database-schema-designer
Use when the user asks to create ERD diagrams, normalize database schemas, design table relationships, or plan schema migrations.
- License 许可
- MIT
- 最近更新
- 2026-07-04
- Stars
- ★ 0
- Forks
- 0
01 前言 Front Matter
- name
- database-schema-designer
- description
- Use when the user asks to create ERD diagrams, normalize database schemas, design table relationships, or plan schema migrations.
02 资源文件 1
- 参考文档references/full-schema-examples.md10.2 KB
03 SKILL.md 正文
Database Schema Designer
Tier: POWERFUL
Category: Engineering
Domain: Data Architecture / Backend
Overview
Design relational database schemas from requirements and generate migrations, TypeScript/Python types, seed data, RLS policies, and indexes. Handles multi-tenancy, soft deletes, audit trails, versioning, and polymorphic associations.
Core Capabilities
- Schema design — normalize requirements into tables, relationships, constraints
- Migration generation — Drizzle, Prisma, TypeORM, Alembic
- Type generation — TypeScript interfaces, Python dataclasses/Pydantic models
- RLS policies — Row-Level Security for multi-tenant apps
- Index strategy — composite indexes, partial indexes, covering indexes
- Seed data — realistic test data generation
- ERD generation — Mermaid diagram from schema
When to Use
- Designing a new feature that needs database tables
- Reviewing a schema for performance or normalization issues
- Adding multi-tenancy to an existing schema
- Generating TypeScript types from a Prisma schema
- Planning a schema migration for a breaking change
Schema Design Process
Step 1: Requirements → Entities
Given requirements:
"Users can create projects. Each project has tasks. Tasks can have labels. Tasks can be assigned to users. We need a full audit trail."
Extract entities:
User, Project, Task, Label, TaskLabel (junction), TaskAssignment, AuditLog
Step 2: Identify Relationships
User 1──* Project (owner)
Project 1──* Task
Task *──* Label (via TaskLabel)
Task *──* User (via TaskAssignment)
User 1──* AuditLog
Step 3: Add Cross-cutting Concerns
- Multi-tenancy: add
organization_idto all tenant-scoped tables - Soft deletes: add
deleted_at TIMESTAMPTZinstead of hard deletes - Audit trail: add
created_by,updated_by,created_at,updated_at - Versioning: add
version INTEGERfor optimistic locking
Full Schema Example (Task Management SaaS)
→ See references/full-schema-examples.md for details
Row-Level Security (RLS) Policies
-- Enable RLS
ALTER TABLE tasks ENABLE ROW LEVEL SECURITY;
ALTER TABLE projects ENABLE ROW LEVEL SECURITY;
-- Create app role
CREATE ROLE app_user;
-- Users can only see tasks in their organization's projects
CREATE POLICY tasks_org_isolation ON tasks
FOR ALL TO app_user
USING (
project_id IN (
SELECT p.id FROM projects p
JOIN organization_members om ON om.organization_id = p.organization_id
WHERE om.user_id = current_setting('app.current_user_id')::text
)
);
-- Soft delete: never show deleted records
CREATE POLICY tasks_no_deleted ON tasks
FOR SELECT TO app_user
USING (deleted_at IS NULL);
-- Only task creator or admin can delete
CREATE POLICY tasks_delete_policy ON tasks
FOR DELETE TO app_user
USING (
created_by_id = current_setting('app.current_user_id')::text
OR EXISTS (
SELECT 1 FROM organization_members om
JOIN projects p ON p.organization_id = om.organization_id
WHERE p.id = tasks.project_id
AND om.user_id = current_setting('app.current_user_id')::text
AND om.role IN ('owner', 'admin')
)
);
-- Set user context (call at start of each request)
SELECT set_config('app.current_user_id', $1, true);
Seed Data Generation
// db/seed.ts
import { faker } from '@faker-js/faker'
import { db } from './client'
import { organizations, users, projects, tasks } from './schema'
import { createId } from '@paralleldrive/cuid2'
import { hashPassword } from '../src/lib/auth'
async function seed() {
console.log('Seeding database...')
