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SQL Queries

AnthropicOfficialPublished by Anthropic

  • Skill
  • Tool integration
  • Data and AI
  • Apache-2.0
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From the author

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.
— the description by Anthropic

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Written by AutoGineer from the skill’s own files, not by the author

The SQL Queries skill provides a structured reference for developers and data analysts working with multiple SQL dialects, including PostgreSQL, Snowflake, BigQuery, Redshift, and Databricks. It covers dialect-specific syntax for date arithmetic, string manipulation, and semi-structured data handling, alongside performance optimization strategies and common analytical patterns like window functions, CTEs, cohort analysis, and funnel tracking.

What it does

  • Provides syntax references for major SQL dialects
  • Offers performance optimization tips for specific data warehouses
  • Demonstrates complex analytical patterns using CTEs and window functions
  • Includes guidance on deduplication and funnel analysis
  • Provides troubleshooting steps for common SQL errors
  • Assists in translating queries between different database environments

When to use it

  • When writing new analytical queries or complex data transformations
  • When optimizing slow-running queries in a specific data warehouse
  • When porting SQL code from one dialect to another
  • When implementing common patterns like cohort retention or funnel analysis
  • When debugging syntax or type mismatch errors

Advantages

  • Reduces context switching by centralizing dialect-specific syntax
  • Improves query performance through warehouse-specific best practices
  • Enhances code readability using standardized CTE patterns
  • Simplifies complex analytical tasks with pre-built templates

Usage examples

“How do I calculate a 7-day moving average of revenue in Snowflake?”
The agent provides the window function syntax using AVG() OVER() with the appropriate ROWS BETWEEN clause for Snowflake.
“Translate this PostgreSQL query to BigQuery syntax.”
The agent identifies dialect-specific differences, such as replacing ILIKE with LOWER() and adjusting date functions to BigQuery standards.
“My Redshift query is slow, how can I optimize it?”
The agent suggests checking distribution keys, sort keys, and reviewing the query plan using EXPLAIN to identify data movement issues.
“Show me how to deduplicate a table based on the most recent timestamp.”
The agent provides a CTE pattern using ROW_NUMBER() partitioned by the entity ID and ordered by the timestamp.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/anthropics/sql-queries/download

  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip sql-queries.zip -d .agents/skills

Documentation by Anthropic

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:

-- 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:

-- 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:

-- 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:

-- 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:

-- Case-insensitive by default (depends on collation)
column ILIKE '%pattern%'
REGEXP_LIKE(column, 'pattern')

-- Parse JSON
column:key::string  -- dot notation for VARIANT
PARSE_JSON('{"key": "value"}')
GET_PATH(variant_col, 'path.to.key')

-- Flatten arrays/objects
SELECT f.value FROM table, LATERAL FLATTEN(input => array_col) f

Semi-structured data:

-- VARIANT type access
data:customer:name::STRING
data:items[0]:price::NUMBER

-- Flatten nested structures
SELECT
    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/time
CURRENT_DATE(), CURRENT_TIMESTAMP()

-- Date arithmetic
DATE_ADD(date_column, INTERVAL 7 DAY)
DATE_SUB(date_column, INTERVAL 1 MONTH)
DATE_DIFF(end_date, start_date, DAY)
TIMESTAMP_DIFF(end_ts, start_ts, HOUR)

-- Truncate to period
DATE_TRUNC(created_at, MONTH)
TIMESTAMP_TRUNC(created_at, HOUR)

-- Extract parts
EXTRACT(YEAR FROM created_at)
EXTRACT(DAYOFWEEK FROM created_at)  -- 1=Sunday

-- Format
FORMAT_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 manipulation
SPLIT(str, delimiter)  -- returns ARRAY
ARRAY_TO_STRING(array, delimiter)

Arrays and structs:

-- Array operations
ARRAY_AGG(column)
UNNEST(array_column)
ARRAY_LENGTH(array_column)
value IN UNNEST(array_column)

-- Struct access
struct_column.field_name

Performance tips:

