analytics-data-analysis
Implement analytics, data analysis, and visualization best practices using Python, Jupyter, and modern data tools.
Découvrez les skills Vie quotidienne, comparez leurs dépôts et consultez leurs instructions.
Implement analytics, data analysis, and visualization best practices using Python, Jupyter, and modern data tools.
Azure cloud development guidelines for ARM templates, Azure Pipelines, Kubernetes, and cloud-native services.
Docker containerization best practices for building, securing, and deploying containers.
Expert guidance for Micronaut framework development with compile-time dependency injection, GraalVM native builds, and cloud-native microservices
Comprehensive DevOps engineering practices for Kubernetes, CI/CD, Bash scripting, Ansible, and cloud infrastructure.
Expert guidance for Microsoft AutoGen multi-agent framework development including agent creation, conversations, tool integration, and orchestration patterns.
Expert in web scraping and data extraction with Python tools
Expert guidance for LlamaIndex development including RAG applications, vector stores, document processing, query engines, and building production AI applications.
Expert guidance for browser automation and web testing using Selenium WebDriver with best practices for element location, waits, and test organization.
Expert guidance for building web scrapers and crawlers using the Scrapy Python framework with best practices for spider development, data extraction, and pipeline management.
Implement localization (l10n) best practices to adapt applications for specific regions, languages, and cultural preferences.
Expert guidance for browser automation using Puppeteer with best practices for web scraping, testing, screenshot capture, and JavaScript execution in headless Chrome.
Best practices and guidelines for building real-time applications with WebSocket communication
Ruby development guidelines covering idiomatic code style, Ruby 3.x features, testing with RSpec, and best practices for building maintainable Ruby applications.
Expert in Rust development with focus on safety, performance, and async programming
Logging best practices for applications and services including structured logging, log levels, and log management strategies
Expert in Go/Golang development with focus on APIs, microservices, and clean architecture
Best practices and guidelines for building high-performance services with gRPC and Protocol Buffers
Guidelines for building production-grade microservices with FastAPI/Python and Go, covering serverless patterns, clean architecture, observability, and resilience.
Guidelines for implementing Clean Architecture patterns in Flutter and Go applications, with emphasis on separation of concerns, dependency rules, and testability.
Kotlin development guidelines with best practices for clean code, naming conventions, function design, and data handling
Expert in Elixir and Phoenix development with functional programming patterns
Java Quarkus development guidelines for building cloud-native applications with fast startup, minimal memory footprint, and GraalVM native builds
Best practices and guidelines for Apache Kafka event streaming and distributed messaging
Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.