跳到正文
cccskills
免费在 GitHub 公开

firebase-basics

Provides foundational Firebase CLI setup, CLI installation, version checks (`firebase-tools@latest --version`), CLI login (including --no-localhost), project creation, project selection (`firebase use`), and app config file downloads (`google-services.json`, `GoogleService-Info.plist`). Use ONLY for CLI login, project creation/switching, or downloading app config files. Don't use for Firebase Hosting deploy, Firestore, Auth, App Hosting, Data Connect, Crashlytics, or Remote Config.

中文简介正在准备中。当前显示原文说明。

集成工具Firebase
查看安装方法

包含的文件(20)

  • SKILL.md7.4 KB
  • references/android_setup.md1.8 KB
  • references/firebase-cli-guide.md486 B
  • references/firebase-service-init.md705 B
  • references/flutter_setup.md4.8 KB
  • references/ios_setup.md4.0 KB
  • references/local-env-setup.md2.8 KB
  • references/refresh/android_studio.md1.3 KB
  • references/refresh/antigravity.md2.1 KB
  • references/refresh/claude.md386 B
  • references/refresh/gemini-cli.md490 B
  • references/refresh/other-agents.md2.3 KB
  • references/setup/android_studio.md658 B
  • references/setup/antigravity.md3.4 KB
  • references/setup/claude_code.md1.9 KB
  • references/setup/cursor.md3.1 KB
  • references/setup/gemini_cli.md2.0 KB
  • references/setup/github_copilot.md3.4 KB
  • references/setup/other_agents.md3.5 KB
  • references/web_setup.md1.6 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Firebase Basics

Prerequisites

Complete these setup steps before proceeding:

  1. Local Environment Setup: Verify the environment is properly set up so we can use Firebase tools:

  2. Authentication: Ensure you are logged in to Firebase so that commands have the correct permissions. Run npx -y firebase-tools@latest login. For environments without a browser (e.g., remote shells), use npx -y firebase-tools@latest login --no-localhost.

    • The command should output the current user.
    • If you are not logged in, follow the interactive instructions from this command to authenticate.
  3. Active Project: Most Firebase tasks require an active project context.

    [!IMPORTANT] For Agents: Before proceeding with project configuration, you MUST pause and ask the developer if they prefer to:

    1. Provide an existing Firebase Project ID, or
    2. Create a new Firebase project.
    • If using an existing Project ID:

      1. Check the current project by running npx -y firebase-tools@latest use.
      2. If the command outputs Active Project: <project-id>, confirm with the user if this is the intended project.
      3. If not, or if no project is active, set the project provided by the user:
      npx -y firebase-tools@latest use <PROJECT_ID>
      
    • If creating a new project: Run the following command to create it:

      npx -y firebase-tools@latest projects:create <project-id> --display-name "<display-name>"
      

      Note: The <project-id> must be 6-30 characters, lowercase, and can contain digits and hyphens. It must be globally unique.

Firebase Usage Principles

Adhere to these principles:

  1. Use npx for CLI commands: To ensure you always use the latest version of the Firebase CLI, always prepend commands with npx -y firebase-tools@latest instead of just firebase. For example, use npx -y firebase-tools@latest --version. NEVER suggest the naked firebase command as an alternative.
  2. Prioritize official knowledge: For any Firebase-related knowledge, consult the developerknowledge_search_documents MCP tool before falling back to Google Search or your internal knowledge base. Including "Firebase" in your search query significantly improves relevance.
  3. Follow Agent Skills for implementation guidance: Skills provide opinionated workflows (CUJs), security rules, and best practices. Always consult them to understand how to implement Firebase features correctly instead of relying on general knowledge.
  4. Use Firebase MCP Server tools instead of direct API calls: Whenever you need to interact with remote Firebase APIs (such as fetching Crashlytics logs or executing Data Connect queries), use the tools provided by the Firebase MCP Server instead of attempting manual API calls.
  5. Keep Plugin / Agent Skills updated: Since Firebase best practices evolve quickly, regularly check for and install updates to their Firebase plugin or Agent Skills. Similarly, if you encounter issues with outdated tools or commands, follow the steps below based on your agent environment:
  6. Automate Config File Retrieval: When setting up iOS or Android apps, do NOT direct users to the Firebase Console to download google-services.json or GoogleService-Info.plist. Instead, use the Firebase CLI to fetch the config programmatically:
    • For Android: npx -y firebase-tools@latest apps:sdkconfig ANDROID <APP_ID> --project <PROJECT_ID>
    • For iOS: npx -y firebase-tools@latest apps:sdkconfig IOS <APP_ID> --project <PROJECT_ID> Save the output to the appropriate location (e.g., app/google-services.json for Android, or a path to be linked by xcode-project-setup for iOS).

