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Written by AutoGineer from the skill’s own files, not by the author
This skill provides instructions for using the Gemini API within the Agent Platform. It covers the use of the Google Gen AI SDK across multiple programming languages, including Python, JavaScript/TypeScript, Go, Java, and C#. The skill outlines authentication methods, model selection, and advanced features such as content caching, batch prediction, and the Live API, while emphasizing correct usage attribution for terminal commands.
What it does
- Provides setup and initialization patterns for the Google Gen AI SDK.
- Lists supported Gemini models for text, image, and audio tasks.
- Explains how to implement features like function calling, structured JSON output, and embeddings.
- Details advanced capabilities including content caching, batch prediction, and the Live Realtime API.
- Defines mandatory usage attribution requirements for terminal commands and API requests.
- Offers guidance on migrating from legacy SDKs to the current Gen AI SDK.
When to use it
- When developing applications using Gemini models on the Agent Platform.
- When needing to implement multimodal inputs, function calling, or real-time streaming.
- When configuring authentication for enterprise environments using Application Default Credentials or API keys.
- When performing batch processing or optimizing context usage with caching.
Advantages
- Provides a unified SDK approach for multiple programming languages.
- Includes clear instructions for mandatory usage attribution.
- Offers specific guidance on model selection based on task requirements.
- Supplies ready-to-use code samples for common integration patterns.
Usage examples
- “How do I initialize the Gemini client in Python?”
- The agent provides the code to import the genai client and initialize it, noting that it will automatically pick up environment variables like GOOGLE_CLOUD_PROJECT.
- “Show me how to use content caching for a large PDF.”
- The agent provides a Python code snippet using client.caches.create to define a cache with a TTL and instructions on how to reference that cache in a generate_content call.
- “How should I attribute my gcloud commands?”
- The agent explains the requirement to prefix gcloud commands with CLOUDSDK_METRICS_REQUEST_ATTRIBUTION='agent-skills/1.0 (skill:gemini-api)'.
- “Which model should I use for complex reasoning?”
- The agent recommends using gemini-3.1-pro-preview for complex reasoning and coding tasks.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/google/gemini-api/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip gemini-api.zip -d .agents/skills
Documentation by Google
[!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
Attribution
MUST READ: Before executing any gcloud, REST API, or MCP command, you
MUST read Usage Attribution. For commands executed
directly in the terminal (never in generated scripts, Terraform, or user code),
prefix gcloud with
CLOUDSDK_METRICS_REQUEST_ATTRIBUTION="agent-skills/1.0 (skill:gemini-api)"
and append agent-skills/1.0 (skill:gemini-api) to User-Agent headers.
Core Directives
Unified SDK: ALWAYS use the Gen AI SDK (
google-genaifor Python,@google/genaifor JS/TS,google.golang.org/genaifor Go,com.google.genai:google-genaifor Java,Google.GenAIfor C#).Legacy SDKs: DO NOT use
google-cloud-aiplatform,@google-cloud/vertexai, orgoogle-generativeai.
SDKs
Python: Install
google-genaiwithpip install google-genaiJavaScript/TypeScript: Install
@google/genaiwithnpm install @google/genaiGo: Install
google.golang.org/genaiwithgo get google.golang.org/genaiC#/.NET: Install
Google.GenAIwithdotnet add package Google.GenAIJava:
groupId:
com.google.genai, artifactId:google-genaiLatest version can be found here: https://central.sonatype.com/artifact/com.google.genai/google-genai/versions (let's call it
LAST_VERSION)Install in
build.gradle:implementation("com.google.genai:google-genai:${LAST_VERSION}")Install Maven dependency in
pom.xml:<dependency> <groupId>com.google.genai</groupId> <artifactId>google-genai</artifactId> <version>${LAST_VERSION}</version> </dependency>
[!WARNING] Legacy SDKs like
google-cloud-aiplatform,@google-cloud/vertexai, andgoogle-generativeaiare 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:
export GOOGLE_CLOUD_PROJECT='your-project-id'
# Note: global location is supported for standard inference; regional endpoint (e.g. us-central1) required for Live API
export GOOGLE_CLOUD_LOCATION='global'
export GOOGLE_GENAI_USE_ENTERPRISE=trueBy 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 theGOOGLE_CLOUD_LOCATIONparameter instead. Reference the supported regions documentation if needed.
