skills.rest
mtsatryan
mtsatryanCOMMUNITY
Explore all skills by this author →
GitHub

database-optimizer

Analyze execution plans and deliver optimization plans for PostgreSQL and MySQL databases.

1|Updated Apr 23, 2026
Search Tags:#performance#analytics#indexing#database#scalability#tuning#execution-plan
One-click install
npx skills add https://github.com/mtsatryan/openclaw-ai-agents --skill database-optimizer-mtsatryan
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: database-optimizer
Source: https://github.com/mtsatryan/openclaw-ai-agents/tree/main/database-optimizer
Command: npx skills add https://github.com/mtsatryan/openclaw-ai-agents --skill database-optimizer-mtsatryan

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps database teams overcome performance bottlenecks by providing targeted optimization guidance, enabling sub-second query responses.

Core Features & Use Cases

  • Execution Plan Analysis: Analyze execution plans to identify bottlenecks across PostgreSQL, MySQL, and other systems.
  • Index & Schema Tuning: Recommend index strategies and schema adjustments to improve latency.
  • Resource & Concurrency Optimization: Optimize memory, caching, locking, and replication settings to reduce contention.
  • Use Case: Large OLTP workloads with mixed read/write patterns and frequent slow queries.

Quick Start

Provide a detailed performance optimization plan for the given database workload.

Frequently Asked Questions about database-optimizer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I optimize slow database queries in PostgreSQL and MySQL?▼

To optimize slow database queries, you analyze execution plans to identify bottlenecks and apply index strategies, schema tuning, and caching adjustments to achieve sub-second response times across PostgreSQL, MySQL, and distributed systems.

Why does high latency occur in large OLTP workloads and how can I fix it?▼

High latency in large OLTP workloads occurs due to resource contention and inefficient execution plans; you fix it by optimizing memory, locking mechanisms, and replication settings to reduce mixed read/write bottlenecks.

What is the best way to analyze an execution plan for database performance tuning?▼

Analyzing an execution plan for database performance tuning involves identifying bottleneck operations within the query path, then recommending targeted schema adjustments and index strategies to improve overall latency.

Can I use this approach for distributed systems with resource contention issues?▼

Yes, you can apply this optimization approach to distributed systems experiencing resource contention by evaluating replication considerations, locking behavior, and caching settings to resolve performance bottlenecks.

When should I apply index and schema tuning to resolve slow query responses?▼

You should apply index and schema tuning when execution plan analysis reveals inefficient data retrieval paths, allowing you to restructure indexing strategies and achieve sub-second query responses in demanding workloads.

Related Skills

Explore More Software Engineering Skills→
LaurentPRAT-DBLaurentPRAT-DB

performance-tuning

Analyze Databricks metrics and logs to tune Spark configuration and caching.

Community
Intermediate
Tnemo65Tnemo65

database-optimizer

Analyze slow queries and execution plans to optimize database performance.

Community
Advanced
rohitg00rohitg00
2.5k

postgres-optimization

Guide PostgreSQL index choices, partitioning, and query tuning for large datasets.

Community
Advanced
xiaoquqixiaoquqi

pytorch-fsdp

Apply PyTorch FSDP memory sharding across data-parallel workers for distributed training.

Community
Advanced