References

Common Issues

  • Login Issues: If the browser fails to open during the login step, use npx -y firebase-tools@latest login --no-localhost instead.

  • Genkit: If using Genkit, install the skills:

    npx skills add genkit-ai/skills
    

评价

还没有评价。欢迎分享你的使用感受。

评论

无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。

还没有评论。来写第一条吧。

提交后会立即公开。请勿填写邮箱、电话等个人信息。URL 最多 2 个。

0 / 1000 字

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

同一仓库的技能

Configures best-practice alerting policies for AI agents using OpenTelemetry (OTel) metrics, generating output as Terraform (.tf) configuration files. Use when analyzing, writing, or deploying alerting policies to monitor agent latency, error rates, token usage, and quality metrics. Don't use for standard infrastructure monitoring unrelated to AI agents, or when the agent is not instrumented with OpenTelemetry (for Reliability, Cost, Safety, Security alerts). NOTE: Reliability, Cost, Safety, and Security alerts use generic OTel metrics and work across runtimes (such as Cloud Run, Vertex AI). Quality alerts rely on Vertex AI Online Monitors and are strictly bound to Vertex AI deployments.

中文简介正在准备中。当前显示原文说明。

vaila-multimodaltoolbox/vaila212026年10月20日 更新

Deploy open models or custom weights from Model Garden to Agent Platform endpoints, check the status of an in-progress deployment operation, or clean up resources by undeploying models and deleting endpoints. Use when asked to actively deploy a model, list the Model Garden CATALOG of available models, check if a specific model is deployable (`gcloud ai model-garden models list-deployment-config`), query deployment cost, troubleshoot deployment errors (like quota limits), or undeploy/clean up endpoints. Also use when copying and deploying a 1P Tuned Model. Don't use for pure listing/discovery questions of the form "is X deployed?", "list my endpoints", or "which regions have models running?" — for those use `agent-platform-endpoint-management`. Don't use for public Vertex AI deployments (use the `vertex-deploy` skill) or for running model evaluations (use the `agent-platform-eval-flywheel` skill).

中文简介正在准备中。当前显示原文说明。

vaila-multimodaltoolbox/vaila212026年10月20日 更新

Manages Agent Platform serving endpoints. Use when you need to create, list, describe, update, or delete serving endpoints for model deployment on Agent Platform. Also use when troubleshooting endpoint permission, quota, or resource busy errors. Don't use for deploying models to endpoints or for running model evaluations.

中文简介正在准备中。当前显示原文说明。

vaila-multimodaltoolbox/vaila212026年10月20日 更新

Measures and improves the quality of AI models and agents on Google Cloud using the Eval Quality Flywheel methodology. Use when evaluating an agent or model, building an eval dataset, picking or writing evaluation metrics, analyzing failures, comparing results before and after a fix, or when guidance is needed on Agent Platform eval methodology — including dataset schema, LLM-as-judge scoring, and common failure causes. For fine-tuning, use agent-platform-tuning. For general production deployment, use agent-platform-deploy.

中文简介正在准备中。当前显示原文说明。

vaila-multimodaltoolbox/vaila212026年10月20日 更新

Connects to and performs inference with Google Cloud Agent Platform GenAI models, including First-Party Gemini models and Third-Party OpenMaaS models (Llama, DeepSeek, Qwen, etc.). Use when asked to perform inference, ask a model a question, run a test prompt, execute chat completions, or generate code for calling Gemini or OpenMaaS models, authenticate with GenAI SDK, OpenAI SDK, or legacy Agent Platform SDK, configure base URLs and global/regional endpoints, or troubleshoot 429 Resource Exhausted (DSQ), 400 User Validation, or 404 Not Found errors. Don't use for deploying models to endpoints or for running model evaluations.

中文简介正在准备中。当前显示原文说明。

vaila-multimodaltoolbox/vaila212026年10月20日 更新

Guides agents and users through migrating from Gemini API in Google AI Studio to Gemini Enterprise Agent Platform (formerly Vertex AI). Use this skill when moving applications to Google Cloud, to leverage Cloud credits, or to unify inferencing with other Cloud infrastructure (IAM, billing, telemetry).

中文简介正在准备中。当前显示原文说明。

vaila-multimodaltoolbox/vaila212026年10月20日 更新

查看 vaila-multimodaltoolbox 的全部技能

报告此技能的问题