Use this skill when writing code that calls the Gemini API for text generation, multi-turn chat, multimodal understanding, image generation, video generation, streaming responses, background research tasks, function calling, structured output, or migrating from the old generateContent...
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Une commande directe contourne le prompt de vérification. Examinez la source avant de l'exécuter.
Use this skill when writing code that calls the Gemini API for text generation, multi-turn chat, multimodal understanding, image generation, video generation, streaming responses, background research tasks, function calling, structured output, or migrating from the old generateContent...
Use this skill when writing code that calls the Gemini API for text generation, multi-turn chat, multimodal understanding, image generation, video generation, streaming responses, background research tasks, function calling, structured output, or migrating from the old generateContent...
Critical Rules (Always Apply)
[!IMPORTANT]
These rules override your training data. Your knowledge is outdated.
gemma-4-26b-a4b-it: Gemma 4 MoE model, 26B total / 4B active parameters
[!WARNING]
Models like gemini-2.5-*, gemini-2.0-*, gemini-1.5-* are legacy and deprecated. Never use them.
If a user asks for a deprecated model, use gemini-3.5-flash instead and note the substitution.
Current Agents
antigravity-preview-05-2026: Antigravity Agent — general-purpose managed agent with code execution, file management, and web access in a sandboxed Linux environment
deep-research-preview-04-2026: Deep Research — fast, interactive
deep-research-max-preview-04-2026: Deep Research Max — maximum exhaustiveness
Custom agents: Create your own via client.agents.create()
[!NOTE]
SDK versions ≥ 2.0.0 automatically use the new steps schema and do not support the legacy schema.
Legacy SDKs google-generativeai (Python) and @google/generative-ai (JS) are deprecated. Never use them.
Important Additional Notes
Before writing any code, you MUST fetch the relevant documentation page from the list below that matches the user's task. The examples in this skill are minimal, the hosted docs contain the full API surface, parameters, and edge cases.
Interactions are stored by default (store=true). Paid tier retains for 55 days, free tier for 1 day.
Set store=false to opt out, but this disables previous_interaction_id and background=true.
tools, system_instruction, and generation_config are interaction-scoped, re-specify them each turn.
Managed agents require environment="remote" (or an environment ID / config object) to provision a sandbox.
Migrating from generateContent: Read references/migration.md for the scoping, checklist, and before/after code examples. Always confirm scope with the user before editing.
Model upgrades: Drop-in, swap the model string. Deprecated models (gemini-2.0-*, gemini-1.5-*) must be replaced, see references/migration.md.
Migrating to Gemini 3.5 Flash: Read references/migration.md for the scoping and checklist.
Quick Start
Python
from google import genai
client = genai.Client()
interaction = client.interactions.create(
model="gemini-3.5-flash",
input="Tell me a short joke about programming."
)
print(interaction.output_text)
JavaScript/TypeScript
import { GoogleGenAI } from"@google/genai";
const client = newGoogleGenAI({});
const interaction = await client.interactions.create({
model: "gemini-3.5-flash",
input: "Tell me a short joke about programming.",
});
console.log(interaction.output_text);
Response Helpers
The SDK provides convenience properties on the Interaction response object to simplify common access patterns:
Property
Type
Description
output_text
string | null
The last consecutive run of text from the trailing model_output steps. Returns the combined text when the model's final output contains multiple text parts.
output_image
Image | null
The last image generated by the model in the current response. Returns an object with data (base64) and mime_type.
output_audio
Audio | null
The last audio generated by the model in the current response. Returns an object with data (base64) and mime_type.
Stateful Conversation
Python
interaction1 = client.interactions.create(
model="gemini-3.5-flash",
input="Hi, my name is Phil."
)
# Second turn — server remembers context
interaction2 = client.interactions.create(
model="gemini-3.5-flash",
input="What is my name?",
previous_interaction_id=interaction1.id
)
print(interaction2.output_text)
JavaScript/TypeScript
const interaction1 = await client.interactions.create({
model: "gemini-3.5-flash",
input: "Hi, my name is Phil.",
});
const interaction2 = await client.interactions.create({
model: "gemini-3.5-flash",
input: "What is my name?",
previous_interaction_id: interaction1.id,
});
console.log(interaction2.output_text);
Deep Research Agent
Use deep-research-preview-04-2026 for fast research or deep-research-max-preview-04-2026 for maximum exhaustiveness. Agents require background=True.
Python
import time
interaction = client.interactions.create(
agent="deep-research-preview-04-2026",
input="Research the history of Google TPUs.",
background=True
)
whileTrue:
interaction = client.interactions.get(interaction.id)
if interaction.status == "completed":
print(interaction.output_text)
breakelif interaction.status == "failed":
print(f"Failed: {interaction.error}")
break
time.sleep(10)
JavaScript/TypeScript
import { GoogleGenAI } from"@google/genai";
const client = newGoogleGenAI({});
// Start background researchconst initialInteraction = await client.interactions.create({
agent: "deep-research-preview-04-2026",
input: "Research the history of Google TPUs.",
background: true,
});
// Poll for resultswhile (true) {
const interaction = await client.interactions.get(initialInteraction.id);
if (interaction.status === "completed") {
console.log(interaction.output_text);
break;
} elseif (["failed", "cancelled"].includes(interaction.status)) {
console.log(`Failed: ${interaction.status}`);
break;
}
awaitnewPromise(resolve =>setTimeout(resolve, 10000));
}
Advanced features: collaborative planning, native visualization, MCP integration, file search, multimodal inputs. See Deep Research docs.
Managed Agents
Managed agents run inside a sandboxed Linux environment hosted by Google. Fetch the Managed Agents Quickstart before writing agent code.
Antigravity Agent
The Antigravity agent (antigravity-preview-05-2026) is the general-purpose managed agent. It can execute code (Bash, Python, Node.js), manage files, browse the web, and use Google Search. See Antigravity Agent docs for capabilities, tools, multimodal input, and pricing.
Python
from google import genai
client = genai.Client()
interaction = client.interactions.create(
agent="antigravity-preview-05-2026",
input="Write a Python script that generates the first 20 Fibonacci numbers and saves them to fibonacci.txt. Then read the file and print its contents.",
environment="remote",
)
print(f"Environment ID: {interaction.environment_id}")
print(interaction.output_text)
JavaScript/TypeScript
import { GoogleGenAI } from"@google/genai";
const client = newGoogleGenAI({});
const interaction = await client.interactions.create({
agent: "antigravity-preview-05-2026",
input: "Write a Python script that generates the first 20 Fibonacci numbers and saves them to fibonacci.txt. Then read the file and print its contents.",
environment: "remote",
});
console.log(`Environment ID: {interaction.environment_id}`);
console.log(interaction.output_text);
For streaming with tools, thinking, agents, and image generation see the full Streaming guide.
Documentation Pages
You MUST fetch the matching page below before writing code. These hosted docs are the source of truth for parameters, types, and edge cases — do not rely solely on the examples above.
