HaoNgo232

Analyzes user's requests, determines tech stack, plans structure, and coordinates agents.

Apply consistent changes across many files at once. One pattern, many targets.

**MANDATORY:** Use for complex/vague requests, new features, updates.

Reduce AI token usage by **6.8x average** (up to **49x** on monorepos) by giving the AI a structural map of your codebase instead of letting it read everything.

Keep sessions productive by compressing completed work while preserving key decisions.

Advanced multi-agent coordination with parallel dispatch and synthesis. Use for complex tasks requiring multiple specialist perspectives.

Distilled from production-proven coordinator patterns. Transforms sequential agent chains into intelligent parallel orchestration.

**Philosophy:** Every pixel has purpose. Restraint is luxury. User psychology drives decisions.

API design principles and decision-making. REST vs GraphQL vs tRPC selection, response formats, versioning, pagination.

Main application building orchestrator. Creates full-stack applications from natural language requests. Determines project type, selects tech stack, coordinates agents.

Pragmatic coding standards - concise, direct, no over-engineering, no unnecessary comments

Design thinking and decision-making for web UI. Use when designing components, layouts, color schemes, typography, or creating aesthetic interfaces. Teaches principles, not fixed values.

Game development orchestrator. Routes to platform-specific skills based on project needs.

Generative Engine Optimization for AI search engines (ChatGPT, Claude, Perplexity).

Automatic quality control, linting, and static analysis procedures. Use after every code modification to ensure syntax correctness and project standards. Triggers onKeywords: lint, format, check, validate, types, static analysis.

React and Next.js performance optimization from Vercel Engineering. Use when building React components, optimizing performance, eliminating waterfalls, reducing bundle size, reviewing code for performance issues, or implementing server/client-side optimizations.

Gather relevant source files for a task, resolve their dependencies, and package everything into a structured context prompt.

Produce an architectural design and implementation plan based on task requirements, identifying scope, dependencies, impact, and a step-by-step rollout strategy.

Explore the codebase and export a structured context file (e.g., context.xml) for pasting into an external LLM like ChatGPT or Claude Web.

Trace execution paths from an error or bug report to find the root cause, gathering surrounding context (callers, imports, tests) along the way.

Analyze dependencies, callers, and coupling before refactoring, then produce a safe incremental refactoring plan.

Review recent code changes by gathering git diffs, identifying affected callers and tests, and analyzing for security, performance, and breaking changes.

Identify untested functions by comparing source symbols with test symbols, then prepare context for writing missing tests.

Database design principles and decision-making. Schema design, indexing strategy, ORM selection, serverless databases.