Browse

LSM tree .cursorrules prompt files

An LSM tree is a data structure that optimizes write performance on disk-based databases by batching writes and merging data in the background.

Filters

All

React

Tailwind CSS

Filters

Tech

Clear

React

Tailwind CSS

Node.js

Tailwind

JavaScript

Shadcn UI

Next.js

Radix UI

Python

WebP

Zod

JSX

Vite

CSS

Supabase

HTML

Jest

Next.js App Router

PostgreSQL

Docker

Redis

Pydantic

Tailwind Aria

TailwindCSS

FastAPI

Git

ESLint

pytest

react-hook-form

Solidity

Vercel

JSDoc

GitHub Actions

Typescript

Prettier

Suspense

Vitest

ARIA

Zustand

React Testing Library

Celery

Radix

Shadcn

SQLAlchemy

PHP

SvelteKit

JSON

Vue.js

Pinia

MongoDB

tRPC

Clerk

Expo

PEP 8

SSR

Prisma

dotenv

GitLab CI

SQLite

SQL

Playwright

tanStackQuery

DaisyUI

Laravel

RESTful API

Drizzle ORM

VueUse

Express

asyncpg

TypeScript

scikit-learn

pandas

GitHub

RabbitMQ

Rust

Java

GraphQL

Svelte

Elixir

ES6

Nuxt 3

styled-components

Vue Router

Astro

Svelte 5

Markdown

OpenZeppelin

Drizzle

Stripe

Flexbox

CORS

Lighthouse

JWT

Swagger

aiomysql

Ruff

Langchain

Django

matplotlib

Clear

Database Design

Deployment and Scaling

Personal Website

Minimalist Design

3D Graphics

Entrepreneurship

Design System

Laravel development

TALL stack

Alpine.js integration

Laravel 11 changes

Livewire components

Contract Development

Decentralized Applications (DApps)

Coding

Nuxt-specific Guidelines

Formatting

UI

Composition API

Key Conventions

ES6

Bun

GraphQL

Unix Philosophy

Behavior-Driven Development

Data Engineering

JavaScript Code Style

Static Generation

Async/Await

React Functional Components

JavaScript ES6

component libraries

frontend development

database integration

code maintainability

Async Ecosystem

Rust Best Practices

Concurrency

TypeScript coding standards

Web development

Error handling and validation

React best practices

Programming principles

AI Modeling

Village Simulation

Data Structures and Algorithms

Programming Paradigms

Prompting Guidance

AI Behavior Guidelines

Styling Guidelines

Web Scraping

Data Extraction

Software Best Practices

Mobile Development

Component Libraries

Software development

Code best practices

Debugging and Troubleshooting

Material UI Usage

iterative refinement

full-stack development

authentication

system thinking

clean code practices

data fetching

testing and documentation

User-Friendly Tools

README Generator

Software Projects

data science

software testing

command line applications

JavaScript naming conventions

Programming best practices

Code Improvement

Data Analysis

Medical Imaging

Code Reproducibility

document authoring

asynchronous operations

serverless architecture

Adobe Edge Delivery Services

accessibility

lazy loading

responsive design

GitHub integration

spreadsheet integration

semantic HTML

block-based development

metadata handling

content management

Cross-Platform Development

Scripting

Software Documentation

Programming Languages

Cross-platform applications

Schema validation

Monorepo management

Mobile development

Thank you! Your submission has been received!

Oops! Something went wrong while submitting the form.

NEW

Python Containerization .cursorrules prompt file

Python and Database Algorithms Expert Guide

You are an expert in Python, database algorithms, and containerization technologies.

Code Style and Structure

Write clean, Pythonic code with type hints and docstrings.
Follow PEP 8 guidelines for code formatting.
Use functional programming patterns where appropriate; prefer composition over inheritance.
Implement modular design to separate concerns (e.g., storage engine, query processor, transaction manager).
Use descriptive variable names (e.g., is_transaction_active, has_index).
Structure your project with clear separation: src/, tests/, docs/, etc.

Naming Conventions

Use snake_case for function and variable names.
Use PascalCase for class names.
Use UPPER_CASE for constants.

Python Best Practices

Leverage Python's built-in data structures (lists, dicts, sets) effectively.
Use list comprehensions and generator expressions for concise, readable code.
Implement context managers (with statement) for resource management.
Utilize collections module for specialized data structures.

Database Algorithm Implementation

Implement B-tree or B+ tree for efficient indexing.
Use WAL (Write-Ahead Logging) for ACID compliance.
Implement MVCC (Multi-Version Concurrency Control) for transaction isolation.
Create a simple query parser and execution engine.
Develop a basic storage engine (e.g., append-only file, LSM tree).

Performance Optimization

Use cProfile for performance profiling.
Implement connection pooling for database connections.
Use appropriate data structures for different operations (e.g., hash tables for quick lookups).
Implement query optimization techniques (e.g., join order optimization).

Testing

Write unit tests for individual components (e.g., B-tree operations, WAL recovery).
Implement integration tests for database operations.
Use property-based testing for complex algorithms.
Employ fuzzing techniques to test edge cases and improve robustness.