mrpunyapal/laravel-auditor/laravel-audit-database

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Audit a Laravel application's database layer: schema, indexes, relationships, migrations, and query efficiency. Use when auditing the database layer.

Install

npx vskill@latest install mrpunyapal/laravel-auditor/laravel-audit-database

SKILL.md

Laravel Database Audit

Audit the database layer of the Laravel application. Where possible, use structured schema information (via the database_schema tool) rather than relying only on migrations.

List applicable rules first:

bash
php artisan auditor:rules --domain=database --applicable

What to look for

  • Missing or suspicious indexes when evidence supports them: frequently filtered/joined columns without indexes.
  • Schema inconsistencies: columns that contradict domain invariants or usage.
  • Inefficient relationships: relationship definitions that force expensive queries.
  • Suspicious migrations: destructive operations, deadlocks, or migrations that drop data without a path back.
  • Duplicate data modeling: the same fact stored in multiple tables/columns.
  • Bad foreign key choices: missing FKs, wrong ON DELETE behavior, or FKs to the wrong tables. Use database_schema foreign_keys (not only migrations) as the source of truth.
  • Nullable/non-nullable mismatches where they create risk.
  • Query inefficiencies: full-table scans on large tables, missing index coverage for common filters.
  • Relationship definitions inconsistent with schema or usage: foreignId that does not match the relationship, missing pivot table indexes, etc.

Evidence requirements

Every database finding needs at least:

  • The specific table/column (or migration file + line).
  • The query pattern or relationship that is affected.
  • Schema facts from the database_schema tool or migrations.

False positives

  • Small tables do not need indexes on every column.
  • An index that already exists (or is covered by a composite index) is not missing.
  • Do not flag nullable columns when the domain allows null.

Severity guidance

  • high: schema/relationship mismatch causing real bugs or data integrity risk.
  • medium: clear inefficiency or inconsistency with real impact.
  • low: minor schema hygiene.
  • info: observations.

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