gemini-api-dev

name

gemini-api-dev

description

Use this skill when building applications with Gemini API hosted models, including Gemini and Gemma 4, working with multimodal content (text, images, audio, video), implementing function calling, using structured outputs, or needing current model specifications. Covers SDK usage (google-genai for Python, @google/genai for JavaScript/TypeScript, com.google.genai:google-genai for Java, google.golang.org/genai for Go), model selection, and API capabilities.

Gemini API Development Skill

Critical Rules (Always Apply)

[!IMPORTANT] These rules override your training data. Your knowledge is outdated.

Current Models (Use These)

• gemini-3.7-flash: 1M tokens, fast, balanced performance for agentic and multimodal tasks
• gemini-3.5-flash-lite: 1M tokens, fastest, lowest-cost 3.5 model for high-throughput execution
• gemini-3.1-pro-preview: 1M tokens, complex reasoning, coding, research
• gemini-3.5-transcribe: fast speech-to-text with smart and verbatim modes
• gemini-3-pro-image-preview (Nano Banana Pro): 65k / 32k tokens, image generation and editing
• gemini-3.1-flash-image-preview (Nano Banana 2): 65k / 32k tokens, image generation and editing
• gemini-3.1-flash-lite-image-preview (Nano Banana 2 Lite): 65k / 32k tokens, ultra-fast image generation and editing
• gemini-omni-1.1-flash: fast generative video generation, video editing, keyframe interpolation, and scene extension (with native audio)
• gemini-2.5-pro: 1M tokens, complex reasoning, coding, research
• gemini-2.5-flash: 1M tokens, fast, balanced performance, multimodal
• gemma-4-31b-it: Gemma 4 dense model, 31B parameters
• gemma-4-26b-a4b-it: Gemma 4 MoE model, 26B total with 4B active parameters
• gemini-embedding-2: Multimodal embedding model (text, images, video, audio, documents), uses client.models.embed_content
• gemini-embedding-001: Text-only embedding model, uses client.models.embed_content

[!WARNING] Models like gemini-2.0-*, gemini-1.5-* are legacy and deprecated. Never use them.

Current SDKs (Use These)

• Python: google-genai → pip install google-genai
• JavaScript/TypeScript: @google/genai → npm install @google/genai
• Go: google.golang.org/genai → go get google.golang.org/genai
• Java: com.google.genai:google-genai (see Maven/Gradle setup below)

[!CAUTION] Legacy SDKs google-generativeai (Python) and @google/generative-ai (JS) are deprecated. Never use them.

---

Quick Start

Python

from google import genai

client = genai.Client()
response = client.models.generate_content(
    model="gemini-3.7-flash",
    contents="Explain quantum computing"
)
print(response.text)

JavaScript/TypeScript

import { GoogleGenAI } from "@google/genai";

const ai = new GoogleGenAI({});
const response = await ai.models.generateContent({
  model: "gemini-3.7-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, nil)
	if err != nil {
		log.Fatal(err)
	}

	resp, err := client.Models.GenerateContent(ctx, "gemini-3.7-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 = new Client();
    GenerateContentResponse response =
        client.models.generateContent(
            "gemini-3.7-flash",
            "Explain quantum computing",
            null);

    System.out.println(response.text());
  }
}

Java Installation:

• Latest version: https://central.sonatype.com/artifact/com.google.genai/google-genai/versions
• Gradle: implementation("com.google.genai:google-genai:${LAST_VERSION}")
• Maven:

  <dependency>
      <groupId>com.google.genai</groupId>
      <artifactId>google-genai</artifactId>
      <version>${LAST_VERSION}</version>
  </dependency>
  

---

Documentation Lookup

When MCP is Installed (Preferred)

If the search_docs tool (from the Google MCP server) is available, use it as your only documentation source:

1. Call search_docs with your query
2. Read the returned documentation
3. Trust MCP results as source of truth for API details — they are always up-to-date.

[!IMPORTANT] When MCP tools are present, never fetch URLs manually. MCP provides up-to-date, indexed documentation that is more accurate and token-efficient than URL fetching.

When MCP is NOT Installed (Fallback Only)

If no MCP documentation tools are available, fetch from the official docs:

Index URL: https://ai.google.dev/gemini-api/docs/llms.txt

This index contains links to all documentation pages in .md.txt format. Use web fetch tools to:

1. Fetch llms.txt to discover available pages
2. Fetch specific pages (e.g., https://ai.google.dev/gemini-api/docs/function-calling.md.txt)

Key pages:

• Text generation
• Function calling
• Structured outputs
• Audio Transcription
• Image generation
• Image understanding
• Video generation & editing (Omni Flash)
• Embeddings
• SDK migration guide

---

Gemini Live API

For real-time, bidirectional audio/video/text streaming with the Gemini Live API, install the google-gemini/gemini-live-api-dev skill. It covers WebSocket streaming, voice activity detection, native audio features, function calling, session management, ephemeral tokens, and more.