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2 changes: 2 additions & 0 deletions .claude-plugin/marketplace.json
Original file line number Diff line number Diff line change
Expand Up @@ -17,6 +17,7 @@
"./build-system/agp/agp-9-upgrade",
"./camera/camerax",
"./device-ai/appfunctions",
"./device-ai/ml-kit-genai-prompt-api",
"./devtools/android-cli",
"./identity/restore-credentials",
"./identity/verified-email",
Expand All @@ -25,6 +26,7 @@
"./jetpack-compose/theming/styles",
"./media/media3-cast-integration",
"./navigation/navigation-3",
"./navigation/navigation-event",
"./performance/r8-analyzer",
"./play/engage-sdk-integration",
"./play/play-billing-library-version-upgrade",
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10 changes: 10 additions & 0 deletions .codex-plugin/plugin.json
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Expand Up @@ -30,6 +30,11 @@
"path": "./device-ai/appfunctions"
}
},
{
"source": {
"path": "./device-ai/ml-kit-genai-prompt-api"
}
},
{
"source": {
"path": "./devtools/android-cli"
Expand Down Expand Up @@ -70,6 +75,11 @@
"path": "./navigation/navigation-3"
}
},
{
"source": {
"path": "./navigation/navigation-event"
}
},
{
"source": {
"path": "./performance/r8-analyzer"
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121 changes: 121 additions & 0 deletions device-ai/ml-kit-genai-prompt-api/SKILL.md
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@@ -0,0 +1,121 @@
---
name: ml-kit-genai-prompt-api
description: Analyzes Android codebases to implement ML Kit GenAI Prompt API. Use
this skill to send natural language requests on-device to Gemini Nano, use structured
output with Prompt API, implement prefix caching, optimize the current prompt, or
apply best practices."
license: Complete terms in LICENSE.txt
metadata:
author: Google LLC
last-updated: '2026-09-03'
keywords:
- ML Kit
- Prompt API
- Structured Output
- Prefix Caching
- Gemini Nano
---

This skill provides step-by-step guidance for integrating and optimizing the ML
Kit GenAI Prompt API in Android apps.

## Prerequisites

- Android API level must be 26 or higher. If `minSdk` is below 26, update it to 26.
- Add the ML Kit GenAI Prompt API dependency (`com.google.mlkit:genai-prompt`) to the app-level `build.gradle` file, with version at least `1.0.0-beta4`.
- If `com.google.mlkit:genai-schema-compiler` dependency is used and KSP plugin version is below 2.3.6, update it to 2.3.6.

## Detailed steps

### 1. Prompt optimization

To optimize prompts for use with the ML Kit Prompt API, follow the
[prompt optimization guide](https://developer.android.com/agents/skills/device-ai/prompt-api/references/prompt-optimization).

### 2. Prefix caching optimization

If the prompt is more than 200 words, implement the [prefix caching API](https://developer.android.com/agents/skills/device-ai/prompt-api/references/prefix-caching).

### 3. Lifecycle and best practices

- The model must be fully downloaded and available before calling the first inference. Follow the guide on [implementing a generative model](https://developer.android.com/agents/skills/device-ai/prompt-api/references/get-started) to check that the `FeatureStatus` of a model is `AVAILABLE` before making an inference.
- Release ML Kit instances by calling `close()` when an `Activity`,
`Fragment`, or `ViewModel` is destroyed. Example:


```kotlin
// Instantiating model in activity, fragment, or ViewModel
val generativeModel = Generation.getClient()

// When activity, fragment, or ViewModel is destroyed
generativeModel.close()
```

<br />

### 4. Structured output

When implementing or refactoring a prompt to use structured output, follow
these rules:

1. **Check for API availability:** Verify Structured Output feature is available on the device with `isStructuredOutputFeatureAvailable()` before using it. Refer to the [Structured Output API guide](https://developer.android.com/agents/skills/device-ai/prompt-api/references/structured-output) for full instructions.
2. **Return type:** Return the `@Generable` typed object from the function
signature instead of a `String` or JSON string.

For example:

fun parseEmail(email: String): String {
...
}

should be refactored to:

fun parseEmail(email: String): ParsedEmail? {
...
}

3. **Example:**

This is the example code before refactoring:


```kotlin
suspend fun parseEmail(email: String): String {
val parseEmailPrompt = "Parse this email and return the sender, title, and short summary of the email less than 10 words: "

val parsedEmail = generativeModel.generateContent(parseEmailPrompt + email)

return parsedEmail.candidates[0].text
}
```

<br />

This is the example code after using Structured Output API:


```kotlin
@Generable
data class ParsedEmail(
@Guide(description = "Sender of the email")
var sender: String = "",

@Guide(description = "Title of the email")
var title: String = "",

@Guide(description = "Summary of the email less than 10 words")
var summary: String = ""
)

suspend fun parseEmail(email: String): ParsedEmail? {
val parseEmailPrompt =
"Parse this email: $email"

val baseRequest = GenerateContentRequest.Builder(TextPart(parseEmailPrompt)).build()
val typedRequest = generateTypedContentRequest(baseRequest, ParsedEmail::class)
val typedResponse = generativeModel.generateContent(typedRequest)
return typedResponse.candidates[0].response
}
```

<br />
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