devops
Deploy applications with Docker and Kubernetes, automate with CI/CD, manage infrastructure with code, and configure cloud platforms and networking.
Discover Development skills, compare source repositories and read the instructions for each workflow.
Deploy applications with Docker and Kubernetes, automate with CI/CD, manage infrastructure with code, and configure cloud platforms and networking.
Optimize application performance through caching strategies, load balancing, database scaling, and monitoring. Build systems handling thousands of concurrent users.
Logging, metrics, and distributed tracing. OpenTelemetry, Prometheus, Grafana, and production debugging.
Secure backend applications against OWASP threats. Implement authentication, encryption, scanning, compliance, and incident response procedures.
Backend testing strategies and test automation. Unit, integration, E2E, and load testing with best practices.
Master ASP.NET Core fundamentals including C#, project structure, routing, middleware, and basic API development. Essential skills for all ASP.NET Core developers.
Master ASP.NET Core deployment, Docker, Azure cloud, CI/CD pipelines, and production infrastructure for enterprise applications.
Master advanced ASP.NET Core development including Entity Framework Core, authentication, testing, and enterprise patterns for production applications.
Retrofit, OkHttp, REST APIs, JSON serialization, network security.
Unit testing, performance optimization, security implementation, Play Store deployment.
MVVM pattern, Clean Architecture, Repository pattern, dependency injection, SOLID principles.
Room ORM, SQLite, SharedPreferences, DataStore, encryption.
Android core components lifecycle, Activities, Fragments, Services, Intent system.
Master Kotlin syntax, OOP principles, SOLID practices, functional programming, and data structures.
XML layouts, ConstraintLayout, Jetpack Compose, Material Design 3.
Systematic vulnerability finding, threat modeling, and attack surface analysis for AI/LLM security assessments
Implement mitigations, create input filters, design output guards, and build defensive prompting for LLM security
Ethical vulnerability reporting, coordinated disclosure, and bug bounty participation for AI systems
Tools and frameworks for AI red teaming including PyRIT, garak, Counterfit, and custom attack automation
Generate adversarial inputs, edge cases, and boundary test payloads for stress-testing LLM robustness
Techniques to test and bypass AI safety filters, content moderation systems, and guardrails for security assessment
Attack techniques for Retrieval-Augmented Generation systems including knowledge base poisoning
Securing AI/ML infrastructure including model storage, API endpoints, and compute resources
Test AI training pipelines for data poisoning vulnerabilities and backdoor injection
The default order uses GitHub repository stars from our indexed data. Stars belong to the source repository and may be shared by multiple skills. They indicate repository interest, not skill installs or an independent quality score.