技能库智客分类：安全测试 firebase-ai-logic-basics

Firebase Ai Logic Basics

将Firebase AI Logic(Gemini API)整合到网络应用程序的官方技能. 封面设置,多模式推论,结构化输出,以及安全性.

111201 安装量

官方网址：skills.sh

技能介绍

先看中文介绍；官方 description 原文单独保留，不改写 SKILL.md。

做什么

将Firebase AI Logic(Gemini API)整合到网络应用程序的官方技能. 封面设置,多模式推论,结构化输出,以及安全性.

何时用

官方 description 未单独写出 Use when。按规范，代理会在用户任务与这段 description 的关键词匹配时激活本技能。

代理如何加载

按 Agent Skills 渐进披露：启动时只加载 name 与 description（约 100 token）；任务匹配后才读入整份 SKILL.md 正文；scripts/、references/、assets/ 仅在需要时再读。本文件正文结构：Firebase AI Logic Basics、Overview、Setup & Initialization、Prerequisites、Installation、Core Capabilities。

文件分析

文件分析：除 SKILL.md 外，正文引用了 references/ios_setup.md、references/flutter_setup.md、references/usage_patterns_web.md、references/usage_patterns_android.md，属于带资源的技能包，这些文件按需再读。

官方 description（原文）

Official skill for integrating Firebase AI Logic (Gemini API) into web applications. Covers setup, multimodal inference, structured output, and security.

Firebase AI Logic BasicsOverviewSetup & InitializationPrerequisitesInstallationCore CapabilitiesText-Only GenerationMultimodal (Text + Images/Audio/Video/PDF input)Chat Session (Multi-turn)Streaming ResponsesGenerate Images with Nano BananaSearch Grounding with the built in googleSearch tool

来源分类：skills.sh agent-skill

来源与网址

市场来源：skills.sh

GitHub 完整包

开源榜 Skill 分类

SKILL.md 与 Agent 调用

官方规范 ↗

name: firebase-ai-logic-basics
description: Official skill for integrating Firebase AI Logic (Gemini API) into web applications. Covers setup, multimodal inference, structured output, and security.

发现技能客户端向 Agent 提供名称与描述目录。
匹配与调用用户指定或任务匹配后，载入 SKILL.md 指令。
按需加载按步骤读取参考文档、使用脚本与素材。

指令中引用的文件 · 4

references/ios_setup.md
references/flutter_setup.md
references/usage_patterns_web.md
references/usage_patterns_android.md

以下路径提取自原文；文件是否齐全请以来源仓库中的完整目录为准。

具体调用语法与可用工具以目标 Agent 客户端为准。查看调用机制说明 ↗

安装这个技能

Skills CLI ↗

先选择目标 Agent 和安装范围，保留技能包的附属文件，安装后检查客户端能否发现该技能。

该技能引用了附属文件，请从来源获取完整目录；仅复制 SKILL.md 可能缺少依赖。

交给 Agent 安装

复制安装指令给支持 Agent Skills 的代理，确认其中的目标目录与客户端匹配。

把 Agent Skill「firebase-ai-logic-basics」安装到我的项目：SKILL.md 原文与官方 description 见 https://zicq.com/zh/skills/skl-9e8ce6957337208b-Firebase-Ai-Logic-Basics.html
请存为 .cursor/skills/firebase-ai-logic-basics/SKILL.md 或 .claude/skills/firebase-ai-logic-basics/SKILL.md，frontmatter 的 name 与 description 保持原样，不要改写。
该技能还带 scripts/、references/、assets/ 等文件，请从 https://github.com/firebase/agent-skills 取完整目录，不要只建一个 SKILL.md。

GitHub 完整包 ↗

终端安装 · Skills CLI

需要 Node.js 与 npx。先查看仓库技能列表，确认实际名称。

npx skills add 'https://github.com/firebase/agent-skills' --list

目标客户端安装范围

npx skills add 'https://github.com/firebase/agent-skills' --skill 'firebase-ai-logic-basics'

CLI 会交互选择目标 Agent，默认安装到项目；用户级安装使用 -g。先通过查看命令核对仓库内容，再用 npx skills list 检查已安装技能。

SKILL.md 原文可复制，不会改动源文件

---
name: firebase-ai-logic-basics
description: Official skill for integrating Firebase AI Logic (Gemini API) into web applications. Covers setup, multimodal inference, structured output, and security.
version: 1.0.1
metadata:
  category: AiAndMachineLearning
---

# Firebase AI Logic Basics

## Overview

Firebase AI Logic is a product of Firebase that allows developers to add gen AI
to their mobile and web apps using client-side SDKs. You can call Gemini models
directly from your app without managing a dedicated backend. Firebase AI Logic,
which was previously known as "Vertex AI for Firebase", represents the evolution
of Google's AI integration platform for mobile and web developers.

