasoning

Learning Objectives: Advanced AI systems that can plan and execute complex tasks

• Subtopics:
  • Planning vs Acting Mode Separation
  • Task Decomposition and Subtask Management
  • Memory Systems and Context Persistence
  • Goal-Oriented Behavior Patterns
• Practical Examples: Code refactoring, project setup, debugging workflows
• Tools to Study: Cursor Agent prompts, Windsurf Wave 11, Cline architecture

🎯 Topic 4: Code Generation & Modification Patterns

Learning Objectives: Specialized techniques for programming assistance

• Subtopics:
  • Code Context Understanding and Analysis
  • Diff Generation and Patch Application
  • Code Quality and Best Practices Integration
  • Language-Specific Optimizations
• Practical Examples: React component generation, debugging assistance
• Tools to Study: v0 React generator, GitHub Copilot variants, Lovable systems

🎯 Topic 5: User Experience & Interaction Design

Learning Objectives: Creating intuitive and helpful AI assistant interactions

• Subtopics:
  • Conversational Flow Design
  • Progress Feedback and Status Updates
  • Error Communication and Recovery
  • Personalization and Adaptation
• Practical Examples: IDE integrations, chat interfaces, command-line tools
• Tools to Study: VSCode Agent, Xcode integration, Warp.dev CLI

🎯 Topic 6: Enterprise & Production Considerations

Learning Objectives: Scaling AI assistants for real-world deployment

• Subtopics:
  • Security and Privacy Controls
  • Performance Optimization and Rate Limiting
  • Multi-Model Orchestration
  • Monitoring and Analytics Integration
• Practical Examples: Enterprise deployments, API management
• Tools to Study: Anthropic enterprise configs, Amp multi-model, Manus Agent

🎯 Topic 7: Open Source Implementation Strategies

Learning Objectives: Building and deploying community-driven AI tools

• Subtopics:
  • Local vs Cloud-Based Processing
  • Extension and Plugin Architecture
  • Community Contribution Patterns
  • Licensing and Distribution Models
• Practical Examples: VS Code extensions, CLI tools, web platforms
• Tools to Study: Cline, Bolt, RooCode, Lumo frameworks

🎯 Topic 8: Advanced Research & Future Directions

Learning Objectives: Cutting-edge developments and emerging patterns

• Subtopics:
  • Multi-Modal AI Integration (text, code, images)
  • Autonomous Software Engineering
  • AI-AI Collaboration Patterns
  • Ethical AI and Responsible Development
• Practical Examples: Advanced research prototypes, experimental features
• Tools to Study: Devin AI, latest Anthropic research, emerging platforms

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�🔍 Key Insights & Patterns

Prompt Engineering Evolution

Modern AI assistants have evolved far beyond simple instruction-following:

• Agentic Behavior: Multi-step reasoning and autonomous task execution
• Tool Integration: Sophisticated file system, terminal, and web operations
• Memory Systems: Persistent context and learning capabilities
• Safety Boundaries: Robust content policies and security measures

Common Architecture Patterns

1. Planning vs Acting Modes - Separate reasoning and execution phases
2. Tool Orchestration - Parallel and sequential tool usage patterns
3. Context Management - Advanced chunking and memory strategies
4. Error Recovery - Sophisticated retry and fallback mechanisms

Model-Specific Adaptations

Different prompts optimized for:

• GPT-4/5 series - OpenAI models
• Claude Sonnet-4 - Anthropic's latest
• Gemini Pro - Google's flagship
• Custom Models - Specialized implementations

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🛠️ How to Use This Repository

📖 For Students & Learners

Follow the structured learning path:

# Start with fundamentals
1. Study basic prompts in NotionAI/ and Perplexity/
2. Examine tool definitions in Claude Code/tools.json
3. Analyze agentic behaviors in Cursor Prompts/
4. Practice with open-source examples in Open Source prompts/

Recommended Learning Sequence:

1. Week 1-2: Topics 1-2 (Fundamentals & Tool Integration)
2. Week 3-4: Topics 3-4 (Agentic Behavior & Code Generation)
3. Week 5-6: Topics 5-6 (UX Design & Enterprise)
4. Week 7-8: Topics 7-8 (Open Source & Advanced Research)

🔬 For AI Researchers

# Analyze prompt engineering techniques
grep -r "tool_use" ./*/
grep -r "planning" ./*/
grep -r "memory" ./*/
grep -r "agentic" ./*/

# Study evolution patterns
find . -name "*.txt" -exec grep -l "version\|v1\|v2" {} \;

👩‍💻 For Developers Building AI Tools

Progressive Implementation Guide:

1. Basic Integration: Start with simple tool definitions from Claude Code/
2. Enhanced UX: Study conversational patterns in VSCode Agent/
3. Advanced Features: Implement agentic behaviors from Windsurf/
4. Production Ready: Apply enterprise patterns from Anthropic/

Code Analysis Commands:

# Study tool schemas
find . -name "tools.json" -exec cat {} \;

# Analyze prompt structures  
grep -r "system\|instruction\|role" ./ --include="*.txt"

# Review safety measures
grep -r "policy\|safety\|refuse" ./ --include="*.txt"

🏢 For Product Teams

Competitive Analysis Framework:

• Feature Comparison: Map capabilities across platforms
• UX Pattern Analysis: Study interaction designs
• Technical Architecture: Compare implementation approaches
• Market Positioning: Understand differentiation strategies

Assessment Tools:

• Use provided comparison matrices
• Analyze user flow patterns
• Benchmark performance characteristics

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🧪 Practical Exercises & Assessments

