Write correct, performant SQL across all major data warehouse dialects (Snowflake, BigQuery, Databricks, PostgreSQL, etc.). Use when writing queries, optimizing slow SQL, translating between dialects, or building complex analytical queries with CTEs, window functions, or aggregations.
View on GitHubFebruary 2, 2026
Select agents to install to:
npx add-skill https://github.com/anthropics/knowledge-work-plugins/blob/main/data/skills/sql-queries/SKILL.md -a claude-code --skill sql-queriesInstallation paths:
.claude/skills/sql-queries/# SQL Queries Skill
Write correct, performant, readable SQL across all major data warehouse dialects.
## Dialect-Specific Reference
### PostgreSQL (including Aurora, RDS, Supabase, Neon)
**Date/time:**
```sql
-- Current date/time
CURRENT_DATE, CURRENT_TIMESTAMP, NOW()
-- Date arithmetic
date_column + INTERVAL '7 days'
date_column - INTERVAL '1 month'
-- Truncate to period
DATE_TRUNC('month', created_at)
-- Extract parts
EXTRACT(YEAR FROM created_at)
EXTRACT(DOW FROM created_at) -- 0=Sunday
-- Format
TO_CHAR(created_at, 'YYYY-MM-DD')
```
**String functions:**
```sql
-- Concatenation
first_name || ' ' || last_name
CONCAT(first_name, ' ', last_name)
-- Pattern matching
column ILIKE '%pattern%' -- case-insensitive
column ~ '^regex_pattern$' -- regex
-- String manipulation
LEFT(str, n), RIGHT(str, n)
SPLIT_PART(str, delimiter, position)
REGEXP_REPLACE(str, pattern, replacement)
```
**Arrays and JSON:**
```sql
-- JSON access
data->>'key' -- text
data->'nested'->'key' -- json
data#>>'{path,to,key}' -- nested text
-- Array operations
ARRAY_AGG(column)
ANY(array_column)
array_column @> ARRAY['value']
```
**Performance tips:**
- Use `EXPLAIN ANALYZE` to profile queries
- 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:**
```sql
-- Current date/time
CURRENT_DATE(), CURRENT_TIMESTAMP(), SYSDATE()
-- Date arithmetic
DATEADD(day, 7, date_column)
DATEDIFF(day, start_date, end_date)
-- Truncate to period
DATE_TRUNC('month', created_at)
-- Extract parts
YEAR(created_at), MONTH(created_at), DAY(created_at)
DAYOFWEEK(created_at)
-- Format
TO_CHAR(created_at, 'YYYY-MM-DD')
```
**String functions:**
```sql
-- Case-insensitive by default (depends on collation)
column ILIKE '%pattern%'
REGEXP_LIKE(column, 'pattern')
-- Parse JSON
column:key::string -- dot notation for VARIANT
PARSE_