It supports the two Gemini API providers:

-   **Gemini Developer API**: It has a free tier ideal for prototyping, and
    pay-as-you-go for production
-   **Agent Platform Gemini API** (formerly branded Vertex AI): Ideal for scale
    with enterprise-grade production readiness, requires Blaze plan

Use the Gemini Developer API as a default, and only Agent Platform Gemini API
(formerly branded Vertex AI) if the application requires it.

## Setup & Initialization

### Prerequisites

-   Before starting, ensure you have **Node.js 16+** and npm installed. Install
    them if they aren’t already available.
-   Identify the platform the user is interested in building on prior to
    starting: Android, iOS, Flutter or Web.
-   If their platform is unsupported, Direct the user to Firebase Docs to learn
    how to set up AI Logic for their application (share this link with the user
    https://firebase.google.com/docs/ai-logic/get-started)

### Installation

The library is part of the standard Firebase Web SDK.

`npm install -g firebase@latest`

If you're in a firebase directory (with a firebase.json) the currently selected
project will be marked with "current" using this command:

`npx -y firebase-tools@latest projects:list`

Ensure there's at least one app associated with the current project

`npx -y firebase-tools@latest apps:list`

Initialize AI logic SDK with the init command

`npx -y firebase-tools@latest init ailogic`

This will automatically enable the Gemini Developer API in the Firebase console.

More info in
[Firebase AI Logic Getting Started](https://firebase.google.com/docs/ai-logic/get-started.md.txt)

## Core Capabilities

> [!WARNING] **CRITICAL: Use current model names:** Always check the
> [Firebase AI Logic Models documentation](https://firebase.google.com/docs/ai-logic/models.md.txt)
> for the currently supported model names. Do NOT use `gemini-2.0-pro` or
> `gemini-2.0-flash` or other older models that are shutdown.

### Text-Only Generation

### Multimodal (Text + Images/Audio/Video/PDF input)

Firebase AI Logic allows Gemini models to analyze image files directly from your
app. This enables features like creating captions, answering questions about
images, detecting objects, and categorizing images. Beyond images, Gemini can
analyze other media types like audio, video, and PDFs by passing them as inline
data with their MIME type. For files larger than 20 megabytes (which can cause
HTTP 413 errors as inline data), store them in Cloud Storage for Firebase and
pass their URLs to the Gemini Developer API.

### Chat Session (Multi-turn)

Maintain history automatically using `startChat`.

### Streaming Responses

To improve the user experience by showing partial results as they arrive (like a
typing effect), use `generateContentStream` instead of `generateContent` for
faster display of results.

### Generate Images with Nano Banana

> [!WARNING] **Use current Image model names:** Always check the
> [Firebase AI Logic Models documentation](https://firebase.google.com/docs/ai-logic/models.md.txt)
> for the currently supported image generation (Nano Banana) model names.

-   Requires an upgraded Blaze pay-as-you-go billing plan.

### Search Grounding with the built in googleSearch tool

## Supported Platforms and Frameworks

Supported Platforms and Frameworks include Kotlin and Java for Android, Swift
for iOS, JavaScript for web apps, Dart for Flutter, and C Sharp for Unity.

## Advanced Features

### Structured Output (JSON)

Enforce a specific JSON schema for the response.

### On-Device AI (Hybrid)

Hybrid on-device inference for web apps, where the Firebase Javascript SDK
automatically checks for Gemini Nano's availability (after installation) and
switches between on-device or cloud-hosted prompt execution. This requires
specific steps to enable model usage in the Chrome browser, more info in the
[hybrid-on-device-inference documentation](https://firebase.google.com/docs/ai-logic/hybrid-on-device-inference.md.txt).

## Security & Production

### App Check

> [!WARNING] **Critical Safety Requirement:** In order to use AI Logic safely,
> you MUST set up App Check on your app. This prevents unauthorized clients from
> using your API quota and accessing your backend resources.

See
[App Check with reCAPTCHA Enterprise](https://firebase.google.com/docs/app-check/web/recaptcha-enterprise-provider.md.txt)
for setup instructions.

