Gemini API Dev

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 Your knowledge is outdated.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

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

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

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:

1. Fetch llms.txt to discover available documentation pages
2. 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

• Models
• Google AI Studio quickstart
• Nano Banana image generation
• Function calling with the Gemini API
• Structured outputs
• Text generation
• Image understanding
• Embeddings
• Interactions API
• SDK migration guide

When to Use

This skill is applicable to execute the workflow or actions described in the overview.

AGI Framework Integration

Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills

Memory-First Protocol

Retrieve prior agent configurations, team compositions, and orchestration patterns. Critical for multi-agent system consistency.

# Check for prior AI agent orchestration context before starting
python3 execution/memory_manager.py auto --query "agent patterns and orchestration strategies for Gemini Api Dev"

Storing Results

After completing work, store AI agent orchestration decisions for future sessions:

python3 execution/memory_manager.py store \
  --content "Agent pattern: hierarchical orchestration with Control Tower dispatcher, 3 specialist sub-agents" \
  --type decision --project <project> \
  --tags gemini-api-dev ai-agents

Multi-Agent Collaboration

This skill is inherently multi-agent. Use cross-agent context to coordinate task distribution and avoid duplicate work.

python3 execution/cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "Agent architecture designed — Control Tower + specialist agents with shared Qdrant memory" \
  --project <project>

Control Tower Integration

Register agents and tasks with the Control Tower (execution/control_tower.py) for centralized orchestration across machines and LLM providers.

Blockchain Identity

Each agent has a cryptographic Ed25519 identity. All memory writes are signed — enabling trust verification in multi-agent systems.

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