Firebase Data Connect logo

Firebase Data Connect

Organization
firebase
firebase-data-connect

Builds and deploys Firebase SQL Connect (aka Firebase Data Connect) backends with PostgreSQL securely. Use when designing schemas with tables and relations, writing authorized queries and mutations, configuring real-time data updates, or generating type-safe SDKs. Use when you need a relational database with Firebase, or when the user mentions SQL Connect or Data Connect.

Overview

Publisherfirebase
Repositoryagent-skills
Skill namefirebase-data-connect
Stars
461
Forks
102
Bundled files
16
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 16 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by firebase on GitHub. Read the source before you install it.

Installation

Install the Firebase Data Connect AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/firebase/agent-skills/tree/main/skills/firebase-data-connect-basics
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/firebase/agent-skills.git /tmp/agent-skills
mkdir -p .claude/skills
cp -r /tmp/agent-skills/skills/firebase-data-connect-basics .claude/skills/firebase-data-connect
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Firebase Data Connect in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Firebase Data Connect on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Firebase Data Connect is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Firebase SQL Connect

Firebase SQL Connect is a relational database service using Cloud SQL for PostgreSQL with GraphQL schema, auto-generated queries/mutations, and type-safe SDKs.

[!NOTE] Product Rename: Firebase Data Connect was renamed to Firebase SQL Connect. All instructions, references, and examples in this skill repository referring to "Data Connect" or "Firebase Data Connect" apply to "SQL Connect" and "Firebase SQL Connect" as well.

Project Structure

text
dataconnect/
├── dataconnect.yaml      # Service configuration
├── seed_data.gql         # LOCAL ONLY — prototype/test data
├── schema/
│   └── schema.gql        # Data model (types with @table)
└── connector/
    ├── connector.yaml    # Connector config + SDK generation
    ├── queries.gql       # Queries
    └── mutations.gql     # Mutations

Key Tools for Validation

Rely on these two mechanisms to ensure project correctness:

  1. Review GraphQL Schema: Both user-defined and generated extensions (in .dataconnect/schema/main/).
  2. Validate Operations: Run npx -y firebase-tools@latest dataconnect:compile against the schema.

Operation Strategies: GraphQL vs. Native SQL

Always default to Native GraphQL. Native SQL lacks type safety and bypasses schema-enforced structures. Only use Native SQL when the user explicitly requests it or when the task requires advanced database features.

StrategyWhen to useImplementation
Native GraphQL (Default)Almost all use cases. Standard CRUD, basic filtering/sorting, simple relational joins. Requires full type safety.Auto-generated fields (movie_insert, movies). Strong typing and schema enforcement.
Native SQL (Advanced)PostgreSQL extensions (e.g., PostGIS), window functions (RANK()), complex aggregations, or highly tuned sub-queries.Raw SQL string literals via _select, _execute, etc. Requires strict positional parameters ($1). No type safety.

Development Workflow

Follow this strict workflow to build your application. You must read the linked reference files for each step to understand the syntax and available features.

1. Define Data Model (schema/schema.gql)

Define your GraphQL types, tables, and relationships (which map to a Postgres schema).

Read reference/schema.md for:

  • @table, @col, @default
  • Relationships (@ref, one-to-many, many-to-many)
  • Data types (UUID, Vector, JSON, etc.)

2. Define Authorized Operations (connector/queries.gql, connector/mutations.gql)

Write the queries and mutations your client will use, including authorization logic. SQL Connect is secure by default.

Read reference/operations.md for:

  • Queries: Filtering (where), Ordering (orderBy), Pagination (limit/offset).
  • Mutations: Create (_insert), Update (_update), Delete (_delete).
  • Upserts: Use _upsert to "insert or update" records (CRITICAL for user profiles).
  • Transactions: Use @transaction for multi-step atomic operations. Use _expr: "response.<prevStep>" to pass data between steps.

Read reference/security.md for authorization:

  • @auth(level: ...) for PUBLIC, USER, or NO_ACCESS.
  • @check and @redact for row-level security and validation.

