Postgresql Optimization

PostgreSQL Optimization Workflow

Overview

Specialized workflow for PostgreSQL database optimization including query tuning, indexing strategies, performance analysis, vacuum management, and production database administration.

When to Use This Workflow

Use this workflow when:

• Optimizing slow PostgreSQL queries
• Designing indexing strategies
• Analyzing database performance
• Tuning PostgreSQL configuration
• Managing production databases

Workflow Phases

Phase 1: Performance Assessment

Skills to Invoke

• database-optimizer - Database optimization
• postgres-best-practices - PostgreSQL best practices

Actions

1. Check database version
2. Review configuration
3. Analyze slow queries
4. Check resource usage
5. Identify bottlenecks

Copy-Paste Prompts

Use @database-optimizer to assess PostgreSQL performance

Phase 2: Query Analysis

Skills to Invoke

• sql-optimization-patterns - SQL optimization
• postgres-best-practices - PostgreSQL patterns

Actions

1. Run EXPLAIN ANALYZE
2. Identify scan types
3. Check join strategies
4. Analyze execution time
5. Find optimization opportunities

Copy-Paste Prompts

Use @sql-optimization-patterns to analyze and optimize queries

Phase 3: Indexing Strategy

Skills to Invoke

• database-design - Index design
• postgresql - PostgreSQL indexing

Actions

1. Identify missing indexes
2. Create B-tree indexes
3. Add composite indexes
4. Consider partial indexes
5. Review index usage

Copy-Paste Prompts

Use @database-design to design PostgreSQL indexing strategy

Phase 4: Query Optimization

Skills to Invoke

• sql-optimization-patterns - Query tuning
• sql-pro - SQL expertise

Actions

1. Rewrite inefficient queries
2. Optimize joins
3. Add CTEs where helpful
4. Implement pagination
5. Test improvements

Copy-Paste Prompts

Use @sql-optimization-patterns to optimize SQL queries

Phase 5: Configuration Tuning

Skills to Invoke

• postgres-best-practices - Configuration
• database-admin - Database administration

Actions

1. Tune shared_buffers
2. Configure work_mem
3. Set effective_cache_size
4. Adjust checkpoint settings
5. Configure autovacuum

Copy-Paste Prompts

Use @postgres-best-practices to tune PostgreSQL configuration

Phase 6: Maintenance

Skills to Invoke

• database-admin - Database maintenance
• postgresql - PostgreSQL maintenance

Actions

1. Schedule VACUUM
2. Run ANALYZE
3. Check table bloat
4. Monitor autovacuum
5. Review statistics

Copy-Paste Prompts

Use @database-admin to schedule PostgreSQL maintenance

Phase 7: Monitoring

Skills to Invoke

• grafana-dashboards - Monitoring dashboards
• prometheus-configuration - Metrics collection

Actions

1. Set up monitoring
2. Create dashboards
3. Configure alerts
4. Track key metrics
5. Review trends

Copy-Paste Prompts

Use @grafana-dashboards to create PostgreSQL monitoring

Optimization Checklist

• Slow queries identified
• Indexes optimized
• Configuration tuned
• Maintenance scheduled
• Monitoring active
• Performance improved

Quality Gates

• Query performance improved
• Indexes effective
• Configuration optimized
• Maintenance automated
• Monitoring in place

Related Workflow Bundles

• database - Database operations
• cloud-devops - Infrastructure
• performance-optimization - Performance

AGI Framework Integration

Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills

Memory-First Protocol

Cache workflow configurations and automation patterns. Retrieve prior pipeline designs to avoid re-building similar flows from scratch.

# Check for prior workflow/automation context before starting
python3 execution/memory_manager.py auto --query "automation patterns and workflow configurations for Postgresql Optimization"

Storing Results

After completing work, store workflow/automation decisions for future sessions:

python3 execution/memory_manager.py store \
  --content "Workflow: automated data pipeline with retry logic, dead-letter queue, and Slack alerts on failure" \
  --type technical --project <project> \
  --tags postgresql-optimization workflow

Multi-Agent Collaboration

Share workflow state with other agents so they can trigger, monitor, or extend the automation.

python3 execution/cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "Workflow automation deployed — pipeline processing 1000+ events/day with 99.9% success rate" \
  --project <project>

Playbook Engine

Combine this skill with others using the Playbook Engine (execution/workflow_engine.py) for guided multi-step automation with progress tracking.