Explorar skills

Clinical safety code review against DCB0129/DCB0160 and NHS Digital standards. This is NOT a general code review — it is a clinical safety audit for tools handling clinical data, calculations, decision support, or patient-facing information. Produces a hazard register, required mitigations, and a release recommendation.

Ship a CQAI tool to GitHub and Hugging Face Spaces in a single workflow. Runs pre-flight checks, commits and pushes to GitHub, pushes to HF Spaces, waits for build, and verifies the app is live. Use after /qa-streamlit passes.

Rules for writing and updating documentation. Use for any non-trivial documentation task in README.md, markdown files, text files, and code comments.

Best practices for Go project Makefiles. Use when creating or modifying a Go project Makefile: adding/reviewing targets, PHONY declarations, tab indentation, docker compose v2 targets.

Comprehensive testing specialist for all levels and types. Use when writing unit, integration, E2E, performance, or security tests; creating test strategies and plans; analyzing test coverage; building automation frameworks; managing defects; debugging test failures; manual testing (exploratory, usability, accessibility); scaling CI/CD test pipelines.

Writing rules for clear, powerful, incarnated blog posts.

TypeScript and Angular coding best practices. Use when working with TypeScript, Angular, or Node.js files: implementation, testing, refactoring, RxJS Observables, type system, strict mode, generics, and async patterns.

Build a complete product experiment plan from a validated hypothesis, metrics, and testing assumptions. Use this skill when the user is ready to prepare an A/B test or structured product experiment for execution.

Go coding best practices and patterns. Use when working with Go or Golang files: implementation, testing, refactoring, architectural review, goroutines, channels, interfaces, error handling, memory management, and API development.

Evaluate AI model capabilities, limitations, and trade-offs relevant to a product decision. Use this skill when a team needs to understand what an AI model can and cannot reliably do for their use case.

Define the success metrics for an AI feature covering quality, user experience, business impact, and safety. Use this skill when an AI feature needs a complete metric framework before launch.

Design workflows that appropriately balance AI automation with human oversight and intervention. Use this skill when a team is building an AI feature and needs to decide when and how humans stay in the loop.