data-sql-optimization

Production-grade SQL optimization with AI-assisted query analysis, EXPLAIN ANALYZE automation, balanced indexing strategies, performance tuning, schema design, and operations across PostgreSQL, MySQL, SQL Server, Oracle, SQLite.

$ Instalar

git clone https://github.com/vasilyu1983/AI-Agents-public /tmp/AI-Agents-public && cp -r /tmp/AI-Agents-public/frameworks/claude-code-kit/framework/skills/data-sql-optimization ~/.claude/skills/AI-Agents-public

// tip: Run this command in your terminal to install the skill

SKILL.md

View on GitHub →

name: data-sql-optimization description: Production-grade SQL optimization with AI-assisted query analysis, EXPLAIN ANALYZE automation, balanced indexing strategies, performance tuning, schema design, and operations across PostgreSQL, MySQL, SQL Server, Oracle, SQLite.

SQL Optimization — Comprehensive Reference

This skill equips Claude with actionable checklists, patterns, and templates for transactional (OLTP) SQL optimization: AI-powered query analysis, automated EXPLAIN interpretation, intelligent indexing (avoiding over-indexing), performance monitoring with pg_stat_statements, schema evolution, migrations, backup/recovery, high availability, and security.

Supported Platforms: PostgreSQL, MySQL, SQL Server, Oracle, SQLite

For OLAP/Analytics: See data-lake-platform (ClickHouse, DuckDB, Doris, StarRocks)

Quick Reference

Task	Tool/Framework	Command	When to Use
Query Performance Analysis	EXPLAIN ANALYZE	`EXPLAIN (ANALYZE, BUFFERS) SELECT ...` (PG) / `EXPLAIN ANALYZE SELECT ...` (MySQL)	Diagnose slow queries, identify missing indexes
Find Slow Queries	pg_stat_statements / slow query log	`SELECT * FROM pg_stat_statements ORDER BY total_exec_time DESC LIMIT 10;`	Identify performance bottlenecks in production
Index Analysis	pg_stat_user_indexes / SHOW INDEX	`SELECT * FROM pg_stat_user_indexes WHERE idx_scan = 0;`	Find unused indexes, validate index coverage
Schema Migration	Flyway / Liquibase	`flyway migrate` / `liquibase update`	Version-controlled database changes
Backup & Recovery	pg_dump / mysqldump	`pg_dump -Fc dbname > backup.dump`	Point-in-time recovery, disaster recovery
Replication Setup	Streaming / GTID	Configure postgresql.conf / my.cnf	High availability, read scaling
Query Optimization	AI-assisted tools	pgai (PostgreSQL) / Performance Schema (MySQL)	ML-based query plan analysis, predictive caching

Decision Tree: Choosing the Right Approach

Query performance issue?
    ├─ Identify slow queries first?
    │   ├─ PostgreSQL → pg_stat_statements (top queries by total_exec_time)
    │   └─ MySQL → Performance Schema / slow query log
    │
    ├─ Analyze execution plan?
    │   ├─ PostgreSQL → EXPLAIN (ANALYZE, BUFFERS, VERBOSE)
    │   ├─ MySQL → EXPLAIN FORMAT=JSON or EXPLAIN ANALYZE
    │   └─ SQL Server → SET STATISTICS IO ON; SET STATISTICS TIME ON;
    │
    ├─ Need indexing strategy?
    │   ├─ PostgreSQL → B-tree (default), GIN (JSONB), GiST (spatial), partial indexes
    │   ├─ MySQL → BTREE (default), FULLTEXT (text search), SPATIAL
    │   └─ Check: Table >10k rows AND selectivity <10% AND 10x+ speedup verified
    │
    ├─ Schema changes needed?
    │   ├─ New database → template-schema-design.md
    │   ├─ Modify schema → template-migration.md (Flyway/Liquibase)
    │   └─ Large tables (MySQL) → gh-ost / pt-online-schema-change (avoid locks)
    │
    ├─ High availability setup?
    │   ├─ PostgreSQL → Streaming replication (template-replication-ha.md)
    │   └─ MySQL → GTID-based replication (template-replication-ha.md)
    │
    ├─ Backup/disaster recovery?
    │   └─ template-backup-restore.md (pg_dump, mysqldump, PITR)
    │
    └─ Analytics on large datasets (OLAP)?
        └─ See data-lake-platform (ClickHouse, DuckDB, Doris, StarRocks)

