Code review, debugging, architecture, testing and docs. 9 reviewed prompts for engineering.
Reviews the structure of a web API project and lists strengths, weaknesses and ranked improvements for a large user base. Use before scaling a service or planning a refactor.
Designs a system from requirements to failure modes: clarifying questions, capacity estimates, components, data model and explicit trade-offs. Use it for real architecture decisions or to practise design interviews.
Reviews how an application is organized and proposes a layered structure with small, testable steps. Use it when code has grown tangled and hard to change.
Drafts a data platform architecture for batch and real-time analytics, machine learning, and governance, with options, trade-offs, cost drivers, and a phased plan. Use when designing or modernising a company's data stack.
Turns a business problem and the data you have into a practical production ML design: metrics, data risks, model options, rollout and monitoring. Use it before committing a team to an ML project.
Reviews backend service code for layering, SOLID violations, API design and performance risks, and proposes small concrete refactors. Use it before merging a feature or when a codebase becomes hard to change.
Recommends the simplest workable approach for an AI-powered feature (better prompts, retrieval over your documents, fine-tuning or a mix) with trade-offs, an evaluation plan and cost notes. Use at the start of an AI project.
Produces a cloud architecture proposal for a workload with components, trade-offs, cost drivers, security and reliability notes, and a migration path. Use it when designing a new system or reviewing an existing setup.
Designs a reliable connection between two systems: data flow, pattern choice, authentication, retries, duplicate protection, monitoring and a build plan. Use when planning a new integration or fixing a fragile one.
