36 lines
1.5 KiB
Markdown
36 lines
1.5 KiB
Markdown
Analyzes Android codebases to identify and recommend high-value AppFunctions.
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## Instructions
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### Workflow: Feature Discovery
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1. **Analyze Manifest \& Entry Points** : Scan `AndroidManifest.xml` and Activity, Fragment, Service classes to identify core user journeys (e.g., Search, Create, Share).
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2. **Identify Atomic Tasks**: Look for methods or logic that represent distinct, self-contained user outcomes.
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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").
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4. **Recommend \& Justify**: List recommendations with a "Rationale" focusing on how an AI assistant adds value (efficiency, hands-free use, or multi-step automation).
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## Critical Constraints
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### Tool-First Thinking
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Avoid recommending functions that are purely informational or redundant with
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existing system actions. Focus on "mutations" (writing data) or "rich queries"
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(finding specific entities).
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### Security \& Privacy
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Don't recommend exposing functions that handle raw credentials, financial
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secrets, or irreversible destructive actions without explicit user confirmation
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steps.
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## Examples
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### Example 1: Media App Discovery
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**Recommended AppFunction:** `playArtistRadio`
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**Rationale:** Allows users to start a personalized music stream using a
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voice command, bypassing several layers of navigation in the "Search" and
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"Artist" menus.
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**Input Required:** Artist Name (String). |