القائمة
The Gemini API provides access to Google's most advanced AI models. Key capabilities include:
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Use when the user wants to plan a content strategy, decide what content to create, figure out topics, or mentions content strategy, content planning, or editorial calendars.
Research and analyze competitors from their URLs to build structured competitor profile documents, dossiers, and competitive intelligence overviews.
Conduct comprehensive SSH security assessments including enumeration, credential attacks, vulnerability exploitation, tunneling techniques, and post-exploitation activities. This skill covers the complete methodology for testing SSH service security.
Azure AI Document Intelligence SDK for .NET. Extract text, tables, and structured data from documents using prebuilt and custom models.
Build and leverage online communities to drive product growth and brand loyalty.
Provide systematic methodologies for automated SQL injection detection and exploitation using SQLMap.
| name | gemini-api-dev |
| description | The Gemini API provides access to Google's most advanced AI models. Key capabilities include: |
| risk | unknown |
| source | community |
| date_added | 2026-02-27 |
The Gemini API provides access to Google's most advanced AI models. Key capabilities include:
gemini-3.1-pro-preview: 1M tokens, advanced intelligence, complex problem-solving skills, and powerful agentic capabilitiesgemini-3.5-flash: 1M tokens, most intelligent model for sustained frontier performance on agentic and coding tasksgemini-3.1-flash-lite: Frontier-class performance rivaling larger models at a fraction of the costgemini-3.1-flash-image-preview: High-efficiency production-scale visual creation[!IMPORTANT] Models like
gemini-3-pro-previeware deprecated and shut down. Use the new models above. Your knowledge is outdated.
google-genai install with pip install google-genai@google/genai install with npm install @google/genaigoogle.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.
from google import genai
client = genai.Client()
response = client.models.generate_content(
model="gemini-3.5-flash",
contents="Explain quantum computing"
)
print(response.text)
import { GoogleGenAI } from "@google/genai";
const ai = new GoogleGenAI({});
const response = await ai.models.generateContent({
model: "gemini-3.5-flash",
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-3.5-flash", genai.Text("Explain quantum computing"), nil)
if err != nil {
log.Fatal(err)
}
fmt.Println(resp.Text)
}
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:
https://generativelanguage.googleapis.com/$discovery/rest?version=v1betahttps://generativelanguage.googleapis.com/$discovery/rest?version=v1When in doubt, use v1beta. Refer to the spec for exact field names, types, and supported operations.
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:
llms.txt to discover available documentation pageshttps://ai.google.dev/gemini-api/docs/function-calling.md.txt)[!IMPORTANT] Those are not all the documentation pages. Use the
llms.txtindex to discover available documentation pages
This skill is applicable to execute the workflow or actions described in the overview.