Ml Kit Genai Prompt API
Analyzes Android codebases to implement ML Kit GenAI Prompt API.
Install
npx skills add android/skills --skill ml-kit-genai-prompt-api -g -a claude-code -y- Repository
- android/skills10.2k
- Category
- Data and ML
- Updated
- 6d ago
- License
- Complete terms in LICENSE.txt
- Context cost
- Lightabout 917 tokens
- Runs scripts
- No, instructions only
What it does
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.
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
minSdkis below 26, update it to 26. - Add the ML Kit GenAI Prompt API dependency (
com.google.mlkit:genai-prompt) to the app-levelbuild.gradlefile, with version at least1.0.0-beta4. - If
com.google.mlkit:genai-schema-compilerdependency 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
FeatureStatusof a model isAVAILABLEbefore making an inference. -
Release ML Kit instances by calling
close()when anActivity,Fragment, orViewModelis destroyed. Example:
<br />// 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:
