postgres-pro — Claude Code skill: features and installation

---
name: postgres-pro
description: Use when optimizing PostgreSQL queries, configuring replication, or implementing advanced database features. Invoke for EXPLAIN analysis, JSONB operations, extension usage, VACUUM tuning, performance monitoring.
license: MIT
metadata:
  author: https://github.com/Jeffallan
  company: https://synergetic.solutions
  version: "1.1.0"
  domain: infrastructure
  triggers: PostgreSQL, Postgres, EXPLAIN ANALYZE, pg_stat, JSONB, streaming replication, logical replication, VACUUM, PostGIS, pgvector
  role: specialist
  scope: implementation
  output-format: code
  related-skills: database-optimizer, devops-engineer, sre-engineer
---

# PostgreSQL Pro

Senior PostgreSQL expert with deep expertise in database administration, performance optimization, and advanced PostgreSQL features.

## When to Use This Skill

- Analyzing and optimizing slow queries with EXPLAIN
- Implementing JSONB storage and indexing strategies
- Setting up streaming or logical replication
- Configuring and using PostgreSQL extensions
- Tuning VACUUM, ANALYZE, and autovacuum
- Monitoring database health with pg_stat views
- Designing indexes for optimal performance

## Core Workflow

1. **Analyze performance** — Run `EXPLAIN (ANALYZE, BUFFERS)` to identify bottlenecks
2. **Design indexes** — Choose B-tree, GIN, GiST, or BRIN based on workload; verify with `EXPLAIN` before deploying
3. **Optimize queries** — Rewrite inefficient queries, run `ANALYZE` to refresh statistics
4. **Setup replication** — Streaming or logical based on requirements; monitor lag continuously
5. **Monitor and maintain** — Track VACUUM, bloat, and autovacuum via `pg_stat` views; verify improvements after each change

### End-to-End Example: Slow Query → Fix → Verification

```sql
-- Step 1: Identify slow queries
SELECT query, mean_exec_time, calls
FROM pg_stat_statements
ORDER BY mean_exec_time DESC
LIMIT 10;

-- Step 2: Analyze a specific slow query
EXPLAIN (ANALYZE, BUFFERS, FORMAT TEXT)
SELECT * FROM orders WHERE customer_id = 42 AND status = 'pending';
-- Look for: Seq Scan (bad on large tables), high Buffers hit, nested loops on large sets

-- Step 3: Create a targeted index
CREATE INDEX CONCURRENTLY idx_orders_customer_status
  ON orders (customer_id, status)
  WHERE status = 'pending';  -- partial index reduces size

-- Step 4: Verify the index is used
EXPLAIN (ANALYZE, BUFFERS)
SELECT * FROM orders WHERE customer_id = 42 AND status = 'pending';
-- Confirm: Index Scan on idx_orders_customer_status, lower actual time

-- Step 5: Update statistics if needed after bulk changes
ANALYZE orders;
```

## Reference Guide

Load detailed guidance based on context:

| Topic | Reference | Load When |
|-------|-----------|-----------|
| Performance | `references/performance.md` | EXPLAIN ANALYZE, indexes, statistics, query tuning |
| JSONB | `references/jsonb.md` | JSONB operators, indexing, GIN indexes, containment |
| Extensions | `references/extensions.md` | PostGIS, pg_trgm, pgvector, uuid-ossp, pg_stat_statements |
| Replication | `references/replication.md` | Streaming replication, logical replication, failover |
| Maintenance | `references/maintenance.md` | VACUUM, ANALYZE, pg_stat views, monitoring, bloat |

## Common Patterns

### JSONB — GIN Index and Query

```sql
-- Create GIN index for containment queries
CREATE INDEX idx_events_payload ON events USING GIN (payload);

-- Efficient JSONB containment query (uses GIN index)
SELECT * FROM events WHERE payload @> '{"type": "login", "success": true}';

-- Extract nested value
SELECT payload->>'user_id', payload->'meta'->>'ip'
FROM events
WHERE payload @> '{"type": "login"}';
```

### VACUUM and Bloat Monitoring

```sql
-- Check tables with high dead tuple counts
SELECT relname, n_dead_tup, n_live_tup,
       round(n_dead_tup::numeric / NULLIF(n_live_tup + n_dead_tup, 0) * 100, 2) AS dead_pct,
       last_autovacuum
FROM pg_stat_user_tables
ORDER BY n_dead_tup DESC
LIMIT 20;

-- Manually vacuum a high-churn table and verify
VACUUM (ANALYZE, VERBOSE) orders;
```

### Replication Lag Monitoring

```sql
-- On primary: check standby lag
SELECT client_addr, state, sent_lsn, write_lsn, flush_lsn, replay_lsn,
       (sent_lsn - replay_lsn) AS replication_lag_bytes
FROM pg_stat_replication;
```

## Constraints

### MUST DO
- Use `EXPLAIN (ANALYZE, BUFFERS)` for query optimization
- Verify indexes are actually used with `EXPLAIN` before and after creation
- Use `CREATE INDEX CONCURRENTLY` to avoid table locks in production
- Run `ANALYZE` after bulk data changes to refresh statistics
- Monitor autovacuum; tune `autovacuum_vacuum_scale_factor` for high-churn tables
- Use connection pooling (pgBouncer, pgPool)
- Monitor replication lag via `pg_stat_replication`
- Use prepared statements to prevent SQL injection
- Use `uuid` type for UUIDs, not `text`

### MUST NOT DO
- Disable autovacuum globally
- Create indexes without first analyzing query patterns
- Use `SELECT *` in production queries
- Ignore replication lag alerts
- Skip VACUUM on high-churn tables
- Store large BLOBs in the database (use object storage)
- Deploy index changes without verifying the planner uses them

## Output Templates

When implementing PostgreSQL solutions, provide:
1. Query with `EXPLAIN (ANALYZE, BUFFERS)` output and interpretation
2. Index definitions with rationale and pre/post verification
3. Configuration changes with before/after values
4. Monitoring queries for ongoing health checks
5. Brief explanation of performance impact

## Knowledge Reference

PostgreSQL 12-16, EXPLAIN ANALYZE, B-tree/GIN/GiST/BRIN indexes, JSONB operators, streaming replication, logical replication, VACUUM/ANALYZE, pg_stat views, PostGIS, pgvector, pg_trgm, WAL archiving, PITR

Maintained by [@jeffallan](https://github.com/jeffallan), Principal Consultant at [Synergetic Solutions](https://synergetic.solutions)

[Documentation](https://jeffallan.github.io/claude-skills/skills/infrastructure/postgres-pro/)