What problem does it solve? Android developers need to expose app features to system AI agents, voice commands, and shortcuts without requiring users to open the app UI, but implementing AppFunctions correctly involves KSP setup, metadata XML, KDoc conventions, and ADB testing that are easy to get wrong. ## Core Features & Use Cases - Feature Discovery: Analyzes Android codebases (manifests, activities, services) to identify high-value workflows worth exposing as AppFunctions. - Implementation & Configuration: Generates Kotlin code with @AppFunction annotations, KSP/Gradle setup, app_metadata.xml, and optional Hilt dependency injection. - KDoc Refinement: Optimizes function and serializable documentation so LLM agents and MCP clients correctly understand and invoke the functions. - ADB Testing: Provides commands to list, invoke, enable, and disable AppFunctions on a connected device for verification. - Use Case: A notes app developer asks the assistant to expose note creation to Gemini; the skill discovers the workflow, generates createNote/listNotes AppFunctions with proper KDoc, and verifies registration via adb shell cmd app_function. ## Quick Start Analyze my Android app and implement AppFunctions for its core user workflows, then help me test them with ADB.