explainx.ainewsletter3.4k
Productivity

firebase-data-connect▌

firebase/agent-skills · updated Apr 8, 2026

MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.

$npx skills add https://github.com/firebase/agent-skills --skill firebase-data-connect
0 commentsdiscussion
summary

PostgreSQL-backed GraphQL backend with auto-generated type-safe SDKs for web, mobile, and Flutter.

  • ›Define GraphQL schema with @table decorators and relationships; Data Connect generates SQL and GraphQL operations automatically
  • ›Write queries and mutations in GraphQL with filtering, ordering, pagination, and upsert support; transactions available via @transaction
  • ›Secure operations with @auth levels (PUBLIC, USER, NO_ACCESS) and row-level controls using @check and @redact
  • ›Generate ty
skill.md

Firebase Data Connect

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

Project Structure

dataconnect/
├── dataconnect.yaml      # Service configuration
├── schema/
│   └── schema.gql        # Data model (types with @table)
└── connector/
    ├── connector.yaml    # Connector config + SDK generation
    ├── queries.gql       # Queries
    └── mutations.gql     # Mutations

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.

Read reference/schema.md for:

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

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

Write the queries and mutations your client will use. Data Connect generates the underlying SQL.

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.

3. Secure Your App (connector/ files)

Add authorization logic closely with your operations.

Read reference/security.md for:

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

4. Generate & Use SDKs

Generate type-safe code for your client platform.

Read reference/sdks.md for:

  • Android (Kotlin), iOS (Swift), Web (TypeScript), Flutter (Dart).
  • How to initialize and call your queries/mutations.
  • Nested Data: See how to access related fields (e.g., movie.reviews).

Feature Capability Map

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

Feature Reference File Key Concepts
Data Modeling reference/schema.md @table, @unique, @index, Relations
Vector Search reference/advanced.md Vector, @col(dataType: "vector")
Full-Text Search reference/advanced.md @searchable
Upserting Data reference/operations.md _upsert mutations
Complex Filters reference/operations.md _or, _and, _not, eq, contains
Transactions reference/operations.md @transaction, response binding
Environment Config reference/config.md dataconnect.yaml, connector.yaml

Deployment & CLI

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

Common commands (run from project root):

# Initialize Data Connect
npx -y firebase-tools@latest init dataconnect

# Start local emulator
npx -y firebase-tools@latest emulators:start --only dataconnect

# Generate SDK code
npx -y firebase-tools@latest dataconnect:sdk:generate

# Deploy to production
npx -y firebase-tools@latest deploy --only dataconnect

Examples

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

how to use firebase-data-connect

How to use firebase-data-connect on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • ›Cursor installed and configured on your development machine
  • ›Node.js version 16.0+ with npm package manager (verify with node --version)
  • ›Active project directory or workspace where you want to add firebase-data-connect
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/firebase/agent-skills --skill firebase-data-connect

The skills CLI fetches firebase-data-connect from GitHub repository firebase/agent-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/firebase-data-connect

Reload or restart Cursor to activate firebase-data-connect. Access the skill through slash commands (e.g., /firebase-data-connect) or your agent's skill management interface.

⚠

Security & Verification Notice

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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

Additional Resources

›View source code on GitHub›Skills CLI documentation›Learn more about Cursor›What are agent skills?

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases▌

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

✓

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

✓

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

✓

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

✓

Save 3-5 hours/week on communication overhead

Implementation Guide▌

Prerequisites

  • ›Claude Desktop or compatible AI client
  • ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • ›Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • ⚠Not validating competitive research—verify facts before sharing
  • ⚠Accepting user stories without involving engineering team
  • ⚠Over-relying on frameworks without qualitative judgment
  • ⚠Not customizing outputs to company culture and communication style
  • ⚠Skipping stakeholder validation of generated requirements

Best Practices▌

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • −Don't publish competitive analysis without fact-checking
  • −Don't finalize user stories without engineering review
  • −Don't make prioritization decisions solely on AI scoring
  • −Don't skip customer validation of generated requirements
  • −Don't ignore company-specific context and culture

💡 Pro Tips

  • ★Provide context: company goals, constraints, customer feedback
  • ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • ★Use skill for 70% generation + 30% customization to company needs

When to Use This▌

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path▌

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.5★★★★★50 reviews
  • ★★★★★Soo Ghosh· Dec 20, 2024

    firebase-data-connect has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • ★★★★★Chaitanya Patil· Dec 12, 2024

    Solid pick for teams standardizing on skills: firebase-data-connect is focused, and the summary matches what you get after install.

  • ★★★★★Soo Iyer· Dec 4, 2024

    Solid pick for teams standardizing on skills: firebase-data-connect is focused, and the summary matches what you get after install.

  • ★★★★★Omar Rao· Nov 23, 2024

    We added firebase-data-connect from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • ★★★★★Noah Garcia· Nov 11, 2024

    firebase-data-connect fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • ★★★★★Maya Chawla· Nov 11, 2024

    I recommend firebase-data-connect for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • ★★★★★Meera Bhatia· Nov 7, 2024

    Useful defaults in firebase-data-connect — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • ★★★★★Piyush G· Nov 3, 2024

    We added firebase-data-connect from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • ★★★★★Soo Kapoor· Oct 26, 2024

    I recommend firebase-data-connect for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • ★★★★★Shikha Mishra· Oct 22, 2024

    firebase-data-connect fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

showing 1-10 of 50

1 / 5