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29 production-ready scripts for iOS app testing, building, and automation. Provides semantic UI navigation, build automation, accessibility testing, and simulator lifecycle management. Optimized for AI agents with minimal token output.

Uses Chrome DevTools MCP for accessibility (a11y) debugging and auditing based on web.dev guidelines. Use when testing semantic HTML, ARIA labels, focus states, keyboard navigation, tap targets, and color contrast.

Guide for feature flags and A/B tests with the Flags SDK (`flags` npm package) and Vercel Flags. Use when: declaring flags with `flag()`, using `vercelAdapter` or `vercel flags` CLI (add, list, enable, disable, inspect, archive, rm, sdk-keys), setting up providers/adapters (Vercel, Statsig, LaunchDarkly, PostHog, GrowthBook, Hypertune, Global Config, OpenFeature, Split, Flagsmith, Reflag, Optimizely, or custom adapters), implementing precompute patterns for static pages, setting up `identify`/`dedupe`, integrating Flags Explorer/Toolbar, working with flags in Next.js (App Router, Pages Router, Middleware) or SvelteKit, writing custom adapters, or encrypting/decrypting flag values. Triggers: feature flags, A/B testing, experimentation, flags SDK, flag adapters, precompute, Flags Explorer, feature gates, flag overrides, Vercel Flags, vercel flags CLI, vercel flags add, vercel flags list, vercel flags enable, vercel flags disable, `flags/next`, `flags/sveltekit`, `flags/react`, `@flags-sd

Build robust backend systems with modern technologies (Node.js, Python, Go, Rust), frameworks (NestJS, FastAPI, Django), databases (PostgreSQL, MongoDB, Redis), APIs (REST, GraphQL, gRPC), authentication (OAuth 2.1, JWT), testing strategies, security best practices (OWASP Top 10), performance optimization, scalability patterns (microservices, caching, sharding), DevOps practices (Docker, Kubernetes, CI/CD), and monitoring. Use when designing APIs, implementing authentication, optimizing database queries, setting up CI/CD pipelines, handling security vulnerabilities, building microservices, or developing production-ready backend systems.

DeepEval evaluation workflow for AI agents and LLM applications. TRIGGER when the user wants to evaluate or improve an AI agent, tool-using workflow, multi-turn chatbot, RAG pipeline, or LLM app; add evals; generate datasets or goldens; use deepeval generate; use deepeval test run; send results to Confident AI; monitor production; run online evals; inspect traces; or iterate on prompts, tools, retrieval, or agent behavior from eval failures. AI agents are the primary use case. Covers Python SDK, pytest eval suites, CLI generation, traced evals, Confident AI reporting, and agent-driven improvement loops. DO NOT TRIGGER for unrelated generic pytest, non-AI test setup, or non-DeepEval observability work unless the user asks to compare or migrate to DeepEval; for instrumenting an app with DeepEval tracing, @observe, or framework integrations (use the `deepeval-tracing` skill); or for raw OpenTelemetry / OTLP export without the deepeval package (use the `deepeval-otel` skill).

Bun as runtime, package manager, bundler, and test runner. When to choose Bun vs Node, migration notes, and Vercel support.