Guides schema design, indexing, ORM (Drizzle, Prisma) and database (PostgreSQL, SQLite, Neon) selection with checklists, anti-patterns, and optimization for context-specific apps.
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database-selection.mdindexing.mdmigrations.mdoptimization.mdorm-selection.mdschema-design.mdscripts/schema_validator.pyDesigns and optimizes AI agent action spaces, tool definitions, observation formats, error recovery, and context for higher task completion rates.
Enables AI agents to execute x402 payments with per-task budgets, spending controls, and non-custodial wallets via MCP tools. Use when agents pay for APIs, services, or other agents.
Compares coding agents like Claude Code and Aider on custom YAML-defined codebase tasks using git worktrees, measuring pass rate, cost, time, and consistency.
Learn to THINK, not copy SQL patterns.
Read ONLY files relevant to the request! Check the content map, find what you need.
| File | Description | When to Read |
|---|---|---|
database-selection.md | PostgreSQL vs Neon vs Turso vs SQLite | Choosing database |
orm-selection.md | Drizzle vs Prisma vs Kysely | Choosing ORM |
schema-design.md | Normalization, PKs, relationships | Designing schema |
indexing.md | Index types, composite indexes | Performance tuning |
optimization.md | N+1, EXPLAIN ANALYZE | Query optimization |
migrations.md | Safe migrations, serverless DBs | Schema changes |
Before designing schema:
ā Default to PostgreSQL for simple apps (SQLite may suffice) ā Skip indexing ā Use SELECT * in production ā Store JSON when structured data is better ā Ignore N+1 queries
This skill is applicable to execute the workflow or actions described in the overview.