Desenvolvimento

UAT stage — execute Playwright tests and mechanical backpressure against the running app

Use when writing code in typed languages - enforces full typing with no any/unknown/untyped escapes, even if it requires extra time

Ajuda usuários a encontrar ideias de produtos Sui de um corpus selecionado ou a refinar as suas próprias. Ativa-se quando o usuário busca ideias, produtos ou projetos para desenvolver na Sui, cobrindo diversas solicitações como 'brainstorm' ou 'o que devo construir', focando apenas na fase de ideia.

Use for LARGE work requiring feature-level grouping. Creates epic tracking issues, manages related issues under a common label, tracks epic progress, and coordinates with milestones.

Use after code review - implement ALL findings; any finding not fixed MUST have tracking issue created; no finding disappears without trace

Deep-validate a single GitHub issue against the codebase: cross-reference file paths, detect already-implemented features, check for related PRs, verify dependencies, and deliver a structured verdict with recommended action. Triggers: review issue, validate issue, check issue status, is this issue still needed, issue review.

Planeje um vídeo de marketing de 30-60 segundos para um produto Sui (Twitter, Shorts, página de lançamento). Use quando o usuário desejar um vídeo de marketing, promoção ou lançamento.

Use to spawn isolated worker processes for autonomous issue work. Uses Task tool with run_in_background for parallel execution and TaskOutput for monitoring. Pre-extracts context to minimize worker token usage.

Project manager for GitHub issues: brainstorm design approaches, create structured issues optimized for LLM agent teams, triage and recommend what to work on next, audit and clean up stale issues, or deep-validate a single issue against the codebase. Triggers: create issue, plan work, new task, project manager, write ticket, draft issue, plan feature, plan project, start project, create ticket, re

Pragmatic coding standards for writing clean, maintainable code — naming, functions, structure, anti-patterns, and pre-edit safety checks. Use when writing new code, refactoring existing code, reviewing code quality, or establishing coding standards.

Performance analysis: bundle size checks, profiling, benchmarks, and algorithmic complexity review. Identifies performance-sensitive areas and creates a structured GitHub issue.

Verify project dependencies against VCP dependency management standards. Checks lockfile hygiene, version ranges, package existence, and suspicious packages.