PostgreSQL-backed GraphQL backend with auto-generated type-safe SDKs for web, mobile, and Flutter.
Works with
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
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionfirebase-data-connectExecute the skills CLI command in your project's root directory to begin installation:
Fetches firebase-data-connect from firebase/agent-skills 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-data-connect. Access via /firebase-data-connect 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
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
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
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Firebase Data Connect is a relational database service using Cloud SQL for PostgreSQL with GraphQL schema, auto-generated queries/mutations, and type-safe SDKs.
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
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.
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.)
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
_upsertto "insert or update" records (CRITICAL for user profiles).- Transactions: use
@transactionfor multi-step atomic operations.
connector/ files)Add authorization logic closely with your operations.
Read reference/security.md for:
@auth(level: ...)for PUBLIC, USER, or NO_ACCESS.@checkand@redactfor row-level security and validation.
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).
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 |
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
For complete, working code examples of schemas and operations, see examples.md.
Make data-driven prioritization decisions faster
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
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
firebase-data-connect has been reliable in day-to-day use. Documentation quality is above average for community skills.
Solid pick for teams standardizing on skills: firebase-data-connect is focused, and the summary matches what you get after install.
Solid pick for teams standardizing on skills: firebase-data-connect is focused, and the summary matches what you get after install.
We added firebase-data-connect from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
firebase-data-connect fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
I recommend firebase-data-connect for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Useful defaults in firebase-data-connect — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
We added firebase-data-connect from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
I recommend firebase-data-connect for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
firebase-data-connect fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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