database-schema-design▌
secondsky/claude-skills · updated Apr 8, 2026
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Comprehensive database schema design patterns for PostgreSQL and MySQL with normalization, relationships, constraints, and error prevention.
database-schema-design
Comprehensive database schema design patterns for PostgreSQL and MySQL with normalization, relationships, constraints, and error prevention.
Quick Start (10 Minutes)
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):
- 1NF: No repeating groups, atomic values
- 2NF: No partial dependencies on composite keys
- 3NF: No transitive dependencies
- Load
references/normalization-guide.mdfor detailed examples
Step 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
);
Critical Rules
✓ Always Do
| 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 |
✗ Never Do
| 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 |
Top 7 Critical Errors
Error 1: Missing Primary Key
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
);
Error 2: No Foreign Key Constraints
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);
Error 3: VARCHAR(MAX) Everywhere
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'))
);
Error 4: Wrong Data Types (Dates as Strings)
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
);
Error 5: No Indexes on Foreign Keys
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);
Error 6: Missing Audit Columns
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();
Error 7: EAV Anti-Pattern
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.
Common Schema Patterns
<How to use database-schema-design on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add database-schema-design
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches database-schema-design from GitHub repository secondsky/claude-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate database-schema-design. Access the skill through slash commands (e.g., /database-schema-design) or your agent's skill management interface.
Security & Verification Notice
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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ 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.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.8★★★★★42 reviews- ★★★★★Arjun Harris· Dec 28, 2024
Registry listing for database-schema-design matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Lucas Taylor· Dec 24, 2024
Useful defaults in database-schema-design — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Omar Okafor· Dec 12, 2024
I recommend database-schema-design for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Advait Huang· Nov 27, 2024
database-schema-design is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Rahul Santra· Nov 23, 2024
database-schema-design is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Arjun Bhatia· Nov 19, 2024
Useful defaults in database-schema-design — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Omar Park· Nov 3, 2024
database-schema-design reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Noor Bansal· Oct 22, 2024
Registry listing for database-schema-design matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Jin Sanchez· Oct 18, 2024
Keeps context tight: database-schema-design is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Pratham Ware· Oct 14, 2024
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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