optimizing-database-connection-pooling
About
This skill helps developers implement and optimize database connection pools for better performance and resource management. It provides guidance on configuring pool settings, troubleshooting issues, and managing connection lifecycles across various programming languages. Use it when working with connection pooling, database performance tuning, or optimizing database interactions.
Quick Install
Claude Code
Recommended/plugin add https://github.com/jeremylongshore/claude-code-plugins-plusgit clone https://github.com/jeremylongshore/claude-code-plugins-plus.git ~/.claude/skills/optimizing-database-connection-poolingCopy and paste this command in Claude Code to install this skill
Documentation
Overview
This skill enables Claude to generate and configure database connection pools, ensuring optimal performance and resource utilization. It provides guidance on selecting appropriate pool settings, managing connection lifecycles, and monitoring pool performance.
How It Works
- Identify Requirements: Analyzes the user's request to determine the target database, programming language, and performance goals.
- Generate Configuration: Creates a connection pool configuration tailored to the specified environment, including settings for minimum and maximum pool size, connection timeout, and other relevant parameters.
- Implement Monitoring: Sets up monitoring for key pool metrics, such as connection usage, wait times, and error rates.
When to Use This Skill
This skill activates when you need to:
- Implement connection pooling for a database application.
- Optimize existing connection pool configurations.
- Troubleshoot connection-related performance issues.
Examples
Example 1: Implementing Connection Pooling in Python
User request: "Implement connection pooling in Python for a PostgreSQL database to improve performance."
The skill will:
- Generate a Python code snippet using a connection pool library like
psycopg2orSQLAlchemy. - Configure the connection pool with optimal settings for the PostgreSQL database, such as maximum pool size and connection timeout.
Example 2: Optimizing Connection Pool Configuration in Java
User request: "Optimize the connection pool configuration in my Java application using HikariCP to reduce connection wait times."
The skill will:
- Analyze the existing HikariCP configuration.
- Suggest adjustments to parameters like minimum idle connections, maximum pool size, and connection timeout to minimize wait times.
Best Practices
- Connection Timeout: Set a reasonable connection timeout to prevent indefinite waiting.
- Pool Size: Adjust the pool size based on the application's workload and database server capacity.
- Connection Testing: Implement connection validation to ensure connections are still valid before use.
Integration
This skill can integrate with other Claude Code plugins for database management, code generation, and performance monitoring to provide a comprehensive solution for database optimization.
GitHub Repository
Related Skills
csv-data-summarizer
MetaThis skill automatically analyzes CSV files to generate comprehensive statistical summaries and visualizations using Python's pandas and matplotlib/seaborn. It should be triggered whenever a user uploads or references CSV data without prompting for analysis preferences. The tool provides immediate insights into data structure, quality, and patterns through automated analysis and visualization.
content-collections
MetaThis skill provides a production-tested setup for Content Collections, a TypeScript-first tool that transforms Markdown/MDX files into type-safe data collections with Zod validation. Use it when building blogs, documentation sites, or content-heavy Vite + React applications to ensure type safety and automatic content validation. It covers everything from Vite plugin configuration and MDX compilation to deployment optimization and schema validation.
llamaindex
MetaLlamaIndex is a data framework for building RAG-powered LLM applications, specializing in document ingestion, indexing, and querying. It provides key features like vector indices, query engines, and agents, and supports over 300 data connectors. Use it for document Q&A, chatbots, and knowledge retrieval when building data-centric applications.
hybrid-cloud-networking
MetaThis skill configures secure hybrid cloud networking between on-premises infrastructure and cloud platforms like AWS, Azure, and GCP. Use it when connecting data centers to the cloud, building hybrid architectures, or implementing secure cross-premises connectivity. It supports key capabilities such as VPNs and dedicated connections like AWS Direct Connect for high-performance, reliable setups.
