Stitch Design Taste

Use this skill whenever the user wants to work with the Loops CLI from the terminal. This includes installing or updating the CLI, authenticating, storing and selecting API keys, validating credentials, and running commands for contacts, contact properties, lists, events, transactional email, campaigns, email messages, themes, components, and uploads. Trigger on phrases like "Loops CLI", "loops auth login", "loops campaigns create", "loops uploads create", "loops email-messages update", "loops themes list", "loops components get", "loops contacts create", "loops events send", "loops transactional send", "loops api-key", "loops agent-context", "brew install loops-so/tap/loops", or any time the user wants to use Loops from the shell instead of application code.

Guides the agent through installing, configuring, and using Capacitor plugins from six sources — official Capacitor plugins, Capawesome plugins, Capacitor Community plugins, Capacitor Firebase plugins, Capacitor MLKit plugins, and RevenueCat plugins. Covers installation, platform-specific configuration (Android and iOS), and basic usage examples. Do not use for migrating Capacitor apps or plugins to a newer version, setting up Capacitor Live Updates, or non-Capacitor mobile frameworks.

Generate a personalized SOUL.md through a warm, adaptive onboarding conversation. Trigger when the user wants to create, set up, or initialize their AI partner's identity — e.g., "create my SOUL.md", "bootstrap my agent", "set up my AI partner", "define who you are", "let's do onboarding", "personalize this AI", "make you mine", or when a SOUL.md is missing. Also trigger for updates: "update my SOUL.md", "change my AI's personality", "tweak the soul".

Worker-Agent integration for intelligent task dispatch and performance tracking

Expert prompt engineering for Seedance 2.0. Use when the user wants to generate a video with multimodal assets (images, videos, audio) and needs the best possible prompt.

The method for finding the gap between what a system is supposed to do and what the code actually does — the class of bug generic scanners miss because they have no model of intent. Defines what counts as documented intent, what counts as implementation evidence, which mismatches matter, and how to avoid hand-wavy findings. Use when auditing AI-built code, reviewing access control against documented permissions, or checking whether a codebase matches its own documentation.