#### App Check Debug Tokens for Local Development & CI/CD

Because App Check attestation providers (like Play Integrity or DeviceCheck)
reject emulators, simulators, or CI environments, you must use **App Check Debug
Tokens** during development and testing to bypass standard attestation.

##### Local Development (Auto-Generated)

1.  Configure your code's App Check provider to use the debug factory:
    *   **Web**: Set `self.FIREBASE_APPCHECK_DEBUG_TOKEN = true;` before
        initializing App Check.
    *   **Android**: Install `DebugAppCheckProviderFactory.getInstance()`.
    *   **iOS**: Set provider factory to `AppCheckDebugProviderFactory()`.
2.  Run your app in the emulator/localhost.
3.  Look at your runtime debugger console / Logcat logs for the generated UUID:
    *   *Example:* `AppCheck debug token:
        "123a4567-b89c-12d3-e456-789012345678"`
4.  Register this token in the Firebase Console under **Security > App Check >
    Apps > Manage debug tokens**.

##### CI/CD Pipelines (Pre-Provisioned)

1.  Generate and register a new debug token in the Firebase Console under
    **Security > App Check > Apps > Manage debug tokens**.
2.  Add this token string as an encrypted secret in your CI system (e.g.
    `APP_CHECK_DEBUG_TOKEN`).
3.  Configure your build to pass this secret as an environment variable to the
    SDK during test execution (e.g. `self.FIREBASE_APPCHECK_DEBUG_TOKEN =
    process.env.APP_CHECK_DEBUG_TOKEN`).

### Remote Config

Consider that you do not need to hardcode model names (e.g., a specific model
version string). Use Firebase Remote Config to update model versions dynamically
without deploying new client code. See
[Changing model names remotely](https://firebase.google.com/docs/ai-logic/change-model-name-remotely.md.txt)

> [!WARNING] **CRITICAL: Backend Provisioning Required** For all platforms
> (Flutter, Android, iOS, Web), you MUST run `npx firebase-tools init ailogic`
> to provision the service. `flutterfire configure` ONLY handles client
> configuration and does NOT enable the AI service, leading to
> `PERMISSION_DENIED` errors.

## Initialization Code References

| Language,   | Gemini API | Context URL                                     |
: Framework,  : provider   :                                                 :
: Platform    :            :                                                 :
| :---------- | :--------- | :---------------------------------------------- |
| Web Modular | Gemini     | firebase://docs/ai-logic/get-started            |
: API         : Developer  :                                                 :
:             : API        :                                                 :
:             : (Developer :                                                 :
:             : API)       :                                                 :
| iOS (Swift) | Gemini     | [ios_setup.md](references/ios_setup.md)         |
:             : Developer  :                                                 :
:             : API        :                                                 :
| Flutter     | Gemini     | [flutter_setup.md](references/flutter_setup.md) |
: (Dart)      : Developer  :                                                 :
:             : API        :                                                 :

## References

[Web SDK code examples and usage patterns](references/usage_patterns_web.md)
[iOS SDK code examples and usage patterns](references/ios_setup.md)
[Flutter SDK code examples and usage patterns](references/flutter_setup.md)

[Android (Kotlin) SDK usage patterns](references/usage_patterns_android.md)

阅读排版

name: firebase-ai-logic-basics description: Official skill for integrating Firebase AI Logic (Gemini API) into web applications. Covers setup, multimodal inference, structured output, and security. version: 1.0.1 metadata: category: AiAndMachineLearning

Firebase AI Logic Basics

Overview

Firebase AI Logic is a product of Firebase that allows developers to add gen AI to their mobile and web apps using client-side SDKs. You can call Gemini models directly from your app without managing a dedicated backend. Firebase AI Logic, which was previously known as "Vertex AI for Firebase", represents the evolution of Google's AI integration platform for mobile and web developers.

It supports the two Gemini API providers:

Gemini Developer API: It has a free tier ideal for prototyping, and pay-as-you-go for production
Agent Platform Gemini API (formerly branded Vertex AI): Ideal for scale with enterprise-grade production readiness, requires Blaze plan

Use the Gemini Developer API as a default, and only Agent Platform Gemini API (formerly branded Vertex AI) if the application requires it.

