Problem Statement Builder
This builder helps you frame your problem statements accurately, ensuring you apply the right level of rigor and structure for your specific use case.
Describe the user pain, bottleneck, or inefficiency you are addressing.
Why does this problem persist? How is it solved today (e.g., spreadsheets, paper)?
Be explicit about who it is for, what is included, and what is OUT of scope.
Select the format/technology stack for the build.
How will the user's life improve? Quantify the outcome if possible.
Case Studies & Resources
Deep dive into real-world vibe coding implementations and prompt engineering resources.
Vibe Coding Examples
Collection of vibe coding examples and best practices for rapid prototyping with AI.
GitHub TopicPrompt Engineering Guide
Comprehensive guide to prompt engineering techniques and patterns for better AI interactions.
GitHub RepositoryAI Agents with Claude
Examples of AI agents and workflows built using Claude's capabilities and tools.
Anthropic CookbookInteractive Tools
Practical tools to help you think systematically, build better AI agents, and master prompt engineering.
Problem Solver
A structured thinking space to go from raw problem → root cause → ideas → architecture decisions → concrete build plan.
AI Agent Skills Builder
Design custom AI agent skills based on best practices from Claude, Gemini, OpenAI, and GitHub Copilot.
Prompt Engineering
An interactive workspace to learn and test professional prompt engineering structures.
Enterprise Pattern
Design and visualize cognitive patterns for enterprise AI agents.
Trip Planner
Design a 6-phase, multi-agent AI pipeline for planning a comprehensive family trip.
Software Engineering Playbooks
Practical, End-to-End implementation guides for building Production-ready Software. Each playbook includes working code, architecture diagrams, and step-by-step instructions.
Research Agent with Gateway
Build a production-ready AI research agent using Agent Gateway for unified traffic management, authentication, and observability across LLM providers.
RAG Pipeline with Vector Database
Implement a complete Retrieval-Augmented Generation pipeline with vector embeddings, semantic search, and context injection for accurate AI responses.
Multi-Agent Orchestration
Create a coordinated multi-agent system with specialized agents, task distribution, and result synthesis for complex problem-solving.
Future References
Explore these resources for deeper learning on AI agent development, spec-driven development, and prompt engineering tools.
Spec-Driven Development
Comprehensive guide on Spec-Driven Development practices and methodologies.
Awesome Copilot
Curated list of GitHub Copilot resources, extensions, and best practices.
Promptfoo
Tool for testing, evaluating, and improving LLM prompts and applications.
Prompts.chat
Collection of prompt engineering resources and templates.
Agent Skills
Agent skills resources and documentation for building AI agent skills.
Awesome Skills
Curated list of awesome skill repositories and collections.
Agent Skills Topic
GitHub topic for discovering agent-related skills and repositories.
AI Agent Topic
Trendshift topic for discovering AI agents.
AI Skills Topic
Trendshift topic for discovering AI skills.
Agent Governance Toolkit
Agent governance toolkit.
Pattern Sources
Our patterns are curated from industry-leading sources with proper attribution and licensing compliance.
Refactoring.Guru
Classic GoF design patterns, code smells catalog, and refactoring techniques (https://refactoring.guru).
Enterprise Integration Patterns
65 messaging patterns for integrating enterprise applications by Gregor Hohpe and Bobby Woolf (CC BY 4.0).
Microservices.io
Comprehensive patterns for microservice architectures by Chris Richardson.
Agent Catalog Patterns
Patterns for agentic systems from agentpatternscatalog.org (CC BY 4.0).
OWASP Foundation
Security patterns from OWASP Top 10 for Web Applications, LLM Applications, and Agentic Applications (CC BY-SA 4.0).
Industry Research
ML/AI patterns from Microsoft, Google, Anthropic, and academic research.
AI Agent Patterns
Spec-driven development patterns from Claude, Gemini, OpenAI, and GitHub Copilot on github/spec-kit and OpenSpec.
Data Engineering Leaders
Data platform patterns from Martin Fowler (Data Mesh), Kimball Group (Dimensional Modeling), and cloud providers.
MLOps Best Practices
Data science patterns from MLflow, Great Expectations, and MLOps practitioners.
Streaming & Analytics
Real-time patterns from Confluent/Kafka, Apache projects, and serverless analytics platforms.
Academic Papers
Rigorous ML patterns from peer-reviewed research including data leakage prevention and active learning.
5-Day AI Agents Course
Intensive Vibe Coding Course With Google by Brenda Flynn et al. (2026) on Kaggle.
The Agent Loop
Foundational Agent Definition (Perceive + Act):
Russell, S. J., & Norvig, P. (1995). Artificial Intelligence: A Modern Approach. Prentice Hall. (Current edition: 4th Ed., Pearson, 2020)
Modern Iterative LLM Agent Loop:
Yao, S., Zhao, J., Yu, D., et al. (2022). ReAct: Synergizing Reasoning and Acting in Language Models. arXiv:2210.03629.
Historical Context:
Incorporating AIMA's perceive/act model, Classical robotics' Sense-Plan-Act loop (Brooks, 1986), and ReAct's Thought→Action→Observation cycle.