Agent skill

sql-cookbook

Craft and optimize SQL for any warehouse dialect — Snowflake, BigQuery, Databricks, Redshift, PostgreSQL. Covers window functions, CTEs, aggregations, joins, funnel queries, cohort retention, deduplication, dialect translation, slow query tuning, and cross-platform syntax differences.

View SKILL.md on GitHub Repository
Stars 50
Forks 8

Install this agent skill to your Project

npx add-skill https://github.com/vm0-ai/vm0-skills/tree/main/sql-cookbook

SKILL.md

SQL Cookbook

Practical reference for building analytical SQL across every major data warehouse platform.

Platform-Specific Syntax Guide

PostgreSQL (Aurora, RDS, Supabase, Neon)

Working with dates and times:

sql
-- Today and now
CURRENT_DATE, CURRENT_TIMESTAMP, NOW()

-- Adding and subtracting intervals
some_date + INTERVAL '7 days'
some_date - INTERVAL '1 month'

-- Round down to a time boundary
DATE_TRUNC('month', event_ts)

-- Pull out individual components
EXTRACT(YEAR FROM event_ts)
EXTRACT(DOW FROM event_ts)  -- Sunday = 0

-- Render as formatted text
TO_CHAR(event_ts, 'YYYY-MM-DD')

Text operations:

sql
-- Joining strings together
first_name || ' ' || last_name
CONCAT(first_name, ' ', last_name)

-- Flexible matching
col ILIKE '%term%'  -- ignores case
col ~ '^regex$'     -- POSIX regex

-- Splitting and extracting
LEFT(str, n), RIGHT(str, n)
SPLIT_PART(str, delimiter, part_num)
REGEXP_REPLACE(str, pattern, replacement)

JSON and array handling:

sql
-- Accessing JSON fields
payload->>'key'             -- returns text
payload->'nested'->'field'  -- returns json object
payload#>>'{a,b,c}'        -- deep path as text

-- Array utilities
ARRAY_AGG(col)
ANY(arr_col)
arr_col @> ARRAY['val']

Tuning guidance:

  • Run EXPLAIN ANALYZE to inspect actual execution plans
  • Add indexes on columns used in WHERE and JOIN conditions
  • Prefer EXISTS over IN for correlated lookups
  • Consider partial indexes for frequently used filter predicates
  • Deploy connection pooling when handling concurrent workloads

Snowflake

Date and time functions:

sql
-- Getting current moments
CURRENT_DATE(), CURRENT_TIMESTAMP(), SYSDATE()

-- Shifting dates
DATEADD(day, 7, some_date)
DATEDIFF(day, start_dt, end_dt)

-- Truncation
DATE_TRUNC('month', event_ts)

-- Component extraction
YEAR(event_ts), MONTH(event_ts), DAY(event_ts)
DAYOFWEEK(event_ts)

-- Formatting
TO_CHAR(event_ts, 'YYYY-MM-DD')

Text and pattern matching:

sql
-- Case-insensitive matching (default collation dependent)
col ILIKE '%term%'
REGEXP_LIKE(col, 'pattern')

-- Working with VARIANT / semi-structured data
col:key::string
PARSE_JSON('{"a": 1}')
GET_PATH(variant_col, 'path.to.field')

-- Expanding nested arrays
SELECT f.value FROM tbl, LATERAL FLATTEN(input => arr_col) f

Navigating semi-structured columns:

sql
-- Dot-path access on VARIANT
payload:customer:name::STRING
payload:items[0]:price::NUMBER

-- Exploding nested arrays into rows
SELECT
    t.id,
    elem.value:name::STRING AS item_name,
    elem.value:qty::NUMBER AS item_qty
FROM my_table t,
LATERAL FLATTEN(input => t.payload:items) elem

Tuning guidance:

  • Rely on clustering keys rather than traditional indexes for large tables
  • Target clustering key columns in filters to maximize partition pruning
  • Size the virtual warehouse appropriately for query complexity
  • Reuse results via RESULT_SCAN(LAST_QUERY_ID()) to skip re-execution
  • Use transient tables for ephemeral staging data

BigQuery

Date and time functions:

sql
-- Current moments
CURRENT_DATE(), CURRENT_TIMESTAMP()

-- Shifting dates forward and backward
DATE_ADD(some_date, INTERVAL 7 DAY)
DATE_SUB(some_date, INTERVAL 1 MONTH)
DATE_DIFF(end_dt, start_dt, DAY)
TIMESTAMP_DIFF(end_ts, start_ts, HOUR)

-- Truncation
DATE_TRUNC(event_ts, MONTH)
TIMESTAMP_TRUNC(event_ts, HOUR)

-- Component extraction
EXTRACT(YEAR FROM event_ts)
EXTRACT(DAYOFWEEK FROM event_ts)  -- Sunday = 1

-- Display formatting
FORMAT_DATE('%Y-%m-%d', date_col)
FORMAT_TIMESTAMP('%Y-%m-%d %H:%M:%S', ts_col)

Text operations:

sql
-- No native ILIKE; lowercase first
LOWER(col) LIKE '%term%'
REGEXP_CONTAINS(col, r'pattern')
REGEXP_EXTRACT(col, r'pattern')

-- Splitting and reassembling
SPLIT(str, delimiter)  -- produces an ARRAY
ARRAY_TO_STRING(arr, delimiter)

Arrays and structs:

sql
-- Array utilities
ARRAY_AGG(col)
UNNEST(arr_col)
ARRAY_LENGTH(arr_col)
val IN UNNEST(arr_col)

-- Struct field access
struct_col.field_name

Tuning guidance:

  • Always apply filters on partition columns (typically date) to minimize bytes scanned
  • Add clustering on columns that appear frequently in WHERE clauses
  • Swap COUNT(DISTINCT ...) for APPROX_COUNT_DISTINCT() on high-cardinality data
  • Avoid SELECT * since billing scales with bytes read
  • Use DECLARE / SET for parameterized script logic
  • Run a dry-run estimate before executing expensive queries

Redshift

Date and time functions:

sql
-- Current moments
CURRENT_DATE, GETDATE(), SYSDATE

-- Shifting dates
DATEADD(day, 7, some_date)
DATEDIFF(day, start_dt, end_dt)

-- Truncation
DATE_TRUNC('month', event_ts)

-- Component extraction
EXTRACT(YEAR FROM event_ts)
DATE_PART('dow', event_ts)

Text operations:

sql
-- Case-insensitive matching
col ILIKE '%term%'
REGEXP_INSTR(col, 'pattern') > 0

-- Splitting and concatenation
SPLIT_PART(str, delimiter, part_num)
LISTAGG(col, ', ') WITHIN GROUP (ORDER BY col)

Tuning guidance:

  • Assign distribution keys (DISTKEY) so joined tables are co-located on the same nodes
  • Define sort keys (SORTKEY) on columns commonly used for filtering
  • Review query plans with EXPLAIN
  • Watch for DS_BCAST and DS_DIST in plans (signs of costly cross-node shuffles)
  • Run ANALYZE and VACUUM on a regular cadence
  • Use late-binding views for flexibility when schemas evolve

Databricks SQL

Date and time functions:

sql
-- Current moments
CURRENT_DATE(), CURRENT_TIMESTAMP()

-- Shifting dates
DATE_ADD(some_date, 7)
DATEDIFF(end_dt, start_dt)
ADD_MONTHS(some_date, 1)

-- Truncation
DATE_TRUNC('MONTH', event_ts)
TRUNC(date_col, 'MM')

-- Component extraction
YEAR(event_ts), MONTH(event_ts)
DAYOFWEEK(event_ts)