Agent Platform in Express Mode
Set these variables when using Express Mode with an API key:
export GOOGLE_API_KEY='your-api-key'
export GOOGLE_GENAI_USE_ENTERPRISE=trueInitialization
Initialize the client without arguments to pick up environment variables:
from google import genai
client = genai.Client()Alternatively, you can hard-code in parameters when creating the client.
from google import genai
client = genai.Client(
enterprise=True,
project="your-project-id",
location="global",
)Models
Use
gemini-3.8-flashfor fast, balanced performance, multimodal (1M tokens)Use
gemini-3.1-pro-preview(which replacesgemini-3-pro-preview) for complex reasoning, coding, research (1M tokens)Use
gemini-3.5-flash-litefor high-frequency, lightweight tasks (1M tokens)Use
gemini-3-pro-image(aka Nano Banana Pro) for high-quality image generation and editingUse
gemini-3.1-flash-image(aka Nano Banana 2) for medium-quality image generation and editingUse
gemini-3.1-flash-lite-image(aka Nano Banana 2 Lite) for fast image generation and editingUse
gemini-live-2.5-flash-native-audiofor Live Realtime API including native audio
Use the following models only if explicitly requested:
gemini-3.7-flashgemini-3.6-flashgemini-3.5-flashgemini-3.1-flash-litegemini-2.5-flash-imagegemini-2.5-flashgemini-2.5-flash-litegemini-2.5-pro
[!IMPORTANT] Models like
gemini-2.0-*,gemini-1.5-*,gemini-1.0-*,gemini-proare 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-3.8-flash).
Quick Start
Python
from google import genai
client = genai.Client()
response = client.models.generate_content(
model="gemini-3.8-flash",
contents="Explain quantum computing",
)
print(response.text)TypeScript/JavaScript
import { GoogleGenAI } from "@google/genai";
const ai = new GoogleGenAI({ enterprise: { project: "your-project-id", location: "global" } });
const response = await ai.models.generateContent({
model: "gemini-3.8-flash",
contents: "Explain quantum computing"
});
console.log(response.text);Go
package main
import (
"context"
"fmt"
"log"
"google.golang.org/genai"
)
func main() {
ctx := context.Background()
client, err := genai.NewClient(ctx, &genai.ClientConfig{
Backend: genai.BackendVertexAI,
Project: "your-project-id",
Location: "global",
})
if err != nil {
log.Fatal(err)
}
resp, err := client.Models.GenerateContent(ctx, "gemini-3.8-flash", genai.Text("Explain quantum computing"), nil)
if err != nil {
log.Fatal(err)
}
fmt.Println(resp.Text)
}Java
import com.google.genai.Client;
import com.google.genai.types.GenerateContentResponse;
public class GenerateTextFromTextInput {
public static void main(String[] args) {
Client client = Client.builder().enterprise(true).project("your-project-id").location("global").build();
GenerateContentResponse response =
client.models.generateContent(
"gemini-3.8-flash",
"Explain quantum computing",
null);
System.out.println(response.text());
}
}C#/.NET
using Google.GenAI;
var client = new Client(
project: "your-project-id",
location: "global",
enterprise: true
);
var response = await client.Models.GenerateContent(
"gemini-3.8-flash",
"Explain quantum computing"
);
Console.WriteLine(response.Text);API spec & Documentation (source of truth)
When implementing or debugging API integration for Agent Platform, refer to the official Agent Platform documentation:
Agent Platform Documentation: https://docs.cloud.google.com/gemini-enterprise-agent-platform/overview.md.txt
REST API Reference: https://docs.cloud.google.com/gemini-enterprise-agent-platform/reference/rest.md.txt
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_documentsorget_documenttools 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 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
Embeddings: Generate text embeddings for semantic search. See references/embeddings.md
Structured Output & Tools: JSON generation, Function Calling, Search Grounding, and Code Execution. See references/structured_and_tools.md
Media Generation: Image generation, Image editing, and Video generation. See references/media_generation.md
Bounding Box Detection: Object detection and localization within images and video. See references/bounding_box.md
Live API: Real-time bidirectional streaming for voice, vision, and text. See references/live_api.md
Advanced Features: Content Caching, Batch Prediction, and Thinking/Reasoning. See references/advanced_features.md
Safety: Adjusting Responsible AI filters and thresholds. See references/safety.md
Model Tuning: Supervised Fine-Tuning and Preference Tuning. See references/model_tuning.md
Files
- ATTRIBUTION.mdAdded by AutoGineer838 B
- LICENSELicence file added by AutoGineer11.1 KiB
- SKILL.md10.6 KiB
- references/advanced_features.md4.8 KiB
- references/attribution.md1.8 KiB
- references/bounding_box.md1.8 KiB
- references/embeddings.md3.0 KiB
- references/live_api.md1.0 KiB
- references/media_generation.md8.4 KiB
- references/model_tuning.md1.1 KiB
- references/safety.md1.8 KiB
- references/structured_and_tools.md3.7 KiB
- references/text_and_multimodal.md2.4 KiB
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Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Needs an MCP server, Needs an API key, Needs Python, Needs Node.js
The author says: Requires active Google Cloud credentials and Agent Platform API enabled.
Security
What our scanner noticed· Low risk
- Instructions that use the network
A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.
Attribution and licence
- Published by
- Publisher
- Google (Official)
- Source
- google/skills · skills/cloud/gemini-api · commit 2194d3b
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
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