@claudeautopm/plugin-databases

@claudeautopm/plugin-databases

Database and data storage specialists for PostgreSQL, MongoDB, Redis, and more.

Installation

# Install the plugin package
npm install -g @claudeautopm/plugin-databases

# Install plugin agents to your project
autopm plugin install databases

Agents Included

Relational Databases

• postgresql-expert - PostgreSQL database specialist
  • Query optimization and indexing
  • Table design and normalization
  • Transactions and ACID compliance
  • Replication and high availability
  • Performance tuning

NoSQL Databases

• mongodb-expert - MongoDB database specialist

  • Document schema design
  • Aggregation pipelines
  • Sharding and replication
  • Query optimization
  • Atlas cloud management

• cosmosdb-expert - Azure Cosmos DB specialist

  • Multi-model database design
  • Consistency levels
  • Partitioning strategies
  • Global distribution
  • Change feed patterns

Caching & In-Memory

• redis-expert - Redis caching and data structures
  • Cache strategies (LRU, TTL)
  • Data structures (strings, sets, sorted sets, hashes)
  • Pub/Sub messaging
  • Redis Cluster and Sentinel
  • Performance optimization

Analytics & Big Data

• bigquery-expert - Google BigQuery analytics
  • SQL query optimization
  • Partitioning and clustering
  • Streaming inserts
  • Cost optimization
  • Data warehouse design

Usage

In Claude Code

After installation, agents are available in your project:

<!-- CLAUDE.md -->
## Active Team Agents

<!-- Load database agents -->
- @include .claude/agents/databases/postgresql-expert.md
- @include .claude/agents/databases/redis-expert.md

Or use autopm team load to automatically include agents:

# Load database-focused team
autopm team load databases

# Or include databases in fullstack team
autopm team load fullstack

Direct Invocation

# Invoke agent directly from CLI
autopm agent invoke postgresql-expert "Optimize slow query performance"

Agent Capabilities

Database Design

• Schema design and normalization
• Indexing strategies
• Partitioning and sharding
• Data modeling best practices

Performance Optimization

• Query optimization
• Index tuning
• Connection pooling
• Caching strategies

High Availability

• Replication setup
• Failover strategies
• Backup and recovery
• Disaster recovery planning

Data Migration

• Schema migration
• Data transformation
• Zero-downtime migrations
• Cross-database migration

MCP Servers

This plugin works with the following MCP servers for enhanced capabilities:

• postgresql - PostgreSQL documentation and query patterns
• mongodb - MongoDB documentation and best practices

Enable MCP servers:

autopm mcp enable postgresql
autopm mcp enable mongodb

Examples

PostgreSQL Query Optimization

@postgresql-expert

Optimize slow-running query:

Query:
SELECT o.*, u.name, p.title
FROM orders o
JOIN users u ON o.user_id = u.id
JOIN products p ON o.product_id = p.id
WHERE o.created_at >= '2024-01-01'
ORDER BY o.created_at DESC
LIMIT 100

Issues:
- Takes 5+ seconds on 10M rows
- High CPU usage
- Blocking other queries

Provide:
1. Query analysis with EXPLAIN
2. Index recommendations
3. Optimized query
4. Performance benchmarks

MongoDB Schema Design

@mongodb-expert

Design schema for e-commerce platform:

Requirements:
- Products with variants (color, size)
- Inventory tracking per variant
- Customer reviews and ratings
- Order history
- Fast product search

Optimize for:
- Read-heavy workload (90% reads)
- Complex product filtering
- Real-time inventory updates
- Aggregated review statistics

Include:
1. Collection schemas
2. Indexing strategy
3. Aggregation pipelines
4. Sharding recommendations

Redis Caching Strategy

@redis-expert

Implement caching layer for API:

Requirements:
- Cache frequently accessed data
- Invalidate on updates
- Handle cache stampede
- Session storage
- Rate limiting

Patterns needed:
- Cache-aside pattern
- Write-through cache
- Distributed locking
- Pub/Sub for invalidation

Include:
1. Redis data structure choices
2. TTL strategies
3. Invalidation patterns
4. Performance metrics

BigQuery Analytics

@bigquery-expert

Design data warehouse for analytics:

Data sources:
- Application logs (1TB/day)
- User events (100M events/day)
- Sales transactions (10M/day)

Requirements:
- Real-time dashboard queries
- Historical trend analysis
- Customer segmentation
- Cost optimization

Include:
1. Table design with partitioning
2. Clustering strategy
3. Materialized views
4. Cost-optimized queries
5. Streaming insert patterns

Cosmos DB Multi-Region Setup

@cosmosdb-expert

Setup globally distributed database:

Requirements:
- 3 regions (US, EU, Asia)
- Strong consistency for writes
- Eventual consistency for reads
- Automatic failover
- Conflict resolution

Collections:
- Users (partition by country)
- Orders (partition by date)
- Products (small, replicated globally)

Include:
1. Consistency level configuration
2. Partition key strategy
3. Conflict resolution policies
4. Failover configuration
5. Cost estimation

Configuration

Environment Variables

Some agents benefit from environment variables:

# PostgreSQL
export PGHOST=localhost
export PGDATABASE=myapp
export PGUSER=postgres

# MongoDB
export MONGODB_URI=mongodb://localhost:27017/myapp

# Redis
export REDIS_URL=redis://localhost:6379

# BigQuery
export GOOGLE_CLOUD_PROJECT=my-project
export BIGQUERY_DATASET=analytics

Agent Customization

You can customize agent behavior in .claude/config.yaml:

plugins:
  databases:
    postgresql:
      default_pool_size: 20
      statement_timeout: 30s
    mongodb:
      read_preference: secondaryPreferred
      write_concern: majority
    redis:
      default_ttl: 3600
      eviction_policy: allkeys-lru
    bigquery:
      default_location: US
      max_query_cost: 10

Documentation

• PostgreSQL Expert Guide
• MongoDB Expert Guide
• Redis Expert Guide
• BigQuery Expert Guide
• Cosmos DB Expert Guide

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

License

MIT ClaudeAutoPM Team

Links

• ClaudeAutoPM
• Plugin Documentation
• npm Package
• Issues