ML Kit GENAI Prompt API

Organization

android

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."

Overview

Repository: android/skills
Publisher: android
Stars: 10.5K
Forks: 695
Bundled files: 6
License: Apache-2.0
Updated: Nov 11, 2026
Links: Skill source Repository

Installation

tmp=$(mktemp -d)
git clone --depth 1 https://github.com/android/skills.git "$tmp"
mkdir -p ~/.claude/skills
cp -R "$tmp/device-ai/ml-kit-genai-prompt-api" ~/.claude/skills/ml-kit-genai-prompt-api
rm -rf "$tmp"

tmp=$(mktemp -d)
git clone --depth 1 https://github.com/android/skills.git "$tmp"
mkdir -p ~/.codex/skills
cp -R "$tmp/device-ai/ml-kit-genai-prompt-api" ~/.codex/skills/ml-kit-genai-prompt-api
rm -rf "$tmp"

Install the ML Kit GENAI Prompt API agent skill from https://github.com/android/skills/tree/main/device-ai/ml-kit-genai-prompt-api into this project's skills folder.

Instructions

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.

2. Prefix caching optimization

If the prompt is more than 200 words, implement the prefix caching API.

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 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:

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

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

4. Structured output

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

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 for full instructions.
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? {
    ...
}
```

Example:

This is the example code before refactoring:

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
}

This is the example code after using Structured Output API:

@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
}

Bundled files

FAQ

What does ML Kit GENAI Prompt API do?

Who made ML Kit GENAI Prompt API?

android (an organization) publishes it in the android/skills repository on GitHub.

Is ML Kit GENAI Prompt API free?

Yes. ML Kit GENAI Prompt API is open source, published by android under the Apache-2.0 license.

What comes with ML Kit GENAI Prompt API?

Its instructions and 6 files the agent reads only when a task needs them, such as references, examples or scripts.

Which agents can use ML Kit GENAI Prompt API?

Any coding agent that reads SKILL.md files, including Claude Code, Codex, Cursor, GitHub Copilot and Gemini CLI. The agent loads the skill when a task matches its description.

What are agent skills?

An agent skill is a folder with a SKILL.md file: instructions that teach a coding agent how to do one kind of task, and optionally files it can read while it works. The agent keeps only the skill's name and description in mind and reads the rest when a task calls for it.

How are agent skills different from MCP servers?

An MCP server gives an agent new tools to call, code that runs somewhere and returns results. A skill gives it knowledge: how to approach a task with the tools it already has. Many agents use both.

How can my agent test my Android app?

Robomori lets coding agents create, start and control Android emulators and devices from one command, so they can install your app, take screenshots and check dark mode, font sizes and languages.

Overview

Installation

Instructions

Prerequisites

Detailed steps

1. Prompt optimization

2. Prefix caching optimization

3. Lifecycle and best practices

4. Structured output

Bundled files

FAQ

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