When to Use This Skill

Claude should invoke this skill when users ask for:

Query Optimization (Modern Approaches)

AI-assisted SQL query performance review and tuning
Automated EXPLAIN ANALYZE plan interpretation with optimization suggestions
Index creation strategies with balanced approach (avoiding over-indexing)
Troubleshooting slow queries using pg_stat_statements or Performance Schema
Identifying and remediating SQL anti-patterns with operational fixes
Query rewrite suggestions or migration from slow to fast patterns
Statistics maintenance and auto-analyze configuration

Database Operations

Schema design with normalization and performance trade-offs
Database migrations with version control (Liquibase, Flyway)
Backup and recovery strategies (point-in-time recovery, automated testing)
High availability and replication setup (streaming, GTID-based)
Database security auditing (access controls, encryption, SQL injection prevention)
Lock analysis and deadlock troubleshooting
Connection pooling (pgBouncer, Pgpool-II, ProxySQL)

Performance Tuning (Modern Standards)

Memory configuration (work_mem, shared_buffers, effective_cache_size)
Automated monitoring with pg_stat_statements and query pattern analysis
Index health monitoring (unused index detection, index bloat analysis)
Vacuum strategy and autovacuum tuning (PostgreSQL)
InnoDB buffer pool optimization (MySQL)
Partition pruning improvements (PostgreSQL 18+)

Resources (Best Practices Guides)

Find detailed operational patterns and quick references in:

SQL Best Practices: resources/sql-best-practices.md
Query Tuning Patterns: resources/query-tuning-patterns.md
Indexing Strategies: resources/index-patterns.md
EXPLAIN/Analysis: resources/explain-analysis.md
SQL Anti-Patterns: resources/sql-antipatterns.md
External Sources: data/sources.json — vendor docs and reference links
Operational Standards: resources/operational-patterns.md — Deep operational checklists, database-specific guidance, and template selection trees

Each file includes:

Copy-paste ready checklists (e.g., "query review", "index design", "explain review")
Anti-patterns with operational fixes and alternatives
Query rewrite and indexing strategies with examples
Troubleshooting guides (step-by-step)

Templates (Copy-Paste Ready)

Templates are organized by database technology for precision and clarity:

Related Skills

Infrastructure & Operations:

../ops-devops-platform/SKILL.md — Infrastructure, backups, monitoring, and incident response
../qa-observability/SKILL.md — Performance monitoring, profiling, and metrics
../qa-debugging/SKILL.md — Production debugging patterns

Application Integration:

../software-backend/SKILL.md — API/database integration and application patterns
../software-architecture-design/SKILL.md — System design and data architecture
../dev-api-design/SKILL.md — REST API and database interaction patterns

Quality & Security:

../qa-resilience/SKILL.md — Resilience, circuit breakers, and failure handling
../software-security-appsec/SKILL.md — Database security, auth, SQL injection prevention
../qa-testing-strategy/SKILL.md — Database testing strategies

Data Engineering & AI:

../ai-ml-data-science/SKILL.md — SQLMesh, dbt, data transformations
../ai-mlops/SKILL.md — Data pipelines, ETL, and warehouse loading (dlt)
../ai-ml-timeseries/SKILL.md — Time-series databases and forecasting

Navigation

Resources

Templates

Data

data/sources.json — Curated external references

Operational Deep Dives

See resources/operational-patterns.md for:

End-to-end optimization checklists and anti-pattern fixes
Database-specific quick references (PostgreSQL, MySQL, SQL Server, Oracle, SQLite)
Slow query troubleshooting workflow and reliability drills
Template selection decision tree and platform migration notes

Analytical Databases (OLAP)

For OLAP databases and data lake infrastructure, see data-lake-platform:

Query engines: ClickHouse, DuckDB, Apache Doris, StarRocks
Table formats: Apache Iceberg, Delta Lake, Apache Hudi
Transformation: SQLMesh, dbt (staging/marts layers)
Ingestion: dlt, Airbyte (connectors)
Streaming: Apache Kafka patterns

This skill focuses on transactional database optimization (PostgreSQL, MySQL, SQL Server, Oracle, SQLite). Use data-lake-platform for analytical workloads.

