database-design
Database design principles and decision-making. Schema design, indexing strategy, ORM selection, serverless databases.
- License 许可
- MIT
- 最近更新
- 2026-03-28
- Stars
- ★ 0
- Forks
- 2
01 前言 Front Matter
- name
- database-design
- description
- Database design principles and decision-making. Schema design, indexing strategy, ORM selection, serverless databases.
- allowed-tools
- Read, Write, Edit, Glob, Grep
02 资源文件 7
- 参考文档database-selection.md1.1 KB
- 参考文档indexing.md892 B
- 参考文档migrations.md1.1 KB
- 参考文档optimization.md902 B
- 参考文档orm-selection.md771 B
- 参考文档schema-design.md1.4 KB
- 脚本scripts/schema_validator.py5.2 KB
03 SKILL.md 正文
Database Design
Learn to THINK, not copy SQL patterns.
? Selective Reading Rule
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 |
⚠️ Core Principle
- ASK user for database preferences when unclear
- Choose database/ORM based on CONTEXT
- Don't default to PostgreSQL for everything
Decision Checklist
Before designing schema:
- [ ] Asked user about database preference?
- [ ] Chosen database for THIS context?
- [ ] Considered deployment environment?
- [ ] Planned index strategy?
- [ ] Defined relationship types?
Anti-Patterns
❌ Default to PostgreSQL for simple apps (SQLite may suffice) ❌ Skip indexing ❌ Use SELECT * in production ❌ Store JSON when structured data is better ❌ Ignore N+1 queries
04 同仓库更多 Skills
zapier-make-patterns
No-code automation democratizes workflow building. Zapier and Make (formerly Integromat) let non-developers automate business processes without writing code. But no-code doesn't mean no-complexity - these platforms have their own patterns, pitfalls, and breaking points. This skill covers when to use which platform, how to build reliable automations, and when to graduate to code-based solutions. Key insight: Zapier optimizes for simplicity and integrations (7000+ apps), Make optimizes for power
yeet
Use only when the user explicitly asks to stage, commit, push, and open a GitHub pull request in one flow using the GitHub CLI (`gh`).
workflow-automation
Workflow automation is the infrastructure that makes AI agents reliable. Without durable execution, a network hiccup during a 10-step payment flow means lost money and angry customers. With it, workflows resume exactly where they left off. This skill covers the platforms (n8n, Temporal, Inngest) and patterns (sequential, parallel, orchestrator-worker) that turn brittle scripts into production-grade automation. Key insight: The platforms make different tradeoffs. n8n optimizes for accessibility
trigger-dev
Trigger.dev expert for background jobs, AI workflows, and reliable async execution with excellent developer experience and TypeScript-first design. Use when: trigger.dev, trigger dev, background task, ai background job, long running task.
planning-with-files
Transforms workflow to use Manus-style persistent markdown files for planning, progress tracking, and knowledge storage. Use when starting complex tasks, multi-step projects, research tasks, or when the user mentions planning, organizing work, tracking progress, or wants structured output.
n8n-workflow-patterns
Proven workflow architectural patterns from real n8n workflows. Use when building new workflows, designing workflow structure, choosing workflow patterns, planning workflow architecture, or asking about webhook processing, HTTP API integration, database operations, AI agent workflows, or scheduled tasks.