Скиллы/Базы данных/sql-queries

sql-queries

Написание и оптимизация аналитического SQL

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Что получите

SQL-запрос для выбранной системы либо переработанный вариант существующего запроса.

Что передать

Задача, система данных, схема таблиц и, если есть, текущий SQL с ошибкой или планом выполнения.

Автопроверка файлов16.09.2026

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Как использовать

Помогает аналитикам и разработчикам составлять SQL-запросы, разбирать ошибки, улучшать медленные запросы и переносить их между диалектами. Подходит для расчёта метрик, анализа удержания и воронок, а также удаления дублей из выборки.

Укажите целевую систему и нужный результат: период, фильтры, связи таблиц и уровень детализации.
Для нового запроса опишите расчёт; для существующего приложите SQL и поясните проблему — ошибку, неверные данные или медленное выполнение.
Попросите адаптировать запрос к диалекту и рассмотреть способы сокращения объёма обрабатываемых данных.

Условия работы

Подробные рекомендации приведены для PostgreSQL, Snowflake, BigQuery, Redshift и Databricks SQL; синтаксис и способы оптимизации зависят от системы.
Перед выполнением крупных запросов BigQuery следует предварительно оценить стоимость без запуска.

Пример запроса после установки

Напиши запрос для BigQuery, который покажет месячное удержание пользователей по дате первой активности. Используй приложенную схему таблиц и ограничь период последними шестью месяцами.

Пример составлен по инструкции автора; результат запуска не проверялся.

Основание: инструкция закреплённой версии

Для установки

Node.js, npm и Git для команды выше. Подключения и инструменты для работы скилла настраиваются отдельно.

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Файлы скилла

GitHub

SKILL.md11 КБ

Читать инструкцию SKILL.md

Исходный текст закреплённой версии.

---
name: sql-queries
description: 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.
user-invocable: false
---

# 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_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:**
```sql
-- 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:**
```sql
-- 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:**
```sql
-- 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:**
```sql
-- 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:**
```sql
-- 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:**
```sql
-- 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:**
```sql
-- 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:**
```sql
-- 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

```sql
-- 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

```sql
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

```sql
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

```sql
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

```sql
-- 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)

Проверка и особенности

Автоматическая проверка источника и файлов

Автоматически сверены публичный репозиторий, лицензия, YAML-заголовок SKILL.md, состав папки и Git-хеш каждого файла. Исполняемые файлы и срабатывания статических правил исключены из этой партии. Ручной аудит всех инструкций и проверка работы не проводились; эта проверка не гарантирует безопасность. Команда закреплена на проверенном коммите.

Интерес к репозиторию оцениваем по звёздам и форкам GitHub. Это показатели всего репозитория, а не числа установок отдельного скилла.

Проверка состава: 16.09.2026
Проверенная версия: c55597c2697c
Файлы с кодом: Не указаны в данных проверки
Изменения на GitHub: Нужна повторная проверкаПоследняя проверка: 11.10.2026

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Репозиторий и лицензия

anthropics/knowledge-work-plugins

28 988 звёзд3 289 форков

Это показатели всего репозитория, а не установки скилла. Снимок требует обновления.

Другие скиллы из этого репозитория

Последняя активность GitHub: 11.10.2026
Лицензия: Apache-2.0
Добавлен в каталог: 16.09.2026

Полный текст лицензии


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