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 agentmods add skills/beel-collab/presets.dev/gemini-api-integrationnpx skills add beel-collab/presets.dev --skill gemini-api-integrationgit clone --depth 1 https://github.com/beel-collab/presets.devWhat 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 | $0.00027 | $0.01448 |
| Opus 5 | $0.00014 | $0.00724 |
| Sonnet 5 | $0.00005 | $0.00290 |
| Haiku 4.5 | $0.00003 | $0.00145 |
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 2d 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.
How it starts
The opening of the file, as written. The whole thing — 195 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.
- 2d ago First seen · 195 lines · 27 tokens per session scan A 760a0da1283f
gemini-api-integration is a skill published in the GitHub repository beel-collab/presets.dev (2 stars, last pushed 3mo ago), licensed MIT. It adds 27 tokens to every session and 1,448 once invoked, about $0.0001 per session on Opus 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 2026-08-31.
Other skills, from other repositories
prompt-enhancer
Transform poor or overly simple prompts with expert-level framing. Use when the user explicitly asks to improve, refine, or rewrite a prompt, or wants help framing a request for another AI system. Do NOT use for authoring, reviewing, or migrating system prompts or skills targeting a specific Claude model…
ml-llm-wiki
Use when answering questions from this machine-learning knowledge base. Triggers: questions about transformers, attention cost and efficiency, and long-context scaling; 'what do we know about attention', 'check the ML wiki'. Read-only querying of compiled knowledge; to add, update, supersede, lint, audit, or critique…
llm-prompting-guide
Prompt format rules for generative video and music models. Use when writing or reviewing a prompt for MiniMax H3 (text/image/reference-to-video with native audio) or MiniMax Music 3 (song generation from caption plus lyrics), in ComfyUI or elsewhere. Do NOT use for chat-assistant prompts, or for generative models not…
graphify
Use for any question about a codebase, its architecture, file relationships, or project content — especially when graphify-out/ exists, where the question should be treated as a graphify query first. Turns any input (code, docs, papers, images, videos) into a persistent knowledge graph with god nodes, community…
skill-creator-primer
You MUST load this skill before the skill-creator skill AND before making ANY change to, or conducting a review of ANY Agent Skill. Triggers include creating, editing, reviewing, or contributing to any part of an Agent Skill (description, frontmatter, body, references, scripts, trigger evals, conflicts, etc).
llm-wiki
Use when building or maintaining a self-contained personal knowledge base (an LLM wiki) in plain markdown. Triggers: ingesting sources into a wiki, querying wiki knowledge, linting wiki health, auditing article claims against their sources, critiquing a wiki source's reasoning, superseding stale knowledge, 'add to…