  • Always filter on partition columns (usually date) to reduce bytes scanned

  • Use clustering for frequently filtered columns within partitions

  • Use APPROX_COUNT_DISTINCT() for large-scale cardinality estimates

  • Avoid SELECT * -- billing is per-byte scanned

  • Use DECLARE and SET for parameterized scripts

  • Preview query cost with dry run before executing large queries

Redshift (Amazon)

Date/time:

-- Current date/time
CURRENT_DATE, GETDATE(), SYSDATE

-- Date arithmetic
DATEADD(day, 7, date_column)
DATEDIFF(day, start_date, end_date)

-- Truncate to period
DATE_TRUNC('month', created_at)

-- Extract parts
EXTRACT(YEAR FROM created_at)
DATE_PART('dow', created_at)

String functions:

-- Case-insensitive
column ILIKE '%pattern%'
REGEXP_INSTR(column, 'pattern') > 0

-- String manipulation
SPLIT_PART(str, delimiter, position)
LISTAGG(column, ', ') WITHIN GROUP (ORDER BY column)

Performance tips:

  • Design distribution keys for collocated joins (DISTKEY)

  • Use sort keys for frequently filtered columns (SORTKEY)

  • Use EXPLAIN to check query plan

  • Avoid cross-node data movement (watch for DS_BCAST and DS_DIST)

  • ANALYZE and VACUUM regularly

  • Use late-binding views for schema flexibility

Databricks SQL

Date/time:

-- Current date/time
CURRENT_DATE(), CURRENT_TIMESTAMP()

-- Date arithmetic
DATE_ADD(date_column, 7)
DATEDIFF(end_date, start_date)
ADD_MONTHS(date_column, 1)

-- Truncate to period
DATE_TRUNC('MONTH', created_at)
TRUNC(date_column, 'MM')

-- Extract parts
YEAR(created_at), MONTH(created_at)
DAYOFWEEK(created_at)

Delta Lake features:

-- Time travel
SELECT * FROM my_table TIMESTAMP AS OF '2024-01-15'
SELECT * FROM my_table VERSION AS OF 42

-- Describe history
DESCRIBE HISTORY my_table

-- Merge (upsert)
MERGE INTO target USING source
ON target.id = source.id
WHEN MATCHED THEN UPDATE SET *
WHEN NOT MATCHED THEN INSERT *

Performance tips:

  • Use Delta Lake's OPTIMIZE and ZORDER for query performance

  • Leverage Photon engine for compute-intensive queries

  • Use CACHE TABLE for frequently accessed datasets

  • Partition by low-cardinality date columns

Common SQL Patterns

Window Functions
-- Ranking
ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY created_at DESC)
RANK() OVER (PARTITION BY category ORDER BY revenue DESC)
DENSE_RANK() OVER (ORDER BY score DESC)

-- Running totals / moving averages
SUM(revenue) OVER (ORDER BY date_col ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) as running_total
AVG(revenue) OVER (ORDER BY date_col ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) as moving_avg_7d

-- Lag / Lead
LAG(value, 1) OVER (PARTITION BY entity ORDER BY date_col) as prev_value
LEAD(value, 1) OVER (PARTITION BY entity ORDER BY date_col) as next_value

-- First / Last value
FIRST_VALUE(status) OVER (PARTITION BY user_id ORDER BY created_at ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)
LAST_VALUE(status) OVER (PARTITION BY user_id ORDER BY created_at ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)

-- Percent of total
revenue / SUM(revenue) OVER () as pct_of_total
revenue / SUM(revenue) OVER (PARTITION BY category) as pct_of_category
CTEs for Readability
WITH
-- Step 1: Define the base population
base_users AS (
    SELECT user_id, created_at, plan_type
    FROM users
    WHERE created_at >= DATE '2024-01-01'
      AND status = 'active'
),

-- Step 2: Calculate user-level metrics
user_metrics AS (
    SELECT
        u.user_id,
        u.plan_type,
        COUNT(DISTINCT e.session_id) as session_count,
        SUM(e.revenue) as total_revenue
    FROM base_users u
    LEFT JOIN events e ON u.user_id = e.user_id
    GROUP BY u.user_id, u.plan_type
),