Delta Lake capabilities:

sql
-- Query historical snapshots
SELECT * FROM tbl TIMESTAMP AS OF '2024-01-15'
SELECT * FROM tbl VERSION AS OF 42

-- Inspect table history
DESCRIBE HISTORY tbl

-- Upsert with MERGE
MERGE INTO target USING source
ON target.id = source.id
WHEN MATCHED THEN UPDATE SET *
WHEN NOT MATCHED THEN INSERT *

Tuning guidance:

  • Run OPTIMIZE with ZORDER on frequently queried columns
  • Enable the Photon engine for compute-heavy workloads
  • Apply CACHE TABLE on datasets that are read repeatedly
  • Partition by low-cardinality date columns for efficient pruning

Reusable Analytical Patterns

Window Function Recipes

sql
-- Assign sequential position within groups
ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY event_ts DESC)
RANK() OVER (PARTITION BY category ORDER BY revenue DESC)
DENSE_RANK() OVER (ORDER BY score DESC)

-- Cumulative sums and rolling averages
SUM(amount) OVER (ORDER BY dt ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS cumulative_total
AVG(amount) OVER (ORDER BY dt ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS rolling_7d_avg

-- Accessing adjacent rows
LAG(val, 1) OVER (PARTITION BY entity ORDER BY dt) AS prior_val
LEAD(val, 1) OVER (PARTITION BY entity ORDER BY dt) AS next_val

-- Boundary values within a partition
FIRST_VALUE(status) OVER (PARTITION BY user_id ORDER BY event_ts ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)
LAST_VALUE(status) OVER (PARTITION BY user_id ORDER BY event_ts ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)

-- Proportional share
revenue / SUM(revenue) OVER () AS share_of_total
revenue / SUM(revenue) OVER (PARTITION BY category) AS share_within_category

Structured Queries with CTEs

sql
WITH
-- Stage 1: Narrow down to the target population
eligible_users AS (
    SELECT user_id, signup_date, tier
    FROM users
    WHERE signup_date >= DATE '2024-01-01'
      AND account_status = 'active'
),

-- Stage 2: Derive per-user measures
per_user AS (
    SELECT
        u.user_id,
        u.tier,
        COUNT(DISTINCT e.session_id) AS sessions,
        SUM(e.revenue) AS revenue
    FROM eligible_users u
    LEFT JOIN events e ON u.user_id = e.user_id
    GROUP BY u.user_id, u.tier
),

-- Stage 3: Roll up to tier-level summary
tier_summary AS (
    SELECT
        tier,
        COUNT(*) AS users,
        AVG(sessions) AS avg_sessions,
        SUM(revenue) AS total_revenue
    FROM per_user
    GROUP BY tier
)

SELECT * FROM tier_summary ORDER BY total_revenue DESC;

Cohort Retention Tracking

sql
WITH signup_cohorts AS (
    SELECT
        user_id,
        DATE_TRUNC('month', first_seen) AS cohort
    FROM users
),
monthly_activity AS (
    SELECT
        user_id,
        DATE_TRUNC('month', activity_date) AS active_month
    FROM user_events
)
SELECT
    s.cohort,
    COUNT(DISTINCT s.user_id) AS cohort_size,
    COUNT(DISTINCT CASE
        WHEN a.active_month = s.cohort THEN a.user_id
    END) AS m0,
    COUNT(DISTINCT CASE
        WHEN a.active_month = s.cohort + INTERVAL '1 month' THEN a.user_id
    END) AS m1,
    COUNT(DISTINCT CASE
        WHEN a.active_month = s.cohort + INTERVAL '3 months' THEN a.user_id
    END) AS m3
FROM signup_cohorts s
LEFT JOIN monthly_activity a ON s.user_id = a.user_id
GROUP BY s.cohort
ORDER BY s.cohort;

