Integrate Gemini AI into Firebase apps with text generation, streaming, and image analysis capabilities.
Works with
Supports text generation, streaming responses, and multimodal (image + text) analysis through Firebase's Gemini integration
Includes SDK setup for web (JavaScript/TypeScript) with Firebase initialization and model configuration
Provides security rules templates for protecting AI request logs and enforces API key management via environment variables
Built-in best practices cover
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionfirebase-ai-logicExecute the skills CLI command in your project's root directory to begin installation:
Fetches firebase-ai-logic from supercent-io/skills-template 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 firebase-ai-logic. Access via /firebase-ai-logic 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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# Install Firebase CLI
npm install -g firebase-tools
# Login
firebase login
# Initialize project
firebase init
In Firebase Console:
Web (JavaScript):
npm install firebase @anthropic-ai/sdk
Initialization code:
import { initializeApp } from 'firebase/app';
import { getAI, getGenerativeModel } from 'firebase/ai';
const firebaseConfig = {
apiKey: "YOUR_API_KEY",
authDomain: "YOUR_PROJECT.firebaseapp.com",
projectId: "YOUR_PROJECT_ID",
};
const app = initializeApp(firebaseConfig);
const ai = getAI(app);
const model = getGenerativeModel(ai, { model: "gemini-2.0-flash" });
Text generation:
async function generateContent(prompt: string) {
const result = await model.generateContent(prompt);
return result.response.text();
}
// Example usage
const response = await generateContent("Explain the key features of Firebase.");
console.log(response);
Streaming response:
async function streamContent(prompt: string) {
const result = await model.generateContentStream(prompt);
for await (const chunk of result.stream) {
const text = chunk.text();
console.log(text);
}
}
Multimodal (image + text):
async function analyzeImage(imageUrl: string, prompt: string) {
const imagePart = {
inlineData: {
data: await fetchImageAsBase64(imageUrl),
mimeType: "image/jpeg"
}
};
const result = await model.generateContent([prompt, imagePart]);
return result.response.text();
}
Firebase Security Rules:
rules_version = '2';
service cloud.firestore {
match /databases/{database}/documents {
// Protect AI request logs
match /ai_logs/{logId} {
allow read: if request.auth != null && request.auth.uid == resource.data.userId;
allow create: if request.auth != null;
}
}
}
project/
├── src/
│ ├── ai/
│ │ ├── client.ts # Initialize AI client
│ │ ├── prompts.ts # Prompt templates
│ │ └── handlers.ts # AI handlers
│ └── firebase/
│ └── config.ts # Firebase config
├── firebase.json
└── .env.local # API key (gitignored)
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.
supercent-io/skills-template
supercent-io/skills-template
supercent-io/skills-template
supercent-io/skills-template
supercent-io/skills-template
davila7/claude-code-templates
I recommend firebase-ai-logic for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Solid pick for teams standardizing on skills: firebase-ai-logic is focused, and the summary matches what you get after install.
Useful defaults in firebase-ai-logic — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Registry listing for firebase-ai-logic matched our evaluation — installs cleanly and behaves as described in the markdown.
firebase-ai-logic has been reliable in day-to-day use. Documentation quality is above average for community skills.
Keeps context tight: firebase-ai-logic is the kind of skill you can hand to a new teammate without a long onboarding doc.
firebase-ai-logic is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
firebase-ai-logic fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
We added firebase-ai-logic from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Solid pick for teams standardizing on skills: firebase-ai-logic is focused, and the summary matches what you get after install.
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