Gemini API Integration

Overview

This public intake copy packages

plugins/antigravity-awesome-skills/skills/gemini-api-integration

https://github.com/sickn33/antigravity-awesome-skills

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses

metadata.json

ORIGIN.md

Gemini API Integration

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Error Handling, Limitations.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• 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

Operating Table

metadata.json

ORIGIN.md

SKILL.md

SKILL.md

## Related Skills

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Model - Best For - Speed - Cost
2. gemini-1.5-flash - High-throughput, cost-sensitive tasks - Fast - Low
3. gemini-1.5-pro - Complex reasoning, long context - Medium - Medium
4. gemini-2.0-flash - Latest fast model, multimodal - Very Fast - Low
5. gemini-2.0-pro - Most capable, advanced tasks - Slow - High
6. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
7. Read the overview and provenance files before loading any copied upstream support files.

Imported Workflow Notes

Imported: 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());

5. Function Calling / Tool Use

const tools = [{
  functionDeclarations: [{
    name: "get_weather",
    description: "Get current weather for a city",
    parameters: {
      type: "OBJECT",
      properties: {
        city: { type: "STRING", description: "City name" },
      },
      required: ["city"],
    },
  }],
}];

const model = genAI.getGenerativeModel({ model: "gemini-1.5-pro", tools });
const result = await model.generateContent("What's the weather in Mumbai?");

const call = result.response.functionCalls()?.[0];
if (call) {
  // Execute the actual function
  const weatherData = await getWeather(call.args.city);
  // Send result back to model
}

6. Multi-turn Chat

const chat = model.startChat({
  history: [
    { role: "user", parts: [{ text: "You are a helpful coding assistant." }] },
    { role: "model", parts: [{ text: "Sure! I'm ready to help with code." }] },
  ],
});

const response = await chat.sendMessage("How do I reverse a string in Python?");
console.log(response.response.text());

7. Model Selection Guide

gemini-1.5-flash

gemini-1.5-pro

gemini-2.0-flash

gemini-2.0-pro

Imported: 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.

Imported: Error Handling

try {
  const result = await model.generateContent(prompt);
  return result.response.text();
} catch (error) {
  if (error.status === 429) {
    // Rate limited — wait and retry with exponential backoff
    await new Promise(r => setTimeout(r, 2 ** retryCount * 1000));
  } else if (error.status === 400) {
    // Invalid request — check prompt or parameters
    console.error("Invalid request:", error.message);
  } else {
    throw error;
  }
}

Examples

Example 1: Ask for the upstream workflow directly

Use @gemini-api-integration-v2 to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @gemini-api-integration-v2 against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @gemini-api-integration-v2 for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @gemini-api-integration-v2 using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• ✅ Do: Use gemini-1.5-flash for most tasks — it's fast and cost-effective
• ✅ Do: Always stream responses for user-facing chat UIs to reduce perceived latency
• ✅ Do: Store API keys in environment variables, never hard-code them
• ✅ Do: Implement exponential backoff for rate limit (429) errors
• ✅ Do: Use systemInstruction to set persistent model behavior
• ❌ Don't: Use gemini-pro for simple tasks — Flash is cheaper and faster
• ❌ Don't: Send large base64 images inline for files > 20MB — use File API instead

Imported Operating Notes

Imported: Best Practices

• ✅ Do: Use

  gemini-1.5-flash

  for most tasks — it's fast and cost-effective
• ✅ Do: Always stream responses for user-facing chat UIs to reduce perceived latency
• ✅ Do: Store API keys in environment variables, never hard-code them
• ✅ Do: Implement exponential backoff for rate limit (429) errors
• ✅ Do: Use

  systemInstruction

  to set persistent model behavior
• ❌ Don't: Use

  gemini-pro

  for simple tasks — Flash is cheaper and faster
• ❌ Don't: Send large base64 images inline for files > 20MB — use File API instead
• ❌ Don't: Ignore safety ratings in responses for production apps

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in

plugins/antigravity-awesome-skills/skills/gemini-api-integration

metadata.json

ORIGIN.md

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated

SKILL.md

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Imported Troubleshooting Notes

Imported: Troubleshooting

Problem:

API_KEY_INVALID

GEMINI_API_KEY

Problem: Response blocked by safety filters Solution: Check

result.response.promptFeedback.blockReason

Problem: Slow response times Solution: Switch to

gemini-1.5-flash

Problem:

RESOURCE_EXHAUSTED

Related Skills

• @game-design-v2

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @gdb-cli-v2

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @gdpr-data-handling-v2

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @gemini-api-dev-v2

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

references

references/n/a

examples

examples/n/a

scripts

scripts/n/a

agents

agents/n/a

assets

assets/n/a

Imported Reference Notes

Imported: Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.