Conversion Funnel Measurement

sql
WITH step_flags AS (
    SELECT
        user_id,
        MAX(CASE WHEN event_name = 'page_view' THEN 1 ELSE 0 END) AS saw_page,
        MAX(CASE WHEN event_name = 'signup_start' THEN 1 ELSE 0 END) AS began_signup,
        MAX(CASE WHEN event_name = 'signup_complete' THEN 1 ELSE 0 END) AS finished_signup,
        MAX(CASE WHEN event_name = 'first_purchase' THEN 1 ELSE 0 END) AS made_purchase
    FROM events
    WHERE event_date >= CURRENT_DATE - INTERVAL '30 days'
    GROUP BY user_id
)
SELECT
    COUNT(*) AS total_users,
    SUM(saw_page) AS viewers,
    SUM(began_signup) AS signup_starts,
    SUM(finished_signup) AS signup_completions,
    SUM(made_purchase) AS purchasers,
    ROUND(100.0 * SUM(began_signup) / NULLIF(SUM(saw_page), 0), 1) AS view_to_start_rate,
    ROUND(100.0 * SUM(finished_signup) / NULLIF(SUM(began_signup), 0), 1) AS start_to_finish_rate,
    ROUND(100.0 * SUM(made_purchase) / NULLIF(SUM(finished_signup), 0), 1) AS finish_to_purchase_rate
FROM step_flags;

Removing Duplicate Records

sql
-- Retain only the latest version of each entity
WITH ordered AS (
    SELECT
        *,
        ROW_NUMBER() OVER (
            PARTITION BY entity_id
            ORDER BY modified_at DESC
        ) AS seq
    FROM raw_table
)
SELECT * FROM ordered WHERE seq = 1;

Troubleshooting Query Failures

When a query produces an error, work through these checks:

  1. Syntax issues: Verify dialect compatibility (e.g., BigQuery lacks ILIKE; only BigQuery has SAFE_DIVIDE)
  2. Missing columns: Confirm column names against the actual schema; watch for case sensitivity with quoted identifiers in PostgreSQL
  3. Type conflicts: Add explicit casts when comparing mismatched types (CAST(col AS DATE) or col::DATE)
  4. Divide-by-zero: Wrap denominators with NULLIF(denominator, 0) or use platform-specific safe division
  5. Column ambiguity: Alias every table in JOINs and always prefix column references
  6. GROUP BY violations: Every selected column that is not inside an aggregate must appear in GROUP BY (BigQuery permits alias references as an exception)

Recommended Agent Skills

Expand your agent's capabilities with these related and highly-rated skills.

vm0-ai/vm0-skills

brave-search

Brave Search API for web search. Use when user says "search web", "Brave search", or asks to "find on web" without specifying Google.

50 8
Explore
vm0-ai/vm0-skills

supadata

Supadata API for YouTube/web data. Use when user mentions "Supadata", "YouTube data", "channel stats", or web scraping data.

50 8
Explore
vm0-ai/vm0-skills

roadmap-planning

Build and prioritize product roadmaps using scoring models like RICE, ICE, and value-effort matrices. Activate when creating a product roadmap, prioritizing features, sequencing initiatives, mapping dependencies, balancing team capacity, choosing between Now/Next/Later or quarterly planning, or communicating roadmap tradeoffs to executives and stakeholders.

50 8
Explore
vm0-ai/vm0-skills

qdrant

Qdrant API for vector search. Use when user mentions "Qdrant", "vector database", "semantic search", or embeddings storage.

50 8
Explore
vm0-ai/vm0-skills

calendly

Calendly scheduling API. Use when user mentions "Calendly", "calendly.com", "schedule a meeting", "booking link", "event types", or asks about interview scheduling.

50 8
Explore
vm0-ai/vm0-skills

stripe

Stripe API for payments. Use when user mentions "Stripe", "payment", "subscription", "billing", "invoice", or asks about payment processing.

50 8
Explore

Didn't find tool you were looking for?

Be as detailed as possible for better results