📝 Topic-Based Assignments

Assignment 1: Basic Prompt Analysis (Topics 1-2)

• Task: Compare 3 different basic prompts and identify key patterns
• Files: NotionAi/Prompt.txt, Perplexity/Prompt.txt, Warp.dev/Prompt.txt
• Deliverable: Analysis report comparing instruction styles and safety measures
• Assessment: Understanding of prompt structure fundamentals

Assignment 2: Tool Integration Study (Topics 2-3)

• Task: Create a comprehensive tool mapping across platforms
• Files: All tools.json files in repository
• Deliverable: Tool functionality matrix and integration patterns
• Assessment: Technical comprehension of API design

Assignment 3: Agentic Behavior Implementation (Topics 3-4)

• Task: Design a multi-step coding assistant workflow
• Files: Cursor Prompts/Agent Prompt v1.2.txt, Windsurf/Prompt Wave 11.txt
• Deliverable: Pseudo-code implementation of agentic system
• Assessment: Understanding of complex AI behaviors

Assignment 4: Enterprise Deployment Plan (Topics 5-6)

• Task: Create production deployment strategy
• Files: Anthropic/, Manus Agent Tools & Prompt/
• Deliverable: Technical architecture document
• Assessment: Production-readiness considerations

Final Project: Custom AI Assistant Design (Topics 7-8)

• Task: Design and specify a novel AI coding assistant
• Resources: Entire repository for reference
• Deliverable: Complete system specification including prompts, tools, and UX
• Assessment: Synthesis of all learning objectives

🎯 Learning Outcomes Assessment

Knowledge Check Questions:

1. What are the key differences between system and user prompts?
2. How do modern AI assistants handle tool orchestration?
3. What safety mechanisms are commonly implemented?
4. How do different platforms approach agentic behavior?
5. What are the enterprise considerations for AI assistant deployment?

Practical Skills Evaluation:

• ✅ Prompt engineering and optimization
• ✅ Tool schema design and implementation
• ✅ Safety and content policy development
• ✅ User experience design for AI interactions
• ✅ Technical architecture planning

🏆 Certification Criteria

Basic Level (Topics 1-4):

• Complete 2 assignments with 80%+ accuracy
• Demonstrate understanding of core concepts
• Pass knowledge check questions

Advanced Level (Topics 5-8):

• Complete all assignments including final project
• Show innovation in AI assistant design
• Demonstrate enterprise-level thinking

Expert Level (Research Track):

• Contribute new insights or patterns
• Publish analysis of emerging trends
• Mentor other learners in the community

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📚 Documentation Structure

├── Commercial Tools/
│   ├── Cursor Prompts/           # Advanced pair programming
│   ├── VSCode Agent/             # GitHub Copilot variants  
│   ├── Windsurf/                 # Latest agentic prompts
│   ├── Claude Code/              # Anthropic's system
│   └── v0 Prompts and Tools/     # Vercel's generator
├── Open Source/
│   ├── Cline/                    # VS Code extension
│   ├── Bolt/                     # Web platform
│   └── [Others]/                 # Various OSS tools
├── Enterprise/
│   ├── Anthropic/                # Advanced Claude configs
│   ├── Xcode/                    # Apple integration
│   └── [Specialized]/            # Domain-specific tools
└── Documentation/
    ├── README.md                 # This file
    └── [Analysis Files]/         # Research insights

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🔬 Research Applications

Academic Research

• Prompt Engineering Studies - Evolution of AI instruction methods
• Human-AI Interaction - UX patterns in coding assistants
• AI Safety Research - Content policy and boundary analysis
• Tool Use Studies - Multi-modal AI capability research

Industry Applications

• Competitive Intelligence - Feature and capability analysis
• Product Development - Best practice identification
• Risk Assessment - Security and safety pattern analysis
• Training Data - Prompt engineering examples

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🛡️ Security & Ethics

Responsible Use

• Research Only: This collection is for educational and research purposes
• No Malicious Use: Do not use for creating harmful AI systems
• Attribution: Credit original creators when building upon this work
• Privacy Respect: No personal data or credentials included

Content Policies

All included prompts maintain:

• ✅ Content safety boundaries
• ✅ Copyright compliance
• ✅ Security best practices
• ✅ Ethical AI guidelines

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📈 Recent Updates

October 2025

• ✨ Added Windsurf Wave 11 prompts
• 🔄 Updated Cursor v1.2 configurations
• 📊 Enhanced VSCode Agent variants
• 🆕 New open-source tool additions

Contributing

We welcome contributions! Please:

1. Verify Authenticity - Ensure prompts are from official sources
2. Maintain Quality - Include proper documentation
3. Respect Licenses - Follow original terms of use
4. Update Index - Add new tools to this README

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🌟 Star History

⭐ Star this repository to stay updated with the latest AI assistant developments!

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📞 Connect & Support

• 🐛 Issues: Report problems or suggest improvements
• 💬 Discussions: Join the community conversation
• 🔄 Updates: Watch this repo for the latest additions
• 🎯 Requests: Suggest new AI tools to document

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📜 License

This project is licensed under the MIT License - see the LICENSE file for details.

Note: Individual prompts and configurations may have their own licenses. Please respect the original creators' terms.

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👨‍💻 Course Developer & Credits

Powered by: Puran Bahadur Thapa
Website: https://eastlink.com.np
WhatsApp: +9779801901140

For technical support, web development consulting, or custom PHP training programs, feel free to reach out through the contact information above. Professional development services and advanced training modules available.

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Course Version: 1.0.0
Last Updated: October 2025
Developed by: Puran Bahadur Thapa
Maintained by: Computer Science Department

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