Read reference/realtime.md for real-time subscriptions:

  • @refresh directive for time-based polling and event-driven updates.
  • CEL conditions to scope refresh triggers precisely.

Read reference/native_sql.md for Native SQL operations:

  • Embedding raw SQL with _select, _selectFirst, _execute
  • Strict rules for positional parameters ($1, $2), quoting, and CTEs
  • Advanced PostgreSQL features (PostGIS, Window Functions)

3. Use type-safe SDK in your apps

Generate type-safe code for your client platform.

Configure SDK generation in connector.yaml:

yaml
connectorId: my-connector
generate:
  javascriptSdk:
    outputDir: "../web-app/src/lib/dataconnect"
    package: "@movie-app/dataconnect"
  kotlinSdk:
    outputDir: "../android-app/app/src/main/kotlin/com/example/dataconnect"
    package: "com.example.dataconnect"
  swiftSdk:
    outputDir: "../ios-app/DataConnect"
    package: "DataConnectGenerated"

Generate SDKs:

bash
npx -y firebase-tools@latest dataconnect:sdk:generate

For platform-specific instructions on how to use the generated SDKs, read:


Feature Capability Map

If you need to implement a specific feature, consult the mapped reference file:

FeatureReference FileKey Concepts
Data Modelingreference/schema.md@table, @unique, @index, Relations
Vector Searchreference/search.mdVector, @col(dataType: "vector"), embeddings
Full-Text Searchreference/search.md@searchable, movies_search
Upserting Datareference/operations.md_upsert mutations
Complex Filtersreference/operations.md_or, _and, _not, eq, contains
Transactionsreference/operations.md@transaction, response binding
Environment Configreference/config.mddataconnect.yaml, connector.yaml
Realtime Subscriptionsreference/realtime.md@refresh, subscribe(), auto-refresh
Cloud Functions Integrationreference/cloud_functions.mdonMutationExecuted, triggering events
Data Seeding & Migrationsreference/data_seeding.mdseed_data.gql, _insertMany, Admin SDK bulk
Starter Templatestemplates.mdCRUD, user-owned resources, many-to-many, SDK init

Deployment & CLI

Read reference/config.md for deep dive on configuration.

Follow these patterns based on your current task:

How to initialize SQL Connect in a Firebase project

  1. Understand the app idea. Ask clarification questions if unclear.
  2. Run npx -y firebase-tools@latest init dataconnect.
  3. Validate that the app template and generated SDK are setup.

How to build apps using SQL Connect locally

  1. Start the emulator: npx -y firebase-tools@latest emulators:start --only dataconnect.
  2. Write schema and operations.
  3. Seed local test data into seed_data.gql. Read reference/data_seeding.md.
  4. Run npx -y firebase-tools@latest dataconnect:compile or npx -y firebase-tools@latest dataconnect:sdk:generate to validate them.
  5. Use the operations in your app and build it.

How to deploy SQL Connect to Cloud SQL

  1. Run npx -y firebase-tools@latest deploy --only dataconnect.

Examples

For complete, working code examples of schemas and operations, see examples.md.

For ready-to-use starter templates (CRUD, user-owned resources, many-to-many, YAML configs, SDK init), see templates.md.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Firebase Data Connect AI skill do?

Builds and deploys Firebase SQL Connect (aka Firebase Data Connect) backends with PostgreSQL securely. Use when designing schemas with tables and relations, writing authorized queries and mutations, configuring real-time data updates, or generating type-safe SDKs. Use when you need a relational database with Firebase, or when the user mentions SQL Connect or Data Connect.

Why use Firebase Data Connect on TypingMind?

Because you install it once and use it with any model. Firebase Data Connect is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Firebase Data Connect in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/firebase/agent-skills/tree/main/skills/firebase-data-connect-basics. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Firebase Data Connect?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Firebase Data Connect?

As many as you like. As long as a model supports skills, you can use Firebase Data Connect with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Firebase Data Connect AI skill free?

Yes. It is published on GitHub by firebase under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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