sql-queries▌
phuryn/pm-skills · updated Apr 8, 2026
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Transform natural language requirements into optimized SQL queries across multiple database platforms. This skill helps product managers, analysts, and engineers generate accurate queries without manual syntax work.
SQL Query Generator
Purpose
Transform natural language requirements into optimized SQL queries across multiple database platforms. This skill helps product managers, analysts, and engineers generate accurate queries without manual syntax work.
How It Works
Step 1: Understand Your Database Schema
- If you provide a schema file (SQL, documentation, or diagram description), I will read and analyze it
- Extract table names, column definitions, data types, and relationships
- Identify primary keys, foreign keys, and indexing strategies
Step 2: Process Your Request
- Clarify the exact data you need to retrieve or analyze
- Confirm the SQL dialect (BigQuery, PostgreSQL, MySQL, Snowflake, etc.)
- Ask for any additional requirements (filters, aggregations, sorting)
Step 3: Generate Optimized Query
- Write efficient SQL that leverages your database structure
- Include comments explaining complex logic
- Add performance considerations for large datasets
- Provide alternative approaches if applicable
Step 4: Explain and Test
- Explain the query logic in plain English
- Suggest how to test or validate results
- Offer tips for performance optimization
- If you want, generate a test script or sample data
Usage Examples
Example 1: Query from Schema File
Upload your database_schema.sql file and say:
"Generate a query to find users who signed up in the last 30 days
and had at least 5 active sessions"
Example 2: Query from Diagram Description
"Here's my database: Users table (id, email, created_at), Sessions table
(id, user_id, timestamp, duration). Generate a query for average session
duration per user in January 2026."
Example 3: Complex Analysis Query
"Create a BigQuery query to analyze our revenue by region and customer tier,
including year-over-year growth rates."
Key Capabilities
- Multi-Dialect Support: Works with BigQuery, PostgreSQL, MySQL, Snowflake, SQL Server
- File Reading: Reads schema files, SQL dumps, and data documentation
- Query Optimization: Suggests indexes, partitioning, and performance improvements
- Explanation: Breaks down queries for learning and documentation
- Testing: Can generate test queries and sample data scripts
- Script Execution: Create executable SQL scripts for your database
Tips for Best Results
- Provide context: Share your database schema or structure
- Be specific: Clearly describe what data you need and any filters
- Mention database: Specify which SQL dialect you're using
- Include constraints: Mention data volume, time ranges, and performance needs
- Request format: Ask for the query result format if you need specific output
Output Format
You'll receive:
- SQL Query: Production-ready SQL code with comments
- Explanation: What the query does and how it works
- Performance Notes: Optimization tips and considerations
- Test Script (if requested): Sample data and validation queries
Further Reading
How to use sql-queries 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-queries
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches sql-queries from GitHub repository phuryn/pm-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-queries. Access the skill through slash commands (e.g., /sql-queries) 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
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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
4.6★★★★★25 reviews- ★★★★★Ganesh Mohane· Dec 24, 2024
Useful defaults in sql-queries — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Shikha Mishra· Dec 24, 2024
Solid pick for teams standardizing on skills: sql-queries is focused, and the summary matches what you get after install.
- ★★★★★Ren Shah· Dec 20, 2024
sql-queries fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Sakshi Patil· Nov 15, 2024
sql-queries is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Sakura Ndlovu· Nov 11, 2024
Registry listing for sql-queries matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Chaitanya Patil· Oct 6, 2024
Keeps context tight: sql-queries is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Valentina Anderson· Oct 2, 2024
sql-queries reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Meera Ghosh· Sep 5, 2024
sql-queries fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Maya Ghosh· Aug 24, 2024
We added sql-queries from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Kwame Harris· Aug 12, 2024
Useful defaults in sql-queries — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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