Related Skills

This skill focuses on query optimization within a single database. For related workflows:

SQL Transformation & Analytics Engineering: → ai-ml-data-science skill

SQLMesh templates for building staging/intermediate/marts layers
Incremental models (FULL, INCREMENTAL_BY_TIME_RANGE, INCREMENTAL_BY_UNIQUE_KEY)
DAG management and model dependencies
Unit tests and audits for SQL transformations

Data Ingestion (Loading into Warehouses): → ai-mlops skill

dlt templates for extracting from REST APIs, databases
Loading to Snowflake, BigQuery, Redshift, Postgres, DuckDB
Incremental loading patterns (timestamp, ID-based, merge/upsert)
Database replication (Postgres, MySQL, MongoDB → warehouse)

Data Lake Infrastructure: → data-lake-platform skill

ClickHouse, DuckDB, Doris, StarRocks query engines
Iceberg, Delta Lake, Hudi table formats
Kafka streaming, Dagster/Airflow orchestration

Use Case Decision:

Query is slow in production → Use this skill (data-sql-optimization)
Building feature pipelines in SQL → Use ai-ml-data-science (SQLMesh)
Loading data from APIs/DBs to warehouse → Use ai-mlops (dlt)
Analytics on large datasets (OLAP) → Use data-lake-platform

External Resources

See data/sources.json for 62+ curated resources including:

Core Documentation:

RDBMS Documentation: PostgreSQL, MySQL, SQL Server, Oracle, SQLite, DuckDB official docs
Query Optimization: Use The Index, Luke, SQL Performance Explained, vendor optimization guides
Schema Design: Database Refactoring (Fowler), normalization guides, data type selection

Modern Optimization (December 2025):

PostgreSQL 18: 3x I/O performance improvements, uuidv7(), virtual generated columns, protocol 3.2, faster major upgrades
MySQL 8.4/9.0: Thread pool management, parallel processing, AI integration, workload analysis
DuckDB: Columnar optimization, zone maps, vectorized execution, query optimizer insights (100x improvements)

Operations & Infrastructure:

HA & Replication: Streaming replication, GTID-based replication, failover automation
Migrations: Liquibase, Flyway version control and deployment patterns
Backup/Recovery: pgBackRest, Percona XtraBackup, point-in-time recovery
Monitoring: pg_stat_statements, Performance Schema, EXPLAIN visualizers (Dalibo, depesz)
Security: OWASP SQL Injection Prevention, Postgres hardening, encryption standards
Analytical Databases: DuckDB extensions, Parquet specification, columnar storage patterns

Use resources/operational-patterns.md and the templates directory for detailed workflows, migration notes, and ready-to-run commands.

data-sql-optimization

$ Instalar

name: data-sql-optimization description: Production-grade SQL optimization with AI-assisted query analysis, EXPLAIN ANALYZE automation, balanced indexing strategies, performance tuning, schema design, and operations across PostgreSQL, MySQL, SQL Server, Oracle, SQLite.

SQL Optimization — Comprehensive Reference

Quick Reference

Decision Tree: Choosing the Right Approach

When to Use This Skill

Query Optimization (Modern Approaches)

Database Operations

Performance Tuning (Modern Standards)

Resources (Best Practices Guides)

Templates (Copy-Paste Ready)

Cross-Platform Templates (All Databases)

PostgreSQL Templates

MySQL Templates

Microsoft SQL Server Templates

Oracle Templates

SQLite Templates

Related Skills

Navigation

Operational Deep Dives

Analytical Databases (OLAP)

Related Skills

External Resources

Repository

Actions

Related Skills