Laravel database optimization

Skill soden46/syarif-laravel-ai-skills/skills/laravel-database-optimization

Optimize Laravel database work across N+1 queries, indexes, selective columns, caching, pagination, large data, locks, and migrations.From its SKILL.md

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npx -y skills add soden46/syarif-laravel-ai-skills --skill laravel-database-optimization

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SKILL.md

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Laravel Database Optimization

Use this skill when improving query performance, reviewing migrations, debugging slow pages, or reducing memory and database load in Laravel apps.

This skill coordinates existing performance skills and adds an optimization workflow.

Priority Order

Prove the bottleneck with logs, query inspection, profiling, EXPLAIN, tests, or realistic data volume.
Fix query shape before adding infrastructure: eager loading, constraints, selective columns, aggregates, pagination, and bounded datasets.
Add indexes that match actual filters, joins, sorts, and uniqueness rules.
Cache only stable or expensive work with explicit invalidation.
Review transaction scope, lock behavior, and retry strategy for write-heavy paths.
Plan migrations with production data size and lock risk in mind.

Query Shape

Prevent N+1 problems with intentional eager loading.
Select only needed columns on hot paths, including constrained relationship columns.
Use withCount, withSum, exists, subqueries, or aggregates instead of loading full relations for summaries.
Avoid unbounded all(), broad get(), and collection-side filtering on large tables.
Use cursor pagination or chunking for large ordered datasets and background processing.

Indexes And Migrations

Add indexes for foreign keys, common filters, common sort paths, and composite access patterns.
Match composite index order to the real query pattern.
Avoid adding indexes speculatively without a query path that needs them.
For production-scale tables, plan additive and reversible migrations; ask before destructive changes.
When altering existing columns, preserve existing attributes required by the database platform.

Caching

Cache expensive reads behind stable keys and short, intentional TTLs.
Invalidate cache near the writes that change the underlying data.
Use tags only when the configured cache store supports them.
Do not cache user-specific or authorization-sensitive data without including the scope in the key.

Transactions And Locks

Keep transactions short and free of slow external calls.
Use row locks only around data that must remain consistent during the write.
Use retry logic for known deadlock-prone flows.
Dispatch jobs, events, notifications, and file cleanup after commit when correctness depends on committed data.

Related Skills

performance-eager-loading
performance-select-columns
performance-caching
data-chunking-large-datasets
migrations-and-factories
database-transactions

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Gives 0 of the 12 instructions most performance cost skills give in 503 tokens

Counted across 797 of the 1,117 authors here whose files we hold, read 2026-09-06

Check for product marketing context firstin 46 of 797, across 20 files
Measure before optimizingin 31 of 797, across 25 files
Profile first to identify the actual bottleneckin 23 of 797, across 22 files
Verify your robots.txt allows AI crawlersin 21 of 797, across 12 files
Import directly and avoid barrel filesin 19 of 797, across 15 files
Spawn all runs in the same turnin 18 of 797, across 11 files
Write a draft of the skillin 17 of 797, across 10 files
Understand the user's intentin 17 of 797, across 10 files
Use React.cache for per-request deduplicationin 16 of 797, across 11 files
Profile before optimizingin 16 of 797, across 14 files
Include specific numbers with sourcesin 15 of 797, across 8 files
Add lazy loading to below-fold imagesin 15 of 797, across 10 files

Said here and by no other author read

Prove the bottleneck before fixing it
Fix query shape before adding infrastructure
Add indexes matching actual query filters
Cache expensive reads with explicit invalidation
Plan migrations with production data in mind

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.