Setup & Initialization

Prerequisites

Before starting, ensure you have Node.js 16+ and npm installed. Install them if they aren’t already available.
Identify the platform the user is interested in building on prior to starting: Android, iOS, Flutter or Web.
If their platform is unsupported, Direct the user to Firebase Docs to learn how to set up AI Logic for their application (share this link with the user https://firebase.google.com/docs/ai-logic/get-started)

Installation

The library is part of the standard Firebase Web SDK.

npm install -g firebase@latest

If you're in a firebase directory (with a firebase.json) the currently selected project will be marked with "current" using this command:

npx -y firebase-tools@latest projects:list

Ensure there's at least one app associated with the current project

npx -y firebase-tools@latest apps:list

Initialize AI logic SDK with the init command

npx -y firebase-tools@latest init ailogic

This will automatically enable the Gemini Developer API in the Firebase console.

More info in Firebase AI Logic Getting Started

Core Capabilities

[!WARNING] CRITICAL: Use current model names: Always check the Firebase AI Logic Models documentation for the currently supported model names. Do NOT use gemini-2.0-pro or gemini-2.0-flash or other older models that are shutdown.

Text-Only Generation

Multimodal (Text + Images/Audio/Video/PDF input)

Firebase AI Logic allows Gemini models to analyze image files directly from your app. This enables features like creating captions, answering questions about images, detecting objects, and categorizing images. Beyond images, Gemini can analyze other media types like audio, video, and PDFs by passing them as inline data with their MIME type. For files larger than 20 megabytes (which can cause HTTP 413 errors as inline data), store them in Cloud Storage for Firebase and pass their URLs to the Gemini Developer API.

Chat Session (Multi-turn)

Maintain history automatically using startChat.

Streaming Responses

To improve the user experience by showing partial results as they arrive (like a typing effect), use generateContentStream instead of generateContent for faster display of results.

Generate Images with Nano Banana

[!WARNING] Use current Image model names: Always check the Firebase AI Logic Models documentation for the currently supported image generation (Nano Banana) model names.

Requires an upgraded Blaze pay-as-you-go billing plan.

Search Grounding with the built in googleSearch tool

Supported Platforms and Frameworks

Supported Platforms and Frameworks include Kotlin and Java for Android, Swift for iOS, JavaScript for web apps, Dart for Flutter, and C Sharp for Unity.

Advanced Features

Structured Output (JSON)

Enforce a specific JSON schema for the response.

On-Device AI (Hybrid)

Hybrid on-device inference for web apps, where the Firebase Javascript SDK automatically checks for Gemini Nano's availability (after installation) and switches between on-device or cloud-hosted prompt execution. This requires specific steps to enable model usage in the Chrome browser, more info in the hybrid-on-device-inference documentation.

Security & Production

App Check

[!WARNING] Critical Safety Requirement: In order to use AI Logic safely, you MUST set up App Check on your app. This prevents unauthorized clients from using your API quota and accessing your backend resources.

See App Check with reCAPTCHA Enterprise for setup instructions.

App Check Debug Tokens for Local Development & CI/CD

Because App Check attestation providers (like Play Integrity or DeviceCheck) reject emulators, simulators, or CI environments, you must use App Check Debug Tokens during development and testing to bypass standard attestation.

Local Development (Auto-Generated)

Configure your code's App Check provider to use the debug factory:
- Web: Set self.FIREBASE_APPCHECK_DEBUG_TOKEN = true; before initializing App Check.
- Android: Install DebugAppCheckProviderFactory.getInstance().
- iOS: Set provider factory to AppCheckDebugProviderFactory().
Run your app in the emulator/localhost.
Look at your runtime debugger console / Logcat logs for the generated UUID:
- Example: AppCheck debug token: "123a4567-b89c-12d3-e456-789012345678"
Register this token in the Firebase Console under Security > App Check > Apps > Manage debug tokens.

CI/CD Pipelines (Pre-Provisioned)

Generate and register a new debug token in the Firebase Console under Security > App Check > Apps > Manage debug tokens.
Add this token string as an encrypted secret in your CI system (e.g. APP_CHECK_DEBUG_TOKEN).
Configure your build to pass this secret as an environment variable to the SDK during test execution (e.g. self.FIREBASE_APPCHECK_DEBUG_TOKEN = process.env.APP_CHECK_DEBUG_TOKEN).

Remote Config

Consider that you do not need to hardcode model names (e.g., a specific model version string). Use Firebase Remote Config to update model versions dynamically without deploying new client code. See Changing model names remotely

[!WARNING] CRITICAL: Backend Provisioning Required For all platforms (Flutter, Android, iOS, Web), you MUST run npx firebase-tools init ailogic to provision the service. flutterfire configure ONLY handles client configuration and does NOT enable the AI service, leading to PERMISSION_DENIED errors.

Initialization Code References