Comprehensive database schema design patterns for PostgreSQL and MySQL with normalization, relationships, constraints, and error prevention.
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AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiondatabase-schema-designExecute the skills CLI command in your project's root directory to begin installation:
Fetches database-schema-design from secondsky/claude-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate database-schema-design. Access via /database-schema-design in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Comprehensive database schema design patterns for PostgreSQL and MySQL with normalization, relationships, constraints, and error prevention.
Step 1: Choose your schema pattern from templates:
# Basic schema with users, products, orders
cat templates/basic-schema.sql
# Relationship patterns (1:1, 1:M, M:M)
cat templates/relationships.sql
# Constraint examples
cat templates/constraints.sql
# Audit patterns
cat templates/audit-columns.sql
Step 2: Apply normalization rules (at minimum 3NF):
references/normalization-guide.md for detailed examplesStep 3: Add essential elements to every table:
CREATE TABLE your_table (
-- Primary key (required)
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
-- Business columns with proper types
name VARCHAR(200) NOT NULL, -- Use appropriate lengths
-- Audit columns (always include)
created_at TIMESTAMPTZ DEFAULT NOW() NOT NULL,
updated_at TIMESTAMPTZ DEFAULT NOW() NOT NULL
);
| Rule | Reason |
|---|---|
| Every table has PRIMARY KEY | Ensures row uniqueness, enables relationships |
| Foreign keys defined explicitly | Enforces referential integrity, prevents orphans |
| Index all foreign keys | Prevents slow JOINs, critical for performance |
| NOT NULL on required fields | Data integrity, prevents NULL pollution |
| Audit columns (created_at, updated_at) | Track changes, debugging, compliance |
| Appropriate data types | Storage efficiency, validation, indexing |
| Check constraints for enums | Enforces valid values at database level |
| ON DELETE/UPDATE rules specified | Prevents accidental data loss or orphans |
| Anti-Pattern | Why It's Bad |
|---|---|
| VARCHAR(MAX) everywhere | Wastes space, slows indexes, no validation |
| Dates as VARCHAR | No date math, no validation, sorting broken |
| Missing foreign keys | No referential integrity, orphaned records |
| Premature denormalization | Hard to maintain, data anomalies |
| EAV (Entity-Attribute-Value) | Query complexity, no type safety, slow |
| Polymorphic associations | No foreign key integrity, complex queries |
| Circular dependencies | Impossible to populate, breaks CASCADE |
| No indexes on foreign keys | Extremely slow JOINs, performance killer |
Symptom: Cannot uniquely identify rows, duplicate data Fix:
-- ❌ Bad
CREATE TABLE users (
email VARCHAR(255),
name VARCHAR(100)
);
-- ✅ Good
CREATE TABLE users (
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
email VARCHAR(255) UNIQUE NOT NULL,
name VARCHAR(100) NOT NULL
);
Symptom: Orphaned records, data inconsistency Fix:
-- ❌ Bad
CREATE TABLE orders (
id UUID PRIMARY KEY,
user_id UUID -- No constraint!
);
-- ✅ Good
CREATE TABLE orders (
id UUID PRIMARY KEY,
user_id UUID NOT NULL REFERENCES users(id) ON DELETE CASCADE
);
-- Index the foreign key
CREATE INDEX idx_orders_user_id ON orders(user_id);
Symptom: Wasted space, slow indexes, no validation Fix:
-- ❌ Bad
CREATE TABLE products (
name VARCHAR(MAX),
sku VARCHAR(MAX),
status VARCHAR(MAX)
);
-- ✅ Good
CREATE TABLE products (
name VARCHAR(200) NOT NULL,
sku VARCHAR(50) UNIQUE NOT NULL,
status VARCHAR(20) NOT NULL
CHECK (status IN ('draft', 'active', 'archived'))
);
Symptom: No date validation, broken sorting, no date math Fix:
-- ❌ Bad
CREATE TABLE events (
event_date VARCHAR(50) -- '2025-12-15' or 'Dec 15, 2025'?
);
-- ✅ Good
CREATE TABLE events (
event_date DATE NOT NULL, -- Validated, sortable
event_time TIMESTAMPTZ -- With timezone
);
Symptom: Extremely slow JOINs, poor query performance Fix:
-- Always index foreign keys
CREATE TABLE order_items (
order_id UUID NOT NULL REFERENCES orders(id),
product_id UUID NOT NULL REFERENCES products(id)
);
-- ✅ Required indexes
CREATE INDEX idx_order_items_order_id ON order_items(order_id);
CREATE INDEX idx_order_items_product_id ON order_items(product_id);
Symptom: Cannot track when records created/modified Fix:
-- ❌ Bad
CREATE TABLE products (
id UUID PRIMARY KEY,
name VARCHAR(200)
);
-- ✅ Good
CREATE TABLE products (
id UUID PRIMARY KEY,
name VARCHAR(200) NOT NULL,
created_at TIMESTAMPTZ DEFAULT NOW() NOT NULL,
updated_at TIMESTAMPTZ DEFAULT NOW() NOT NULL
);
-- Auto-update trigger (PostgreSQL)
CREATE TRIGGER products_updated_at
BEFORE UPDATE ON products
FOR EACH ROW
EXECUTE FUNCTION update_updated_at_column();
Symptom: Complex queries, no type safety, slow performance Fix:
-- ❌ Bad (EAV)
CREATE TABLE product_attributes (
product_id UUID,
attribute_name VARCHAR(100), -- 'color', 'size', 'price'
attribute_value TEXT -- Everything as text!
);
-- ✅ Good (Structured + JSONB)
CREATE TABLE products (
id UUID PRIMARY KEY,
name VARCHAR(200) NOT NULL,
price DECIMAL(10,2) NOT NULL, -- Required fields as columns
color VARCHAR(50), -- Common attributes as columns
size VARCHAR(20),
attributes JSONB -- Optional/dynamic attributes
);
-- Index JSONB
CREATE INDEX idx_products_attributes ON products USING GIN(attributes);
Load references/error-catalog.md for all 12 errors with detailed fixes.
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
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Registry listing for database-schema-design matched our evaluation — installs cleanly and behaves as described in the markdown.
Useful defaults in database-schema-design — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
I recommend database-schema-design for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
database-schema-design is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
database-schema-design is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Useful defaults in database-schema-design — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
database-schema-design reduced setup friction for our internal harness; good balance of opinion and flexibility.
Registry listing for database-schema-design matched our evaluation — installs cleanly and behaves as described in the markdown.
Keeps context tight: database-schema-design is the kind of skill you can hand to a new teammate without a long onboarding doc.
Keeps context tight: database-schema-design is the kind of skill you can hand to a new teammate without a long onboarding doc.
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