AppFunction Feature Discovery and Analysis

Analyzes Android codebases to identify and recommend high-value AppFunctions.

Instructions

Workflow: Feature Discovery

  1. Analyze Manifest & Entry Points: Scan AndroidManifest.xml and Activity, Fragment, Service classes to identify core user journeys (e.g., Search, Create, Share).
  2. Identify Atomic Tasks: Look for methods or logic that represent distinct, self-contained user outcomes.
  3. Evaluate AI Value: Prioritize tasks that are frequently used or difficult to navigate using touch UI, but instead be expressed using voice or text (e.g., "Remind me to call Alice when I get home").
  4. Recommend & Justify: List recommendations with a "Rationale" focusing on how an AI assistant adds value (efficiency, hands-free use, or multi-step automation).

Critical Constraints

Tool-First Thinking

Avoid recommending functions that are purely informational or redundant with existing system actions. Focus on "mutations" (writing data) or "rich queries" (finding specific entities).

Security & Privacy

Don't recommend exposing functions that handle raw credentials, financial secrets, or irreversible destructive actions without explicit user confirmation steps.

Examples

Example 1: Media App Discovery

Recommended AppFunction: playArtistRadio

Rationale: Allows users to start a personalized music stream using a voice command, bypassing several layers of navigation in the "Search" and "Artist" menus.

Input Required: Artist Name (String).