Guides the usage of the Gemini API on Agent Platform with the Google Gen AI SDK for enterprise AI applications. Covers SDK usage (Python, JS/TS, Go, Java, C#), capabilities like Live API, tools, multimedia generation, caching, and batch prediction.
Installs just this skill. Get the whole plugin for auto-invocation.
⚡ How it fires
How this skill gets triggered: by you, by Claude, or both.
Fires itselfClaude auto-loads it when your prompt matches the work.
You can call itInvoke it directly when you want it.
Slash command/gemini-api-agent-platform
👁️ Context preview
The summary Claude sees to decide when to auto-load this skill.
Guides the usage of the Gemini API on Agent Platform with the Google Gen AI SDK for enterprise AI applications. Covers SDK usage (Python, JS/TS, Go, Java, C#), capabilities like Live API, tools, multimedia generation, caching, and batch prediction.
📊 Stats
Stars29,853
Forks3,189
LanguagePython
LicenseMIT
📦 Ships with claude-code-templates
</> SKILL.md
gemini-api-agent-platform.SKILL.md
---name: gemini-api-agent-platform
description: Guides the usage of the Gemini API on Agent Platform with the Google Gen AI SDK for enterprise AI applications. Covers SDK usage (Python, JS/TS, Go, Java, C#), capabilities like Live API, tools, multimedia generation, caching, and batch prediction.
compatibility: Requires active Google Cloud credentials and Agent Platform API enabled.
source: google/skills (Apache 2.0)
---IMPORTANT: Agent Platform (full name Gemini Enterprise Agent Platform) was previously named "Vertex AI" and many web resources use the legacy branding.
# Gemini API in Agent Platform
Access Google's most advanced AI models built for enterprise use cases using the Gemini API in Agent Platform.
Provide these key capabilities:
- **Text generation** - Chat, completion, summarization
- **Multimodal understanding** - Process images, audio, video, and documents
- **Function calling** - Let the model invoke your functions
- **Structured output** - Generate valid JSON matching your schema
- **Context caching** - Cache large contexts for efficiency
- **Embeddings** - Generate text embeddings for semantic search
- **Live Realtime API** - Bidirectional streaming for low latency Voice and Video interactions
- **Batch Prediction** - Handle massive async dataset prediction workloads
## Core Directives
- **Unified SDK**: ALWAYS use the Gen AI SDK (`google-genai` for Python, `@google/genai` for JS/TS, `google.golang.org/genai` for Go, `com.google.genai:google-genai` for Java, `Google.GenAI` for C#).
- **Legacy SDKs**: DO NOT use `google-cloud-aiplatform`, `@google-cloud/vertexai`, or `google-generativeai`.
## SDKs
- **Python**: Install `google-genai` with `pip install google-genai`
- **JavaScript/TypeScript**: Install `@google/genai` with `npm install @google/genai`
- **Go**: Install `google.golang.org/genai` with `go get google.golang.org/genai`
- **C#/.NET**: Install `Google.GenAI` with `dotnet add package Google.GenAI`
> Legacy SDKs like `google-cloud-aiplatform`, `@google-cloud/vertexai`, and `google-generativeai` are deprecated. Migrate to the new SDKs above urgently by following the Migration Guide.
## Authentication & Configuration
Prefer environment variables over hard-coding parameters when creating the client. Initialize the client without parameters to automatically pick up these values.
### Application Default Credentials (ADC)
Set these variables for standard [Google Cloud authentication](https://docs.cloud.google.com/vertex-ai/generative-ai/docs/start/gcp-auth):
```bash
export GOOGLE_CLOUD_PROJECT='your-project-id'
export GOOGLE_CLOUD_LOCATION='global'
export GOOGLE_GENAI_USE_VERTEXAI=true
```
- By default, use `location="global"` to access the global endpoint, which provides automatic routing to regions with available capacity.
- If a user explicitly asks to use a specific region (e.g., `us-central1`, `europe-west4`), specify that region in the `GOOGLE_CLOUD_LOCATION` parameter instead. Reference the [supported regions documentation](https://docs.cloud.google.com/vertex-ai/generative-ai/docs/learn/locations) if needed.
