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Master relational and NoSQL databases. Learn PostgreSQL, MySQL, MongoDB, Redis, and other technologies for data persistence, optimization, and scaling.

Install Skill

1Download skill
2Enable skills in Claude

Open claude.ai/settings/capabilities and find the "Skills" section

3Upload to Claude

Click "Upload skill" and select the downloaded ZIP file

Note: Please verify skill by going through its instructions before using it.

SKILL.md

name databases
description Master relational and NoSQL databases. Learn PostgreSQL, MySQL, MongoDB, Redis, and other technologies for data persistence, optimization, and scaling.
sasmp_version 2.0.0
bonded_agent database-management-agent
bond_type PRIMARY_BOND
atomic_operations DATABASE_SELECTION, SCHEMA_DESIGN, QUERY_OPTIMIZATION, BACKUP_CONFIGURATION
parameter_validation [object Object]
retry_logic [object Object]
logging_hooks [object Object]
exit_codes [object Object]

Database Management Skill

Bonded to: database-management-agent


Quick Start

# Invoke databases skill
"Design a database schema for my e-commerce application"
"Optimize slow queries in PostgreSQL"
"Set up Redis caching for session storage"

Instructions

  1. Analyze Requirements: Understand data patterns, volume, access needs
  2. Select Database: Choose SQL vs NoSQL based on requirements
  3. Design Schema: Create data models, relationships, constraints
  4. Optimize Queries: Implement indexes, analyze execution plans
  5. Set Up Operations: Configure backup, replication, monitoring

Database Selection Guide

Type Best For ACID Scale Examples
Relational Complex queries, transactions Full Vertical PostgreSQL, MySQL
Document Flexible schema, JSON Partial Horizontal MongoDB
Key-Value Caching, sessions No Horizontal Redis
Wide-Column Time series, analytics Partial Horizontal Cassandra
Graph Relationships Varies Varies Neo4j
Search Full-text search No Horizontal Elasticsearch

Decision Tree

Need ACID transactions?
    │
    ├─→ Yes → Complex queries?
    │           ├─→ Yes → PostgreSQL
    │           └─→ No  → MySQL
    │
    └─→ No  → Data type?
              ├─→ Documents/JSON → MongoDB
              ├─→ Key-Value pairs → Redis
              ├─→ Time series → Cassandra/TimescaleDB
              └─→ Full-text search → Elasticsearch

Examples

Example 1: Schema Design

-- E-commerce schema
CREATE TABLE users (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    email VARCHAR(255) UNIQUE NOT NULL,
    password_hash VARCHAR(255) NOT NULL,
    created_at TIMESTAMPTZ DEFAULT NOW()
);

CREATE TABLE products (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    name VARCHAR(255) NOT NULL,
    price DECIMAL(10,2) NOT NULL CHECK (price > 0),
    stock INTEGER NOT NULL DEFAULT 0
);

CREATE TABLE orders (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    user_id UUID REFERENCES users(id),
    total DECIMAL(10,2) NOT NULL,
    status VARCHAR(50) DEFAULT 'pending',
    created_at TIMESTAMPTZ DEFAULT NOW()
);

-- Indexes for common queries
CREATE INDEX idx_orders_user_id ON orders(user_id);
CREATE INDEX idx_orders_created_at ON orders(created_at DESC);

Example 2: Query Optimization

-- Before: Full table scan
EXPLAIN ANALYZE SELECT * FROM orders WHERE user_id = 'abc123';

-- After: Add index
CREATE INDEX idx_orders_user_id ON orders(user_id);

-- Optimized query with selected columns
SELECT id, total, status, created_at
FROM orders
WHERE user_id = 'abc123'
ORDER BY created_at DESC
LIMIT 10;

Example 3: Redis Caching

import redis
import json

r = redis.Redis(host='localhost', port=6379, decode_responses=True)

def get_user(user_id: str) -> dict:
    # Try cache first
    cached = r.get(f"user:{user_id}")
    if cached:
        return json.loads(cached)

    # Cache miss - fetch from DB
    user = db.query(User).get(user_id)
    if user:
        r.setex(f"user:{user_id}", 3600, json.dumps(user.dict()))
    return user.dict()

Troubleshooting

Common Issues

Issue Cause Solution
Query timeout Missing index Run EXPLAIN ANALYZE, add index
Connection refused Wrong config Check host, port, credentials
Deadlock Concurrent updates Use proper isolation, retry logic
OOM on query Large result set Add LIMIT, use cursors

Debug Commands

-- PostgreSQL: Check slow queries
SELECT query, calls, mean_time
FROM pg_stat_statements
ORDER BY mean_time DESC LIMIT 10;

-- Check active connections
SELECT * FROM pg_stat_activity WHERE state = 'active';

-- Check table sizes
SELECT relname, pg_size_pretty(pg_total_relation_size(relid))
FROM pg_stat_user_tables
ORDER BY pg_total_relation_size(relid) DESC;

Test Template

# tests/test_database.py
import pytest
from sqlalchemy import create_engine

class TestDatabaseSchema:
    @pytest.fixture
    def engine(self):
        return create_engine("postgresql://test:test@localhost/testdb")

    def test_users_table_exists(self, engine):
        result = engine.execute("SELECT 1 FROM users LIMIT 1")
        assert result is not None

    def test_foreign_key_constraint(self, engine):
        with pytest.raises(IntegrityError):
            engine.execute(
                "INSERT INTO orders (user_id, total) VALUES ('invalid-uuid', 100)"
            )

References

See references/ directory for:

  • DATABASE_GUIDE.md - Detailed database patterns
  • Schema templates and examples

Resources