Distinctive, production-grade frontend interfaces that reject generic AI aesthetics through intentional design choices. Guides aesthetic direction selection (brutalist, maximalist, retro-futuristic, luxury, organic, etc.) before implementation to ensure cohesive, memorable designs Emphasizes typography, color theming with CSS variables, motion design, spatial composition, and textural details as core design pillars Generates working code (HTML/CSS/JS, React, Vue) that matches implementation complexity to the aesthetic vision, from refined minimalism to elaborate maximalism Explicitly avoids overused fonts, clichéd color schemes, and predictable layouts that characterize generic AI-generated interfaces
React and Next.js performance optimization across 70 rules prioritized by impact. Organized into 8 categories from critical (eliminating waterfalls, bundle optimization) to low priority (advanced patterns), each with specific, actionable rules prefixed for easy reference Covers server-side performance including React.cache() deduplication, parallel fetching, and serialization minimization Addresses client-side concerns: re-render optimization through memoization and dependency management, rendering performance with CSS strategies and hydration patterns Includes JavaScript-level optimizations like DOM batching, caching, and Set/Map lookups for O(1) performance Each rule includes detailed explanations, incorrect and correct code examples, and contextual guidance for automated refactoring and code generation
Systematic diagnosis and remediation for Azure production issues using AppLens, Monitor, and resource health. Covers Container Apps, Function Apps, and AKS troubleshooting with service-specific guides for image pulls, cold starts, health probes, invocation failures, and node/pod issues Integrates AppLens (MCP) for AI-powered root cause analysis and Azure Monitor (MCP) for KQL-based log and metric queries Provides a five-step diagnostic flow: identify symptoms, check resource health, review logs, analyze metrics, and investigate recent changes Includes quick reference commands for activity logs, container logs, and App Insights queries, plus routing guidance for AKS-specific incidents
Unified access to Azure AI services: Search, Speech, OpenAI, and Document Intelligence. AI Search supports full-text, vector, hybrid, and semantic search with AI enrichment capabilities like entity extraction and OCR Speech service enables speech-to-text transcription (real-time and batch), text-to-speech with neural voices, speaker diarization, and custom models MCP server integration provides direct tool access via azure__search and azure__speech commands; falls back to CLI and SDK when MCP is unavailable Includes OpenAI model access, DALL-E image generation, embeddings, and Document Intelligence for form extraction and OCR
Pre-deployment validation for Azure readiness with configuration, infrastructure, RBAC, and identity checks. Runs recipe-specific validation commands including Bicep builds, Terraform validation, and Azure CLI preflight checks Verifies RBAC role assignments in infrastructure code and managed identity permissions before deployment Requires .azure/deployment-plan.md from azure-prepare skill; records validation proof and updates plan status to Validated only after all checks pass Integrates into the three-step workflow: azure-prepare → azure-validate → azure-deploy
Microsoft Entra ID app registration, OAuth 2.0 configuration, and MSAL integration for secure application authentication. Covers app registration setup, authentication configuration, API permissions, and client credential management across web apps, SPAs, mobile apps, and daemon services Includes step-by-step workflows for first-time registration, console application authentication, and service-to-service credential flows Provides Azure CLI commands, MSAL library examples for .NET, JavaScript, Python, and Java, plus security best practices for secret rotation and token validation Distinguishes scope clearly: handles identity and authentication setup but excludes Azure RBAC, Key Vault secrets management, and resource security
Azure compliance scanning, Key Vault expiration auditing, and resource configuration validation. Runs azqr (Azure Quick Review) for comprehensive compliance assessment against best practices across subscriptions and resource groups Monitors Key Vault keys, secrets, and certificates for expiration dates and identifies items without expiration policies Detects orphaned, misconfigured, and non-compliant resources using Resource Graph queries Classifies findings by priority (Critical, High, Medium, Low) with remediation guidance for each issue