-- Step 3: Aggregate to summary level
summary AS (
    SELECT
        plan_type,
        COUNT(*) as user_count,
        AVG(session_count) as avg_sessions,
        SUM(total_revenue) as total_revenue
    FROM user_metrics
    GROUP BY plan_type
)

SELECT * FROM summary ORDER BY total_revenue DESC;
Cohort Retention
WITH cohorts AS (
    SELECT
        user_id,
        DATE_TRUNC('month', first_activity_date) as cohort_month
    FROM users
),
activity AS (
    SELECT
        user_id,
        DATE_TRUNC('month', activity_date) as activity_month
    FROM user_activity
)
SELECT
    c.cohort_month,
    COUNT(DISTINCT c.user_id) as cohort_size,
    COUNT(DISTINCT CASE
        WHEN a.activity_month = c.cohort_month THEN a.user_id
    END) as month_0,
    COUNT(DISTINCT CASE
        WHEN a.activity_month = c.cohort_month + INTERVAL '1 month' THEN a.user_id
    END) as month_1,
    COUNT(DISTINCT CASE
        WHEN a.activity_month = c.cohort_month + INTERVAL '3 months' THEN a.user_id
    END) as month_3
FROM cohorts c
LEFT JOIN activity a ON c.user_id = a.user_id
GROUP BY c.cohort_month
ORDER BY c.cohort_month;
Funnel Analysis
WITH funnel AS (
    SELECT
        user_id,
        MAX(CASE WHEN event = 'page_view' THEN 1 ELSE 0 END) as step_1_view,
        MAX(CASE WHEN event = 'signup_start' THEN 1 ELSE 0 END) as step_2_start,
        MAX(CASE WHEN event = 'signup_complete' THEN 1 ELSE 0 END) as step_3_complete,
        MAX(CASE WHEN event = 'first_purchase' THEN 1 ELSE 0 END) as step_4_purchase
    FROM events
    WHERE event_date >= CURRENT_DATE - INTERVAL '30 days'
    GROUP BY user_id
)
SELECT
    COUNT(*) as total_users,
    SUM(step_1_view) as viewed,
    SUM(step_2_start) as started_signup,
    SUM(step_3_complete) as completed_signup,
    SUM(step_4_purchase) as purchased,
    ROUND(100.0 * SUM(step_2_start) / NULLIF(SUM(step_1_view), 0), 1) as view_to_start_pct,
    ROUND(100.0 * SUM(step_3_complete) / NULLIF(SUM(step_2_start), 0), 1) as start_to_complete_pct,
    ROUND(100.0 * SUM(step_4_purchase) / NULLIF(SUM(step_3_complete), 0), 1) as complete_to_purchase_pct
FROM funnel;
Deduplication
-- Keep the most recent record per key
WITH ranked AS (
    SELECT
        *,
        ROW_NUMBER() OVER (
            PARTITION BY entity_id
            ORDER BY updated_at DESC
        ) as rn
    FROM source_table
)
SELECT * FROM ranked WHERE rn = 1;

Error Handling and Debugging

When a query fails:

  1. Syntax errors: Check for dialect-specific syntax (e.g., ILIKE not available in BigQuery, SAFE_DIVIDE only in BigQuery)

  2. Column not found: Verify column names against schema -- check for typos, case sensitivity (PostgreSQL is case-sensitive for quoted identifiers)

  3. Type mismatches: Cast explicitly when comparing different types (CAST(col AS DATE), col::DATE)

  4. Division by zero: Use NULLIF(denominator, 0) or dialect-specific safe division

  5. Ambiguous columns: Always qualify column names with table alias in JOINs

  6. Group by errors: All non-aggregated columns must be in GROUP BY (except in BigQuery which allows grouping by alias)

Files

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Requirements and compatibility

Written for: Claude Code, Claude apps

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Claude Code frontmatter, Needs Node.js

Security

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Attribution and licence

Published by
Anthropic
Publisher
Anthropic (Official)
Source
anthropics/knowledge-work-plugins · data/skills/sql-queries · commit e050c2e
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from data/LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
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