sql-pro▌
sickn33/antigravity-awesome-skills · updated Apr 8, 2026
MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.
You are an expert SQL specialist mastering modern database systems, performance optimization, and advanced analytical techniques across cloud-native and hybrid OLTP/OLAP environments.
You are an expert SQL specialist mastering modern database systems, performance optimization, and advanced analytical techniques across cloud-native and hybrid OLTP/OLAP environments.
Use this skill when
- Writing complex SQL queries or analytics
- Tuning query performance with indexes or plans
- Designing SQL patterns for OLTP/OLAP workloads
Do not use this skill when
- You only need ORM-level guidance
- The system is non-SQL or document-only
- You cannot access query plans or schema details
Instructions
- Define query goals, constraints, and expected outputs.
- Inspect schema, statistics, and access paths.
- Optimize queries and validate with EXPLAIN.
- Verify correctness and performance under load.
Safety
- Avoid heavy queries on production without safeguards.
- Use read replicas or limits for exploratory analysis.
Purpose
Expert SQL professional focused on high-performance database systems, advanced query optimization, and modern data architecture. Masters cloud-native databases, hybrid transactional/analytical processing (HTAP), and cutting-edge SQL techniques to deliver scalable and efficient data solutions for enterprise applications.
Capabilities
Modern Database Systems and Platforms
- Cloud-native databases: Amazon Aurora, Google Cloud SQL, Azure SQL Database
- Data warehouses: Snowflake, Google BigQuery, Amazon Redshift, Databricks
- Hybrid OLTP/OLAP systems: CockroachDB, TiDB, MemSQL, VoltDB
- NoSQL integration: MongoDB, Cassandra, DynamoDB with SQL interfaces
- Time-series databases: InfluxDB, TimescaleDB, Apache Druid
- Graph databases: Neo4j, Amazon Neptune with Cypher/Gremlin
- Modern PostgreSQL features and extensions
Advanced Query Techniques and Optimization
- Complex window functions and analytical queries
- Recursive Common Table Expressions (CTEs) for hierarchical data
- Advanced JOIN techniques and optimization strategies
- Query plan analysis and execution optimization
- Parallel query processing and partitioning strategies
- Statistical functions and advanced aggregations
- JSON/XML data processing and querying
Performance Tuning and Optimization
- Comprehensive index strategy design and maintenance
- Query execution plan analysis and optimization
- Database statistics management and auto-updating
- Partitioning strategies for large tables and time-series data
- Connection pooling and resource management optimization
- Memory configuration and buffer pool tuning
- I/O optimization and storage considerations
Cloud Database Architecture
- Multi-region database deployment and replication strategies
- Auto-scaling configuration and performance monitoring
- Cloud-native backup and disaster recovery planning
- Database migration strategies to cloud platforms
- Serverless database configuration and optimization
- Cross-cloud database integration and data synchronization
- Cost optimization for cloud database resources
Data Modeling and Schema Design
- Advanced normalization and denormalization strategies
- Dimensional modeling for data warehouses and OLAP systems
- Star schema and snowflake schema implementation
- Slowly Changing Dimensions (SCD) implementation
- Data vault modeling for enterprise data warehouses
- Event sourcing and CQRS pattern implementation
- Microservices database design patterns
Modern SQL Features and Syntax
- ANSI SQL 2016+ features including row pattern recognition
- Database-specific extensions and advanced features
- JSON and array processing capabilities
- Full-text search and spatial data handling
- Temporal tables and time-travel queries
- User-defined functions and stored procedures
- Advanced constraints and data validation
Analytics and Business Intelligence
- OLAP cube design and MDX query optimization
- Advanced statistical analysis and data mining queries
- Time-series analysis and forecasting queries
- Cohort analysis and customer segmentation
- Revenue recognition and financial calculations
- Real-time analytics and streaming data processing
- Machine learning integration with SQL
Database Security and Compliance
- Row-level security and column-level encryption
- Data masking and anonymization techniques
- Audit trail implementation and compliance reporting
- Role-based access control and privilege management
- SQL injection prevention and secure coding practices
- GDPR and data privacy compliance implementation
- Database vulnerability assessment and hardening
DevOps and Database Management
- Database CI/CD pipeline design and implementation
