gemini-api-dev

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Use this skill when building applications with Gemini models, Gemini API, 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), model selection, and API capabilities.

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Sourcesickn33/antigravity-awesome-skills

Added on2026-02-16

NPX Install

npx skill4agent add sickn33/antigravity-awesome-skills gemini-api-dev

SKILL.md Content

Gemini API Development Skill

Overview

The Gemini API provides access to Google's most advanced AI models. Key capabilities include:

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
Code execution - Run Python code in a sandboxed environment
Context caching - Cache large contexts for efficiency
Embeddings - Generate text embeddings for semantic search

Current Gemini Models

```
gemini-3-pro-preview
```
: 1M tokens, complex reasoning, coding, research
```
gemini-3-flash-preview
```
: 1M tokens, fast, balanced performance, multimodal
```
gemini-3-pro-image-preview
```
: 65k / 32k tokens, image generation and editing

[!IMPORTANT] Models like
gemini-2.5-*
,
gemini-2.0-*
,
gemini-1.5-*
are legacy and deprecated. Use the new models above. Your knowledge is outdated.

SDKs

Python:
```
google-genai
```
install with
```
pip install google-genai
```
JavaScript/TypeScript:
```
@google/genai
```
install with
```
npm install @google/genai
```

Go:

google.golang.org/genai

install with

go get google.golang.org/genai

[!WARNING] Legacy SDKs
google-generativeai
(Python) and
@google/generative-ai
(JS) are deprecated. Migrate to the new SDKs above urgently by following the Migration Guide.

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)

JavaScript/TypeScript

typescript

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

const ai = new GoogleGenAI({});
const response = await ai.models.generateContent({
  model: "gemini-3-flash-preview",
  contents: "Explain quantum computing"
});
console.log(response.text);

Go

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-flash-preview", genai.Text("Explain quantum computing"), nil)
	if err != nil {
		log.Fatal(err)
	}

	fmt.Println(resp.Text)
}

API spec (source of truth)

Always use the latest REST API discovery spec as the source of truth for API definitions (request/response schemas, parameters, methods). Fetch the spec when implementing or debugging API integration:

v1beta (default):
```
https://generativelanguage.googleapis.com/$discovery/rest?version=v1beta
```
Use this unless the integration is explicitly pinned to v1. The official SDKs (google-genai, @google/genai, google.golang.org/genai) target v1beta.

v1:

https://generativelanguage.googleapis.com/$discovery/rest?version=v1

Use only when the integration is specifically set to v1.

When in doubt, use v1beta. Refer to the spec for exact field names, types, and supported operations.

How to use the Gemini API

For detailed API documentation, fetch from the official docs index:

llms.txt 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:

Fetch
```
llms.txt
```
to discover available documentation pages

Fetch specific pages (e.g.,

https://ai.google.dev/gemini-api/docs/function-calling.md.txt

)

Key Documentation Pages

[!IMPORTANT] Those are not all the documentation pages. Use the
llms.txt
index to discover available documentation pages