Ultimate Library for
Amazon Q Developer

Let Q do a code review of your staged files before committing and pushing your changes.

Commit you changes to git with a meaningful commit message following conventional commit specification.

Generates a drawio architecture diagram to visualize/document the design of your application

Transforms Amazon Q into a critical thinking partner that challenges your ideas instead of simply agreeing. The AI questions your assumptions, offers skeptical viewpoints, checks your reasoning for flaws, suggests alternative perspectives, and prioritizes accuracy over agreement. Perfect for refining arguments, avoiding confirmation bias, and developing more robust thinking.

This prompt guides you through the process of refactoring Java 17 code to improve its quality, readability, and performance. It covers various aspects of code improvement, from basic cleanup to advanced optimization techniques.

Amazon Q Developer operates more quickly, more accurately, and consistently when properly configured with information it can load quickly. This prompt instructs Q to learn from your project and create context-efficient files that give Q just what it needs, with references to load-on-demand details based on the interaction.

This document explains the standardized naming convention for Amazon Q learning files across different projects and directories.

This document outlines the recommended project structure for AWS CDK applications. A well-organized project structure is critical for maintainability, scalability, and developer onboarding. Following these guidelines will ensure consistency across the codebase and make navigation easier as the project grows.

Empowers Amazon Q Developer to maintain Kiro specifications to apply spec-driven developmen the Kiro way.

The project intelligence documentation system transforms Amazon Q Developer from a stateless assistant into a persistent development partner by providing comprehensive context about your application across various sessions. Once the rule was added to your repository, you can ask Q Developer to initialize the project intelligence with "Initialize Project Intelligence". If already in place, you can force an update by asking Q "Update Project Intelligence".

Some rules defining strict ways of working, encouraging specifications to be completed, and a detailed development plan including AI prompts. The main components are enforcing a TDD approach where tests are written first and any test failures means we need to focus on the code (not change the tests).

Encourages a Test-Driven Design approach, with behaviour-like descriptions. The output of the test run should resemble a specification. Tests will be co-located with the actual code files and follow the *.spec.ts naming convention. The final rule is Definition of Done, which prompts the AI assistant to ensure that the code is in a good state with all tests passing and no errors or warnings. Note: Linting examples assume npm lint:fix script exists.

Enable effective collaboration between product teams, engineering teams, and AI agents. The goal is to make all engineers within a team leverage spec-driven development following the standards of Kiro in any IDE.

Maintains the documentation system called Project Intelligence as a living documentation for both human engineers and AI assistants. The goal is to help engineers improve their productivity by providing detailed and up-to-date documentation. Make sure to also use the project-intelligence project rules available via promptz.dev.