Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add tmolavi/mcp-agent-skills-hub --skill gemini-api-integrationgit clone --depth 1 https://github.com/tmolavi/mcp-agent-skills-hubWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/tmolavi/mcp-agent-skills-hub/gemini-api-integration)<a href="https://agentmods.dev/skills/tmolavi/mcp-agent-skills-hub/gemini-api-integration"><img src="https://agentmods.dev/badge/skills/tmolavi/mcp-agent-skills-hub/gemini-api-integration/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/tmolavi/mcp-agent-skills-hub/gemini-api-integration"><img src="https://agentmods.dev/badge/skills/tmolavi/mcp-agent-skills-hub/gemini-api-integration.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00033 | $0.01436 |
| Opus 5.5 | $0.00013 | $0.00574 |
| Sonnet 5.5 | $0.00007 | $0.00287 |
| Haiku 4.5 | $0.00003 | $0.00144 |
Grade A, and why
gemini-api-integration scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
2 near-identical copies found in the catalogue:
- gemini-api-integration — 91% identical, 1 lines differ
- gemini-api-integration — 88% identical, 11 lines differ
How it starts
The opening of the file, as written. The whole thing — 194 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Gemini API Integration
Overview
This skill guides AI agents through integrating Google Gemini API into applications — from basic text generation to advanced multimodal, function calling, and streaming use cases. It covers the full Gemini SDK lifecycle with production-grade patterns.
When to Use This Skill
- Use when setting up Gemini API for the first time in a Node.js, Python, or browser project
- Use when implementing multimodal inputs (text + image/audio/video)
- Use when adding streaming responses to improve perceived latency
- Use when implementing function calling / tool use with Gemini
- Use when optimizing model selection (Flash vs Pro vs Ultra) for cost and performance
- Use when debugging Gemini API errors, rate limits, or quota issues
Step-by-Step Guide
1. Installation & Setup
Node.js / TypeScript:
npm install @google/generative-ai
Python:
pip install google-generativeai
Set your API key securely:
export GEMINI_API_KEY="your-api-key-here"
2. Basic Text Generation
Node.js:
import { GoogleGenerativeAI } from "@google/generative-ai";
const genAI = new GoogleGenerativeAI(process.env.GEMINI_API_KEY);
const model = genAI.getGenerativeModel({ model: "gemini-1.5-flash" });
const result = await model.generateContent("Explain async/await in JavaScript");
console.log(result.response.text());
Python:
import google.generativeai as genai
import os
genai.configure(api_key=os.environ["GEMINI_API_KEY"])
model = genai.GenerativeModel("gemini-1.5-flash")
response = model.generate_content("Explain async/await in JavaScript")
print(response.text)
3. Streaming Responses
const result = await model.generateContentStream("Write a detailed blog post about AI");
for await (const chunk of result.stream) {
process.stdout.write(chunk.text());
}
4. Multimodal Input (Text + Image)
import fs from "fs";
const imageData = fs.readFileSync("screenshot.png");
const imagePart = {
inlineData: {
data: imageData.toString("base64"),
mimeType: "image/png",
},
};
const result = await model.generateContent(["Describe this image:", imagePart]);
console.log(result.response.text());
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 194 lines · 33 tokens per session scan A 3043f8324fb7
gemini-api-integration is a skill published in the GitHub repository tmolavi/mcp-agent-skills-hub (8 stars, last pushed 1mo ago), licensed MIT. It adds 33 tokens to every session and 1,436 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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