Skill UI

This comprehensive workflow guides the entire lifecycle of building a complex full-stack feature, from initial architectural design to final integration testing. It enforces an API-first approach, ensuring that the database schema, backend services, and frontend components are developed cohesively, promoting scalability, testability, and adherence to modern industry best practices.

This comprehensive workflow guides the process of building and deploying new features that must maintain consistent functionality and design across multiple operating systems (web, mobile, and desktop). It strictly follows an API-first architecture, starting with defining shared API contracts (OpenAPI/GraphQL) and a unified design system. The process then guides parallel implementation across specialized stacks like React/Next.js, SwiftUI, and Jetpack Compose, ensuring high code consistency and faster time-to-market.

Enforces an 80%+ coverage TDD workflow for feature builds, bug fixes, refactors, and API/component work by writing unit, integration, and Playwright E2E tests first, then implementing code, verifying coverage, and mocking downstream services.

Guides developers through strict test-driven development: write failing tests first, iterate red-green-refactor, and keep tests passing before refactoring to ensure reliable features.

This skill focuses on implementing advanced TypeScript features to build robust, type-safe applications. It covers designing type-first APIs, utilizing generics, implementing custom type guards, branded types, and managing complex state with discriminated unions. It is essential for full-stack development, monorepo setups, and projects requiring maximum type integrity (e.g., integrating with tRPC).

Detailed guidance for a strict test-driven development cycle: write a failing test first, confirm the failure, add minimal code to pass, refactor cleanly, and repeat so every feature, fix, or refactor ships with confidence.

This skill provides expert guidance on treating prompts as first-class infrastructure. It covers prompt registry design, systematic versioning, A/B testing, and building comprehensive evaluation pipelines. It ensures that prompt changes are rigorously tested and deployed safely, preventing quality regressions in production AI features at scale.

Utilize Modal, a serverless GPU cloud, for running complex machine learning workloads, including training, fine-tuning, and inference. This tool provides a zero-config environment, eliminating the need for SSH setup or Docker management. It features automatic scale-to-zero billing and local-first execution, making high-powered GPU compute accessible for debugging and small-scale experiments.

This skill applies principles of behavioral psychology to redesign user onboarding flows. Instead of simple feature tours, it focuses on engineering the first-use experience to achieve 'early wins,' build competence, establish ownership, and trigger habit formation. Use it when the goal is to reduce user friction, combat early drop-off, and shift the user's self-perception from an outsider to an active participant.

Defines layered architecture with feature-first folder organization for Flutter apps. Guides design of UI, data, and optional logic layers, component responsibilities, repository as single source of truth, and dependency injection.

A comprehensive methodology guiding technical professionals through the design process before coding. It helps structure complex features by producing detailed design documents, including system overviews, data models, API specifications, multiple trade-off analyses, and implementation plans, ensuring robustness and clarity from the start.

Automates creating a single GitHub feature issue from a specification file, checking existing issues first, filling title/description/labels per feature_request.yml, and including only spec-mandated changes.