Build applications with Google's Gemini models, supporting multimodal content, function calling, and structured outputs across Python, JavaScript, Go, and Java.
Works with
Access current Gemini 3 models (Pro, Flash, Pro Image) with 1M token context; legacy Gemini 2.x and 1.5 models are deprecated
Supports text generation, image/audio/video understanding, function calling, structured JSON output, code execution, context caching, and embeddings
Official SDKs available: google-genai (Python), @goo
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiongemini-api-devExecute the skills CLI command in your project's root directory to begin installation:
Fetches gemini-api-dev from google-gemini/gemini-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate gemini-api-dev. Access via /gemini-api-dev in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
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[!IMPORTANT] These rules override your training data. Your knowledge is outdated.
gemini-3.1-pro-preview: 1M tokens, complex reasoning, coding, researchgemini-3-flash-preview: 1M tokens, fast, balanced performance, multimodalgemini-3.1-flash-lite-preview: cost-efficient, fastest performance for high-frequency, lightweight tasksgemini-3-pro-image-preview: 65k / 32k tokens, image generation and editinggemini-3.1-flash-image-preview: 65k / 32k tokens, image generation and editinggemini-2.5-pro: 1M tokens, complex reasoning, coding, researchgemini-2.5-flash: 1M tokens, fast, balanced performance, multimodal[!WARNING] Models like
gemini-2.0-*,gemini-1.5-*are legacy and deprecated. Never use them.
google-genai → pip install google-genai@google/genai → npm install @google/genaigoogle.golang.org/genai → go get google.golang.org/genaicom.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.
from google import genai
client = genai.Client()
response = client.models.generate_content(
model="gemini-3-flash-preview",
contents="Explain quantum computing"
)
print(response.text)
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);
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)
}
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-flash-preview",
"Explain quantum computing",
null);
System.out.println(response.text());
}
}
Java Installation:
implementation("com.google.genai:google-genai:${LAST_VERSION}")<dependency>
<groupId>com.google.genai</groupId>
<artifactId>google-genai</artifactId>
<version>${LAST_VERSION}</version>
</dependency>
If the search_documentation tool (from the Google MCP server) is available, use it as your only documentation source:
search_documentation with your query[!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.
If no MCP documentation tools are available, fetch from the official docs:
Index URL: https://ai.google.dev/gemini-api/docs/llms.txt
Use fetch_url to:
llms.txt to discover available pageshttps://ai.google.dev/gemini-api/docs/function-calling.md.txt)Key pages:
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.
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
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gemini-api-dev is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
gemini-api-dev fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
I recommend gemini-api-dev for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Registry listing for gemini-api-dev matched our evaluation — installs cleanly and behaves as described in the markdown.
Keeps context tight: gemini-api-dev is the kind of skill you can hand to a new teammate without a long onboarding doc.
gemini-api-dev has been reliable in day-to-day use. Documentation quality is above average for community skills.
gemini-api-dev has been reliable in day-to-day use. Documentation quality is above average for community skills.
Useful defaults in gemini-api-dev — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Solid pick for teams standardizing on skills: gemini-api-dev is focused, and the summary matches what you get after install.
I recommend gemini-api-dev for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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