// Create org
const [org] = await db.insert(organizations).values({
id: createId(),
name: "acme-corp",
slug: 'acme',
plan: 'growth',
}).returning()
// Create users
const adminUser = await db.insert(users).values({
id: createId(),
email: 'admin@acme.com',
name: "alice-admin",
passwordHash: await hashPassword('password123'),
}).returning().then(r => r[0])
// Create projects
const projectsData = Array.from({ length: 3 }, () => ({
id: createId(),
organizationId: org.id,
ownerId: adminUser.id,
name: "fakercompanycatchphrase"
description: faker.lorem.paragraph(),
status: 'active' as const,
}))
const createdProjects = await db.insert(projects).values(projectsData).returning()
// Create tasks for each project
for (const project of createdProjects) {
const tasksData = Array.from({ length: faker.number.int({ min: 5, max: 20 }) }, (_, i) => ({
id: createId(),
projectId: project.id,
title: faker.hacker.phrase(),
description: faker.lorem.sentences(2),
status: faker.helpers.arrayElement(['todo', 'in_progress', 'done'] as const),
priority: faker.helpers.arrayElement(['low', 'medium', 'high'] as const),
position: i * 1000,
createdById: adminUser.id,
updatedById: adminUser.id,
}))
await db.insert(tasks).values(tasksData)
}
console.log(`✅ Seeded: 1 org, ${projectsData.length} projects, tasks`)
}
seed().catch(console.error).finally(() => process.exit(0))
ERD Generation (Mermaid)
erDiagram
Organization ||--o{ OrganizationMember : has
Organization ||--o{ Project : owns
User ||--o{ OrganizationMember : joins
User ||--o{ Task : "created by"
Project ||--o{ Task : contains
Task ||--o{ TaskAssignment : has
Task ||--o{ TaskLabel : has
Task ||--o{ Comment : has
Task ||--o{ Attachment : has
Label ||--o{ TaskLabel : "applied to"
User ||--o{ TaskAssignment : assigned
Organization {
string id PK
string name
string slug
string plan
}
Task {
string id PK
string project_id FK
string title
string status
string priority
timestamp due_date
timestamp deleted_at
int version
}
Generate from Prisma:
npx prisma-erd-generator
# or: npx @dbml/cli prisma2dbml -i schema.prisma | npx dbml-to-mermaid
Common Pitfalls
- Soft delete without index —
WHERE deleted_at IS NULLwithout index = full scan - Missing composite indexes —
WHERE org_id = ? AND status = ?needs a composite index - Mutable surrogate keys — never use email or slug as PK; use UUID/CUID
- Non-nullable without default — adding a NOT NULL column to existing table requires default or migration plan
- No optimistic locking — concurrent updates overwrite each other; add
versioncolumn - RLS not tested — always test RLS with a non-superuser role
Best Practices
- Timestamps everywhere —
created_at,updated_aton every table - Soft deletes for auditable data —
deleted_atinstead of DELETE - Audit log for compliance — log before/after JSON for regulated domains
- UUIDs or CUIDs as PKs — avoid sequential integer leakage
- Index foreign keys — every FK column should have an index
- Partial indexes — use
WHERE deleted_at IS NULLfor active-only queries - RLS over application-level filtering — database enforces tenancy, not just app code
04 同仓库更多 Skills
syllabus
Generates a curated supplementary reading list from any course syllabus using Consensus academic search. Grill-me intake (syllabus input format + course audience + year range) plus a grouping forcing-options checkpoint before any search runs — so the reading list matches the course's level and recency need. Parses the syllabus to extract topics and learning outcomes, searches Consensus for recent peer-reviewed papers per topic, and produces a professionally formatted .docx with clickable Consensus links, plain-language summaries calibrated to audience level, and Bloom-higher-order discussion questions tied to course learning goals. Use when the user uploads a syllabus, course outline, or curriculum document and wants supplementary readings (e.g., 'create a reading list from this syllabus', 'find recent papers for my course') — even casual mentions with a syllabus attached should trigger this skill.