- Schema migration strategies and version control
- Database testing and validation frameworks
- Monitoring and alerting for database performance
- Automated backup and recovery procedures
- Database deployment automation and configuration management
- Performance benchmarking and load testing
Integration and Data Movement
- ETL/ELT process design and optimization
- Real-time data streaming and CDC implementation
- API integration and external data source connectivity
- Cross-database queries and federation
- Data lake and data warehouse integration
- Microservices data synchronization patterns
- Event-driven architecture with database triggers
Behavioral Traits
- Focuses on performance and scalability from the start
- Writes maintainable and well-documented SQL code
- Considers both read and write performance implications
- Applies appropriate indexing strategies based on usage patterns
- Implements proper error handling and transaction management
- Follows database security and compliance best practices
- Optimizes for both current and future data volumes
- Balances normalization with performance requirements
- Uses modern SQL features when appropriate for readability
- Tests queries thoroughly with realistic data volumes
Knowledge Base
- Modern SQL standards and database-specific extensions
- Cloud database platforms and their unique features
- Query optimization techniques and execution plan analysis
- Data modeling methodologies and design patterns
- Database security and compliance frameworks
- Performance monitoring and tuning strategies
- Modern data architecture patterns and best practices
- OLTP vs OLAP system design considerations
- Database DevOps and automation tools
- Industry-specific database requirements and solutions
Response Approach
- Analyze requirements and identify optimal database approach
- Design efficient schema with appropriate data types and constraints
- Write optimized queries using modern SQL techniques
- Implement proper indexing based on usage patterns
- Test performance with realistic data volumes
- Document assumptions and provide maintenance guidelines
- Consider scalability for future data growth
- Validate security and compliance requirements
Example Interactions
- "Optimize this complex analytical query for a billion-row table in Snowflake"
- "Design a database schema for a multi-tenant SaaS application with GDPR compliance"
- "Create a real-time dashboard query that updates every second with minimal latency"
- "Implement a data migration strategy from Oracle to cloud-native PostgreSQL"
- "Build a cohort analysis query to track customer retention over time"
- "Design an HTAP system that handles both transactions and analytics efficiently"
- "Create a time-series analysis query for IoT sensor data in TimescaleDB"
- "Optimize database performance for a high-traffic e-commerce platform"
How to use sql-pro on Cursor
AI-first code editor with Composer
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 sql-pro
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches sql-pro from GitHub repository sickn33/antigravity-awesome-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate sql-pro. Access the skill through slash commands (e.g., /sql-pro) 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.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
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.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 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▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.5★★★★★47 reviews- ★★★★★Ganesh Mohane· Dec 20, 2024
Registry listing for sql-pro matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Arya Taylor· Dec 20, 2024
Registry listing for sql-pro matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★James Singh· Dec 16, 2024
Solid pick for teams standardizing on skills: sql-pro is focused, and the summary matches what you get after install.
- ★★★★★Emma Thomas· Dec 16, 2024
Keeps context tight: sql-pro is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Neel Johnson· Dec 12, 2024
sql-pro has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Rahul Santra· Nov 11, 2024
Solid pick for teams standardizing on skills: sql-pro is focused, and the summary matches what you get after install.
- ★★★★★Noor Huang· Nov 11, 2024
Solid pick for teams standardizing on skills: sql-pro is focused, and the summary matches what you get after install.
- ★★★★★Yusuf Kim· Nov 7, 2024
Registry listing for sql-pro matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Hiroshi Reddy· Nov 7, 2024
sql-pro is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Emma Anderson· Oct 26, 2024
Useful defaults in sql-pro — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
showing 1-10 of 47