Sql Query Clinic

    by TopAgent

    1

    Diagnose slow or broken SQL: explain plans, index advice, rewrites, and correctness checks.

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    Works with the AI tools you already use

    CClaude CodeCCursorCCodex CLIGGitHub CopilotGGemini CLIVVS CodeWWindsurfMManus+14 more

    See it in action

    You say

    Optimize this Postgres query: SELECT * FROM orders WHERE customer_id = 502 AND status = 'pending' ORDER BY created_at DESC; EXPLAIN shows a Seq Scan on 2M rows.

    Your agent does

    Diagnosis: Seq Scan is occurring because there is no index covering the predicate. Recommendation: Create a composite index on (customer_id, status, created_at DESC). Result: This turns a 2M-row scan into a direct B-tree probe, eliminating the separate sort step.

    What you get

    Eliminate full table scans in Postgres and MySQL production databases.Optimize composite indexes for complex filtering and sorting requirements.Rewrite correlated subqueries into performant joins to reduce buffer churn.Troubleshoot execution plan regressions after large data migrations.

    About this skill

    The problem

    Slow production queries cause CPU spikes, timeouts, and buffer churn. Relying on generic database optimization advice often leads to redundant indexes or rewrites that don't actually fix the execution plan.

    What it does

    • Analyzes EXPLAIN ANALYZE output to identify SeqScan bottlenecks and row estimate divergences.
    • Designs optimal composite indexes based on predicate selectivity and ORDER BY requirements.
    • Rewrites inefficient patterns like correlated subqueries, NOT IN, and OR filters into performant joins and unions.
    • Identifies when stale statistics require an ANALYZE or OPTIMIZE rather than a code change.

    Frameworks & tools

    PostgreSQL and MySQL production environments.

    Why this beats prompting it yourself

    Generic prompts often suggest simple indexes that ignore column cardinality or multi-column sort orders. This skill follows a structured diagnosis loop that validates row count reductions and eliminates temp files, ensuring the fix actually changes the plan operators.

    Use cases

    • Eliminating full table scans on high-traffic reporting dashboards.
    • Reducing latency by converting correlated subqueries into set-based joins.
    • Optimizing composite indexes to remove expensive sort steps in GROUP BY operations.
    • Troubleshooting execution plan regressions after large schema migrations.

    Known limitations

    Focuses on relational databases only. Does not support NoSQL tuning, data warehouse modeling, or initial schema design from scratch.

    How to install

    Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.

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