research
Default entry point for any research request — a hybrid router that classifies the question deterministically and either delegates to a specialist research skill (pulse for trends/sentiment, grants for NIH funding, litreview for academic literature, syllabus for course reading, patent for prior-art + IP landscape, dossier for entity research) or runs its own plan-decompose-multi-source-search-synthesize-cite fallback workflow when no specialist matches. Always surfaces the routing decision so users can override. Use when the user makes any research request that doesn't obviously match a more-specific specialist skill (e.g., "research [topic]", "look into [topic]", "what do we know about [topic]", "investigate [topic]", "find me information on [topic]", "do some research on [topic]", "I need to understand [topic]"). Output is a markdown briefing (default) or .docx document (on request) with full citations and an audit log.
pulse
Multi-source recency research skill that takes the pulse of any topic across Reddit, Hacker News, the open web, and optionally X/Twitter within a configurable recent window (default 30 days). Forcing intake clarifies topic specificity, angle (trend/sentiment/problems/opportunities/comparison), time window, and platform scope before searching. Returns a synthesized briefing with citations, engagement metrics, and cross-platform pattern analysis. Use when the user requests multi-source recency intelligence on a topic (e.g., 'pulse on [topic]', 'what's happening with [topic]', 'what are people saying about [topic]', 'current conversation about [topic]', 'take the pulse of [topic]', 'trending: [topic]', 'find me info on [topic]'), and for competitor research, trend discovery, tool comparisons, and audience sentiment analysis.
patent
Patent prior-art and landscape intelligence skill — not generic patent help. Commits to one of five sub-use-cases via forcing intake (novelty search / freedom-to-operate / competitive landscape / acquisition diligence / litigation prior-art) before any search runs. Searches Google Patents, Espacenet, USPTO, and optionally Lens.org for citation-graph signals. Output is an editable Word document (.docx) with verdict, ranked closest art (claim-text extracted), CPC-class-aware landscape, family-resolved hits, geographic coverage, FTO flags where applicable, strategy recommendations, and full audit log. Use when the user asks for patent searching or analysis (e.g., 'prior art search for [invention]', 'freedom to operate analysis for [product]'). Produces search signal, not legal advice — always recommends consulting a patent attorney before filing or licensing decisions. Trademark, copyright, and trade-secret questions are out of scope.
notebooklm
Browser automation skill for controlling Google's NotebookLM. Use when the user wants anything done in NotebookLM (e.g., 'open NotebookLM', 'check my [name] notebook', 'ask my notebook about X', 'add [source] to NotebookLM', 'generate a Video Overview from my notebook', 'use NotebookLM Studio'). Handles reading and querying notebooks, adding sources (URLs, text, files, YouTube links, synthesized content), generating Studio outputs (Audio/Video Overviews, Mind Maps, Reports incl. Briefing Doc/Study Guide/FAQ, Flashcards, Quiz, slide decks, infographics — discover the exact set from the live Studio panel; the UI evolves fast), and creating new notebooks. Requires browser automation environment — fails gracefully when unavailable.
litreview
Academic literature orientation skill that searches papers via free keyless APIs (PubMed E-utilities + OpenAlex) by default — with the Consensus MCP as an optional enhancement lane when connected — builds a strategic search plan using PICO (default) or SPIDER / Decomposition / hybrid as fallbacks, and synthesizes findings into a formatted Word (.docx) research guide. Grill-me intake (research question specificity + framework hint + tentative depth) before the recon search; a second forcing checkpoint after Phase 2 confirms framework + sub-areas + depth before searches consume budget. Configurable depth (5/10/20 queries) controls coverage vs. speed. Output is a 'launching pad' — an orientation guide that lets a researcher dive in confidently, not a finished review. Use when the user starts literature-oriented research (e.g., 'litreview on [topic]', 'literature review on [topic]', 'I'm starting a literature review on X', 'I'm writing a paper on X', 'help me research X', 'I'm doing research on X', 'can you help me research X'). Do NOT use for single one-off paper searches wanting a quick list — that's a plain PubMed/OpenAlex (or Consensus) query.