sql-pro skill

by Jeffallan·Jeffallan/claude-skills·12k stars·MIT

Optimizes SQL queries, designs database schemas, and troubleshoots performance issues. Use when a user asks why their query is slow, needs help writing complex joins or aggregations, mentions database performance issues, or wants to design or migrate a schema. Invoke for complex queries, window functions, CTEs, indexing strategies, query plan analysis, covering index creation, recursive queries, EXPLAIN/ANALYZE interpretation, before/after query benchmarking, or migrating queries between database dialects (PostgreSQL, MySQL, SQL Server, Oracle).

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Install the sql-pro skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description.

git clone --depth 1 https://github.com/Jeffallan/claude-skills.git /tmp/claude-skills
mkdir -p ~/.claude/skills
cp -r /tmp/claude-skills/skills/sql-pro ~/.claude/skills/sql-pro

available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

SQL Pro

Core Workflow

Schema Analysis - Review database structure, indexes, query patterns, performance bottlenecks
Design - Create set-based operations using CTEs, window functions, appropriate joins
Optimize - Analyze execution plans, implement covering indexes, eliminate table scans
Verify - Run EXPLAIN ANALYZE and confirm no sequential scans on large tables; if query does not meet sub-100ms target, iterate on index selection or query rewrite before proceeding
Document - Provide query explanations, index rationale, performance metrics

Reference Guide

Load detailed guidance based on context:

Quick-Reference Examples

CTE Pattern

-- Isolate expensive subquery logic for reuse and readability
WITH ranked_orders AS (
    SELECT
        customer_id,
        order_id,
        total_amount,
        ROW_NUMBER() OVER (PARTITION BY customer_id ORDER BY order_date DESC) AS rn
    FROM orders
    WHERE status = 'completed'          -- filter early, before the join
)
SELECT customer_id, order_id, total_amount
FROM ranked_orders
WHERE rn = 1;                           -- latest completed order per customer

Window Function Pattern

-- Running total and rank within partition — no self-join required
SELECT
    department_id,
    employee_id,
    salary,
    SUM(salary)  OVER (PARTITION BY department_id ORDER BY hire_date) AS running_payroll,
    RANK()       OVER (PARTITION BY department_id ORDER BY salary DESC) AS salary_rank
FROM employees;

EXPLAIN ANALYZE Interpretation

-- PostgreSQL: always use ANALYZE to see actual row counts vs. estimates
EXPLAIN (ANALYZE, BUFFERS, FORMAT TEXT)
SELECT *
FROM orders o
JOIN customers c ON c.id = o.customer_id
WHERE o.created_at > NOW() - INTERVAL '30 days';

Key things to check in the output:

Seq Scan on large table → add or fix an index
actual rows ≫ estimated rows → run ANALYZE to refresh statistics
Buffers: shared hit vs read → high read count signals missing cache / index

Before / After Optimization Example

-- BEFORE: correlated subquery, one execution per row (slow)
SELECT order_id,
       (SELECT SUM(quantity) FROM order_items oi WHERE oi.order_id = o.id) AS item_count
FROM orders o;

-- AFTER: single aggregation join (fast)
SELECT o.order_id, COALESCE(agg.item_count, 0) AS item_count
FROM orders o
LEFT JOIN (
    SELECT order_id, SUM(quantity) AS item_count
    FROM order_items
    GROUP BY order_id
) agg ON agg.order_id = o.id;

-- Supporting covering index (includes all columns touched by the query)
CREATE INDEX idx_order_items_order_qty
    ON order_items (order_id)
    INCLUDE (quantity);

Constraints

MUST DO

Analyze execution plans before recommending optimizations
Use set-based operations over row-by-row processing
Apply filtering early in query execution (before joins where possible)
Use EXISTS over COUNT for existence checks
Handle NULLs explicitly in comparisons and aggregations
Create covering indexes for frequent queries
Test with production-scale data volumes

MUST NOT DO

Use SELECT * in production queries
Use cursors when set-based operations work
Ignore platform-specific optimizations when targeting a specific dialect
Implement solutions without considering data volume and cardinality

Output Templates

When implementing SQL solutions, provide:

Optimized query with inline comments
Required indexes with rationale
Execution plan analysis
Performance metrics (before/after)
Platform-specific notes if applicable

Documentation

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