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Found 1,643 Skills
Development best practices and project patterns. Use when starting projects, setting up CLAUDE.md, coding TypeScript/Next.js/React/Supabase, implementing AI flows, data fetching, testing, deployment, git workflows, browser automation, centralized configuration, or Tailwind CSS v4.
Comprehensive Azure Well-Architected Framework knowledge covering the five pillars: Reliability, Security, Cost Optimization, Operational Excellence, and Performance Efficiency. Provides design principles, best practices, and implementation guidance for building robust Azure solutions.
Infrastructure as Code best practices for Terraform, Docker, Ansible, and CloudFormation. Covers secure-by-default configurations, multi-stage builds, state management, and modular patterns. Use when working with .tf, Dockerfile, docker-compose.yml, .yaml/.yml Ansible files, CloudFormation templates, or when asking about IaC, containers, or infrastructure automation.
Vitest testing framework patterns for test setup, async testing, mocking with vi.*, snapshots, and test performance (formerly test-vitest). This skill should be used when writing or debugging Vitest tests. This skill does NOT cover TDD methodology (use test-tdd skill), API mocking with MSW (use test-msw skill), or Jest-specific APIs.
Use this skill when you are not sure about a fact, have outdated knowledge, or the question is contested. Explicitly communicate the level of confidence instead of asserting uncertain things as fact.
Docker containerization best practices and patterns
Expert knowledge for Azure AI Custom Vision development including best practices, decision making, limits & quotas, security, integrations & coding patterns, and deployment. Use when exporting Custom Vision models, calling prediction APIs, using ONNX/TensorFlow, managing CMK/RBAC, or Smart Labeler, and other Azure AI Custom Vision related development tasks. Not for Azure AI Vision (use azure-ai-vision), Azure AI services (use microsoft-foundry-tools), Azure Machine Learning (use azure-machine-learning), Azure AI Foundry Local (use microsoft-foundry-local).
Expert knowledge for Azure AI Anomaly Detector development including troubleshooting, best practices, architecture & design patterns, limits & quotas, configuration, and deployment. Use when using univariate/multivariate APIs, Docker/IoT Edge containers, predictive maintenance flows, or regional limits, and other Azure AI Anomaly Detector related development tasks. Not for Azure AI Metrics Advisor (use azure-metrics-advisor), Azure Monitor (use azure-monitor), Azure Machine Learning (use azure-machine-learning).
Use when designing, planning, implementing, or reviewing any non-trivial change — enforces defense in depth, input validation, secure defaults, and OWASP best practices to prevent vulnerabilities before they ship
ALWAYS use when working with Angular Dependency Injection, providers, services, inject tokens, or hierarchical injection in Angular applications.
ALWAYS use when working with Angular Components, component architecture, @Component decorator, inputs, outputs, or component design patterns.
ALWAYS use when testing Angular applications with Cypress, E2E testing, or component testing in Angular.