Covers five major dialects: PostgreSQL, Snowflake, BigQuery, Redshift, and Databricks with dialect-specific syntax for date/time, string functions, arrays, and JSON handling
Includes common analytical patterns: window functions, CTEs, cohort retention, funnel analysis, and deduplication with ready-to-use examples
Provides performance optimization tips per dialect, such as clustering keys in Snowflake, partition p
Fetches sql-queries from anthropics/knowledge-work-plugins and configures it for Cursor.
3
Select Cursor when prompted
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
◆ Which agents do you want to install to?
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│ ── Universal (.agents/skills) ────────────────
│ · Cline · Codex · Goose · Windsurf
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4
Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/sql-queries
Restart Cursor to activate sql-queries. Access via /sql-queries in your agent's command palette.
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Security 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 environment. Always review source, verify the publisher, and test in isolation before production.
-- Current date/timeCURRENT_DATE,CURRENT_TIMESTAMP,NOW()-- Date arithmeticdate_column +INTERVAL'7 days'date_column -INTERVAL'1 month'-- Truncate to periodDATE_TRUNC('month', created_at)-- Extract partsEXTRACT(YEARFROM created_at)EXTRACT(DOW FROM created_at)-- 0=Sunday-- FormatTO_CHAR(created_at,'YYYY-MM-DD')
Create indexes on frequently filtered/joined columns
Use EXISTS over IN for correlated subqueries
Partial indexes for common filter conditions
Use connection pooling for concurrent access
Snowflake
Date/time:
-- Current date/timeCURRENT_DATE(),CURRENT_TIMESTAMP(), SYSDATE()-- Date arithmeticDATEADD(day,7, date_column)DATEDIFF(day, start_date, end_date)-- Truncate to periodDATE_TRUNC('month', created_at)-- Extract partsYEAR(created_at),MONTH(created_at),DAY(created_at)DAYOFWEEK(created_at)-- FormatTO_CHAR(created_at,'YYYY-MM-DD')
String functions:
-- Case-insensitive by default (depends on collation)columnILIKE'%pattern%'REGEXP_LIKE(column,'pattern')-- Parse JSONcolumn:key::string -- dot notation for VARIANTPARSE_JSON('{"key": "value"}')GET_PATH(variant_col,'path.to.key')-- Flatten arrays/objectsSELECT f.valueFROMtable, LATERAL FLATTEN(input => array_col) f
Semi-structured data:
-- VARIANT type accessdata:customer:name::STRING
data:items[0]:price::NUMBER
-- Flatten nested structuresSELECT t.id, item.value:name::STRING as item_name, item.value:qty::NUMBER as quantity
FROM my_table t,LATERAL FLATTEN(input => t.data:items) item
Performance tips:
Use clustering keys on large tables (not traditional indexes)
Filter on clustering key columns for partition pruning
Set appropriate warehouse size for query complexity
Use RESULT_SCAN(LAST_QUERY_ID()) to avoid re-running expensive queries
Use transient tables for staging/temp data
BigQuery (Google Cloud)
Date/time:
-- Current date/timeCURRENT_DATE(),CURRENT_TIMESTAMP()-- Date arithmeticDATE_ADD(date_column,INTERVAL7DAY)DATE_SUB(date_column,INTERVAL1MONTH)DATE_DIFF(end_date, start_date,DAY)TIMESTAMP_DIFF(end_ts, start_ts,HOUR)-- Truncate to periodDATE_TRUNC(created_at,MONTH)TIMESTAMP_TRUNC(created_at,HOUR)-- Extract partsEXTRACT(YEARFROM created_at)EXTRACT(DAYOFWEEK FROM created_at)-- 1=Sunday-- FormatFORMAT_DATE('%Y-%m-%d', date_column)FORMAT_TIMESTAMP('%Y-%m-%d %H:%M:%S', ts_column)
String functions:
-- No ILIKE, use LOWER()LOWER(column)LIKE'%pattern%'REGEXP_CONTAINS(column, r'pattern')REGEXP_EXTRACT(column, r'pattern')-- String manipulationSPLIT(str,delimiter)-- returns ARRAYARRAY_TO_STRING(array,delimiter)
›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
Steps
1Install product management skill
2Start with user story generation for known feature
3Progress to competitive analysis: research 2-3 competitors
4Use for roadmap prioritization: apply RICE/ICE scoring
5Draft stakeholder communications and refine based on feedback
6Build template library for recurring PM tasks
7Share 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
