gendb-code-patterns

Generate C++ file structure templates for GenDB query execution.

71|8|Updated Feb 8, 2026

Search Tags:#templates #performance #query optimization #code generation #c++#gendb

Ready to run

npx skills add https://github.com/SolidLao/GenDB --skill gendb-code-patterns

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: gendb-code-patterns
Source: https://github.com/SolidLao/GenDB/tree/main/.claude/skills/gendb-code-patterns
Command: npx skills add https://github.com/SolidLao/GenDB --skill gendb-code-patterns

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides standardized C++ code patterns and file structure templates for generating and optimizing query execution code within the GenDB framework.

Core Features & Use Cases

Code Structure: Offers a template for organizing C++ query files, including necessary includes and a main function.
Performance Utilities: Integrates essential utilities for timing (GENDB_PHASE) and date manipulation (date_utils.h).
Memory Mapping: Demonstrates the recommended pattern for memory-mapping data files for efficient access.
Use Case: When developing a new query for GenDB, use these patterns to ensure consistency, leverage built-in utilities, and adhere to best practices for performance and maintainability.

Quick Start

Use the gendb-code-patterns skill to generate a C++ file structure template for a new GenDB query.

Frequently Asked Questions about gendb-code-patterns

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

FAQPage Schema

How do I structure C++ files for GenDB query generation?▼

To structure C++ files for GenDB query generation, use a template that organizes necessary includes and a main function. This ensures consistency and adherence to best practices for maintainable query execution pipelines.

What is the best way to memory-map data files for GenDB queries?▼

The best way to memory-map data files for GenDB queries is to apply the recommended C++ pattern provided by the code templates. This approach enables efficient data access and high-performance execution during query processing.

How do I measure query execution time in C++ data processing pipelines?▼

To measure query execution time in C++ data processing pipelines, integrate timing utilities like the GENDB_PHASE macro. This provides precise phase tracking during GenDB query execution for performance optimization.

Does GenDB support built-in utilities for date manipulation in C++?▼

GenDB supports built-in utilities for date manipulation in C++ through the date_utils.h header. Integrating this file into your query code template allows standardized date handling alongside memory-mapped data access.

Can I use these C++ code patterns to optimize compilation flags for data processing?▼

You can use these C++ code patterns to optimize compilation flags for data processing. The templates encompass specific compilation flags designed to ensure efficient query execution within the GenDB framework.