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database-optimizer

Optimizes database queries and improves performance across PostgreSQL and MySQL systems.

Skill by https://github.com/Jeffallan

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About

Optimizes database queries and improves performance across PostgreSQL and MySQL systems. Use when investigating slow queries, analyzing execution plans, or optimizing database performance. Invoke for index design, query rewrites, configuration tuning, partitioning strategies, lock contention resolution.

Use cases: design, backend

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What's inside

Skill name
database-optimizer
Description
Optimizes database queries and improves performance across PostgreSQL and MySQL systems. Use when investigating slow queries, analyzing execution plans, or optimizing database performance. Invoke for index design, query rewrites, configuration tuning, partitioning strategies, lock contention resolution.
License
MIT
Metadata
role: specialist · scope: optimization · author: https://github.com/Jeffallan · domain: infrastructure · version: 1.1.1 · triggers: database optimization, slow query, query performance, database tuning, index optimization, execution plan, EXPLAIN ANALYZE, database performance, PostgreSQL optimization, MySQL optimization · output-format: analysis-and-code · related-skills: devops-engineer, postgres-pro, graphql-architect
Instructions
136 lines in SKILL.md · view on GitHub

Files (6)

  • SKILL.md
  • index-strategies.md · references/
  • monitoring-analysis.md · references/
  • mysql-tuning.md · references/
  • postgresql-tuning.md · references/
  • query-optimization.md · references/

SKILL.md (first lines)

# Database Optimizer

Senior database optimizer with expertise in performance tuning, query optimization, and scalability across multiple database systems.

## When to Use This Skill

- Analyzing slow queries and execution plans
- Designing optimal index strategies
- Tuning database configuration parameters
- Optimizing schema design and partitioning
- Reducing lock contention and deadlocks
- Improving cache hit rates and memory usage

## Core Workflow

1. **Analyze Performance** — Capture baseline metrics and run `EXPLAIN ANALYZE` before any changes
2. **Identify Bottlenecks** — Find inefficient queries, missing indexes, config issues
3. **Design Solutions** — Create index strategies, query rewrites, schema improvements
4. **Implement Changes** — Apply optimizations incrementally with monitoring; validate each change before proceeding to the next
5. **Validate Results** — Re-run `EXPLAIN ANALYZE`, compare costs, measure wall-clock improvement, document changes

> ⚠️ Always test changes in non-production first. Revert immediately if write performance degrades or replication lag increases.

## Reference Guide

Load detailed guidance based on context:

| Topic | Reference | Load When |
|-------|-----------|-----------|
| Query Optimization | `references/query-optimization.md` | Analyzing slow queries, execution plans |
| Index Strategies | `references/index-strategies.md` | Designing indexes, covering indexes |
| PostgreSQL Tuning | `references/postgresql-tuning.md` | PostgreSQL-specific optimizations |
| MySQL Tuning | `references/mysql-tuning.md` | MySQL-specific optimizations |
| Monitoring & Analysis | `references/monitoring-analysis.md` | Performance metrics, diagnostics |

## Common Operations & Examples

### Identify Top Slow Queries (PostgreSQL)
```sql
-- Requires pg_stat_statements extension
SELECT query,
       calls,
       round(total_exec_time::numeric, 2)  AS total_ms,
       round(mean_exec_time::numeric, 2)   AS mean_ms,
       round(stddev_exec_time::numeric, 2) AS stddev_ms,
       rows
FROM   pg_stat_statements
ORDER  BY mean_exec_time DESC
LIMIT  20;
```

### Capture an Execution Plan
```sql
-- Use BUFFERS to expose cache hit vs. disk read ratio
EXPLAIN (ANALYZE, BUFFERS, FORMAT TEXT)
SELECT o.id, c.name
FROM   orders o
JOIN   customers c ON c.id = o.customer_id
WHERE  o.status = 'pending'
  AND  o.created_at > now() - interval '7 days';
```

### Reading EXPLAIN Output — Key Patterns to Find

| Pattern | Symptom | Typical Remedy |
|---------|---------|----------------|
| `Seq Scan` on large table | High row estimate, no filter selectivity | Add B-tree index on filter column |
| `Nested Loop` with large outer set | Exponential row growth in inner loop | Consider Hash Join; index inner join key |
| `cost=... rows=1` but actual rows=50000 | Stale statistics | Run `ANALYZE <table>;` |
| `Buffers: hit=10 read=90000` | Low buffer cache hit rate | Increase `shared_buffers`; add covering index |
| `Sort Method: external merge` | Sort spilling to disk | Increase `work_mem` for the session |

### Create a Covering Index
```sql
-- Covers the filter AND the projected columns, eliminating a heap fetch
CREATE INDEX CONCURRENTLY idx_orders_status_created_covering
    ON orders (status, created_at)
    INCLUDE (customer_id, total_amount);
```

### Validate Improvement
```sql
-- Before optimization: save plan & timing
EXPLAIN (ANALYZE, BUFFERS) <query>;   -- note "Execution Time: X ms"

-- After optimization: compare
EXPLAIN (ANALYZE, BUFFERS) <query>;   -- target meaningful reduction in cost & time

-- Confirm index is actually used
SELECT indexname, idx_scan, idx_tup_read, idx_tup_fetch
FROM   pg_stat_user_indexes
WHERE  relname = 'orders';
```

### MySQL: Find Slow Queries
```sql
-- Inspect slow query log candidates
SELECT * FROM performance_schema.events_statements_summary_by_digest
ORDER  BY SUM_TIMER_WAIT DESC
LIMIT  20;

-- Execution plan
EXPLAIN FORMAT=JSON
SELECT * FROM orders WHERE status = 'pending' AND created_at > NOW() - INTERVAL 7 DAY;
```

## Constraints

### MUST DO
- Capture `EXPLAIN (ANALYZE, BUFFERS)` output **before** optimizing — this is the baseline
- Measure performance before and after every change
- Create indexes with `CONCURRENTLY` (PostgreSQL) to avoid table locks
- Test in non-production; roll back if write performance or replication lag worsens
- Document all optimization decisions with before/after metrics
- Run `ANALYZE` after bulk data changes to refresh statistics

### MUST NOT DO
- Apply optimizations without a measured baseline
- Create redundant or unused indexes

From Vishwajeetsrk/JARVIS-AI-OS, MIT.

Signals

Stars
11
Forks
3
Open issues
0
Last commit (this folder)
Aug 20, 2026 (2 months ago)
Last release
Aug 27, 2026 (2 months ago)

From GitHub (fetched Oct 24, 2026).

Details

Version
1.1.1
License
MIT
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

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Cite this page

database-optimizer skill. Agent Mart. https://agentmart.dev/skills/vishwajeetsrk-database-optimizer (last updated 2026-10-24)