postgresql-optimization
PostgreSQL database optimization workflow for query tuning, indexing strategies, performance analysis, and production database management.
Best use case
postgresql-optimization is best used when you need a repeatable AI agent workflow instead of a one-off prompt. It is especially useful for teams working in multi. PostgreSQL database optimization workflow for query tuning, indexing strategies, performance analysis, and production database management.
PostgreSQL database optimization workflow for query tuning, indexing strategies, performance analysis, and production database management.
Users should expect a more consistent workflow output, faster repeated execution, and less time spent rewriting prompts from scratch.
Practical example
Example input
Use the "postgresql-optimization" skill to help with this workflow task. Context: PostgreSQL database optimization workflow for query tuning, indexing strategies, performance analysis, and production database management.
Example output
A structured workflow result with clearer steps, more consistent formatting, and an output that is easier to reuse in the next run.
When to use this skill
- Use this skill when you want a reusable workflow rather than writing the same prompt again and again.
When not to use this skill
- Do not use this when you only need a one-off answer and do not need a reusable workflow.
- Do not use it if you cannot install or maintain the related files, repository context, or supporting tools.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/postgresql-optimization/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How postgresql-optimization Compares
| Feature / Agent | postgresql-optimization | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
PostgreSQL database optimization workflow for query tuning, indexing strategies, performance analysis, and production database management.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# 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
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