Guidance and reference material for instrumenting webapps with Azure Application Insights. Covers SDK setup, telemetry patterns, and configuration for ASP.NET Core and Node.js applications hosted in Azure Distinguishes between this skill (reference and guidance) and azure-prepare (actual implementation); invoke azure-prepare when the user wants to add instrumentation to their project Provides auto-instrumentation guidance for C# ASP.NET Core apps in Azure App Service, plus manual instrumentation paths for creating App Insights resources via Bicep templates or Azure CLI Includes language-specific code modification guides for ASP.NET Core, Node.js, and Python, plus quick references for OpenTelemetry SDKs and exporters
Fast discovery and inventory of Azure resources across subscriptions using Resource Graph queries. Queries any Azure resource type (VMs, storage accounts, web apps, container apps, Key Vaults, etc.) across subscriptions and resource groups in a single command Supports cross-cutting searches for orphaned resources, missing tags, unhealthy states, and resource inventory counts Routes single-resource-type queries to dedicated MCP tools when available; falls back to Azure Resource Graph for broader or unsupported resource types Uses KQL (Kusto Query Language) for flexible filtering, with built-in error handling for authorization, syntax, and scope issues
Transform Azure resource groups into detailed architecture diagrams showing resource relationships and configurations. Discovers all resources within a resource group and analyzes their configurations, dependencies, and interconnections Generates Mermaid diagrams organized by logical layers (Network, Compute, Data, Security, Monitoring) with SKU details and connection labels Maps relationships including network connections, data flows, identity bindings, and configuration dependencies across resources Creates comprehensive markdown documentation with resource inventory tables, architecture diagrams, and relationship explanations
Diagnose and resolve Azure Event Hubs and Service Bus SDK issues with structured troubleshooting workflows. Covers connection failures, authentication errors, AMQP link issues, message lock timeouts, and event processor stalls across Python, Java, JavaScript, and .NET SDKs Includes language-specific troubleshooting guides for Event Hubs and Service Bus, plus connectivity diagnostics for ports, WebSocket fallback, IP firewalls, and private endpoints Provides MCP tools to query resource health, list namespaces/hubs/queues/topics, and search Microsoft Learn documentation for error resolution Structured diagnosis workflow: identify SDK version, check resource health, match error messages, look up docs, verify configuration, and apply fixes
Domain-specific knowledge base for building videos with Remotion and React. Covers 30+ rule files spanning animations, audio, assets, 3D content, charts, text, transitions, and composition management Includes specialized guidance for captions, FFmpeg operations, audio visualization, and sound effects integration Provides patterns for dynamic metadata calculation, media inspection (duration, dimensions, frame extraction), and parametrizable compositions Supports advanced features like Lottie animations, light leak effects, Mapbox integration, TailwindCSS styling, and AI-generated voiceovers
Assess and migrate cloud workloads from AWS, GCP, and other providers to Azure services. Supports Lambda-to-Azure Functions migration with dedicated scenario reference and best practices Generates assessment reports mapping source services to Azure equivalents before any code conversion Converts source code to target Azure runtime models, with output isolated in a separate <source-folder>-azure/ directory Requires sequential phase execution: assessment first, then migration, with user confirmation before destructive actions Hands off to azure-prepare skill for infrastructure provisioning, local testing, and deployment workflows
Assess and automate upgrades of Azure workloads across plans, tiers, and SKUs. Handles plan migrations (Consumption to Flex Consumption), tier upgrades, and cross-service moves (App Service to Container Apps) with sequential assessment before any changes Generates pre-upgrade readiness reports, collects existing app settings and configurations, then executes automated upgrade steps with idempotent scripts Requires explicit user confirmation for destructive actions and target plan/SKU selection before proceeding Validates upgrades by testing app reachability and monitoring, then hands off to azure-validate or azure-deploy for deeper validation or CI/CD setup
Quick reference for ADK Python patterns: agents, tools, callbacks, and state management. Covers agent creation with model and instruction configuration, basic tool definition via FunctionTool, and callback patterns for state initialization and lifecycle hooks Includes built-in tool imports, agent orchestration (SequentialAgent, ParallelAgent, LoopAgent), and state management through CallbackContext ADK 2.0 Workflow API available as opt-in experimental feature for graph-based pipelines with conditional routing and parallel processing; requires Python 3.11+ and explicit user consent before use Requires an existing scaffolded project; use agents-cli scaffold create or scaffold enhance before writing agent code