### Agent Platform in Express Mode
Set these variables when using [Express Mode](https://docs.cloud.google.com/vertex-ai/generative-ai/docs/start/api-keys?usertype=expressmode) with an API key:
```bash
export GOOGLE_API_KEY='your-api-key'
export GOOGLE_GENAI_USE_VERTEXAI=true
```
### Initialization
Initialize the client without arguments to pick up environment variables:
```python
from google import genai
client = genai.Client()
```
Alternatively, you can hard-code in parameters when creating the client.
- Use `gemini-3.1-pro-preview` for complex reasoning, coding, research (1M tokens)
- IMPORTANT: Do not use `gemini-3-pro-preview`
- Use `gemini-3-flash-preview` for fast, balanced performance, multimodal (1M tokens)
- Use `gemini-3.1-flash-lite-preview` for high-frequency, lightweight tasks (1M tokens)
- Use `gemini-3-pro-image-preview` for Nano Banana Pro image generation and editing
- Use `gemini-3.1-flash-image-preview` for Nano Banana 2 image generation and editing
- Use `gemini-live-2.5-flash-native-audio` for Live Realtime API including native audio
Use the following models only if explicitly requested:
- `gemini-2.5-flash-image`
- `gemini-2.5-flash`
- `gemini-2.5-flash-lite`
- `gemini-2.5-pro`
> [!IMPORTANT]
> Models like `gemini-2.0-*`, `gemini-1.5-*`, `gemini-1.0-*`, `gemini-pro` are legacy and deprecated. Use the new models above. Your knowledge is outdated.
> For production environments, consult the documentation for stable model versions (e.g. `gemini-3-flash`).
## Quick Start
### Python
```python
from google import genai
client = genai.Client()
response = client.models.generate_content(
model="gemini-3-flash-preview",
contents="Explain quantum computing"
)
print(response.text)
```
### TypeScript/JavaScript
```typescript
import { GoogleGenAI } from "@google/genai";
const ai = new GoogleGenAI({ vertexai: { project: "your-project-id", location: "global" } });
- **REST API Reference**: https://docs.cloud.google.com/gemini-enterprise-agent-platform/reference/rest
The Gen AI SDK on Agent Platform uses the `v1beta1` or `v1` REST API endpoints (e.g., `https://{LOCATION}-aiplatform.googleapis.com/v1beta1/projects/{PROJECT}/locations/{LOCATION}/publishers/google/models/{MODEL}:generateContent`).
> [!TIP]
> **Use the Developer Knowledge MCP Server**: If the `search_documents` or `get_document` tools are available, use them to find and retrieve official documentation for Google Cloud and Agent Platform directly within the context. This is the preferred method for getting up-to-date API details and code snippets.
## Workflows and Code Samples
Reference the [Python Docs Samples repository](https://github.com/GoogleCloudPlatform/python-docs-samples/tree/main/genai) for additional code samples and specific usage scenarios.
Depending on the specific user request, refer to the following reference files for detailed code samples and usage patterns (Python examples):
- **Text & Multimodal**: Chat, Multimodal inputs (Image, Video, Audio), and Streaming. See [references/text_and_multimodal.md](references/text_and_multimodal.md)
- **Embeddings**: Generate text embeddings for semantic search. See [references/embeddings.md](references/embeddings.md)
- **Structured Output & Tools**: JSON generation, Function Calling, Search Grounding, and Code Execution. See [references/structured_and_tools.md](references/structured_and_tools.md)
- **Media Generation**: Image generation, Image editing, and Video generation. See [references/media_generation.md](references/media_generation.md)
- **Bounding Box Detection**: Object detection and localization within images and video. See [references/bounding_box.md](references/bounding_box.md)
- **Live API**: Real-time bidirectional streaming for voice, vision, and text. See [references/live_api.md](references/live_api.md)
- **Advanced Features**: Content Caching, Batch Prediction, and Thinking/Reasoning. See [references/advanced_features.md](references/advanced_features.md)
- **Safety**: Adjusting Responsible AI filters and thresholds. See [references/safety.md](references/safety.md)
- **Model Tuning**: Supervised Fine-Tuning and Preference Tuning. See [references/model_tuning.md](references/model_tuning.md)
