Analyze and optimize SQLite schemas, queries, indexes, and pragmas.
npx skills add https://github.com/ahrav/scratch-scanner-rs --skill sqlite-review
Please help me install this Agent Skill. Skill: sqlite-review Source: https://github.com/ahrav/scratch-scanner-rs/tree/main/.claude/skills/sqlite-review Command: npx skills add https://github.com/ahrav/scratch-scanner-rs --skill sqlite-review
This Skill addresses performance bottlenecks and schema inefficiencies within SQLite databases, ensuring optimal data storage and retrieval.
findings.db schema and query performance, identifying and adding necessary indexes to keep reports fast.Use the sqlite-review skill to analyze the database located at /path/to/findings.db.
High-intent search queries and answers about installing and using this skill.
To optimize SQLite query performance, you must analyze query execution plans using EXPLAIN QUERY PLAN. This process identifies missing indexes and schema inefficiencies, enabling actionable recommendations like index additions or query rewrites to resolve slow data retrieval bottlenecks.
The best way to review a SQLite schema is through direct database introspection to audit table structures, column types, and foreign keys. This analysis identifies missing, redundant, or unused indexes by examining concrete execution plan evidence and page count queries.
Tuning SQLite pragmas involves adjusting configuration settings like cache_size and journal_mode to optimize database performance. This requires connecting to the actual database to evaluate current configurations and applying targeted pragma adjustments for better storage and retrieval efficiency.
Yes, SQLite schema review requires direct database access. It must execute EXPLAIN QUERY PLAN, page count queries, and schema introspection commands against the actual database file to gather concrete evidence for accurate performance tuning and schema refinement recommendations.
SQLite databases accumulate redundant or unused indexes when schema evolution outpaces index maintenance. Identifying these requires analyzing query execution plans to determine which indexes are actually utilized by the query optimizer and which cause unnecessary write overhead.
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