What problem does it solve? Android developers need to expose app capabilities to AI agents and system assistants without forcing users to open the app UI, but implementing AppFunctions correctly requires coordinating discovery, Kotlin code generation, KDoc optimization, and device testing across multiple APIs. ## Core Features & Use Cases - Feature Discovery: Analyzes Android codebases (manifests, Activities, Services) to identify high-value user workflows suitable for AppFunctions, such as sending messages or playing media. - Kotlin Implementation: Generates AppFunction code using the @AppFunctionServiceEntryPoint architecture with KSP, Hilt or alternative DI, and proper manifest configuration. - KDoc Optimization: Refines function and serializable documentation so AI agents and MCP clients correctly understand workflows, parameters, and constraints. - ADB Testing: Provides commands to list, invoke, enable, and disable app functions on a connected device for verification. - Use Case: You want users to say "Tell Alice I'm running late" and have your chat app send the message via a system AI agent. This skill discovers the workflow, generates the AppFunction service, optimizes its KDoc, and verifies it with ADB. ## Quick Start Analyze my Android app and implement AppFunctions so AI agents can trigger its core workflows without opening the UI.