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sql-optimization-patterns

sql-optimization-patterns

Diagnose slow SQL with query plans, preserve query results, and verify indexing or query changes against representative data.

Independent GitHub snapshot · Automated classification · Original documentation governs

Skill-Dokumentation

GitHub

name: sql-optimization-patterns description: "Diagnose slow SQL with query plans, preserve query results, and verify indexing or query changes against representative data." risk: critical source: community date_added: "2026-02-27"

SQL Optimization Patterns

Diagnose slow SQL with query plans, preserve query results, and verify indexing or query changes against representative data.

Use this skill when

  • Debugging slow-running queries
  • Designing performant database schemas
  • Optimizing application response times
  • Reducing database load and costs
  • Improving scalability for growing datasets
  • Analyzing EXPLAIN query plans
  • Implementing efficient indexes
  • Resolving N+1 query problems

Do not use this skill when

  • The task is unrelated to sql optimization patterns
  • You need a different domain or tool outside this scope

Instructions

  • Confirm database engine/version, query parameters, expected rows, data distribution and the permitted environment. Start read-only; obtain authorization before DDL, data changes, configuration changes or maintenance.
  • Compare result sets before comparing performance. EXPLAIN ANALYZE executes the statement: use approved representative data, account for functions and triggers, and do not assume a transaction rollback undoes every side effect.
  • Record the observed plan, timing conditions and correctness checks. Indexes and query rewrites are hypotheses, not universal speed improvements.
  • If detailed examples are required, open resources/implementation-playbook.md.

Resources

  • resources/implementation-playbook.md for detailed patterns and examples.

Limitations

  • Use this skill only when the task clearly matches the scope described above.
  • Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
  • Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

Worked example

The playbook includes an executable SQLite batch-loading example with bound values and an empty-input case. The repository test exercises it alongside pagination ties and aggregation equivalence. SQLite correctness checks do not establish PostgreSQL performance or production safety.

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