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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

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

Execute Azure deployments for prepared applications with built-in error recovery and validation. Requires .azure/plan.md with Validated status from azure-validate and azure-prepare skills; will not proceed without both prerequisites Executes azd up , azd deploy , terraform apply , and az deployment commands with error handling and post-deployment verification Includes pre-deploy checklist, recipe-based deployment workflows, and post-deploy configuration for SQL managed identity and Entity Framework migrations Supports applications with API Management gateway infrastructure created during preparation phase

Prepare Azure applications for deployment with infrastructure-as-code, configuration, and containerization. Generates Bicep or Terraform infrastructure code, azure.yaml configuration, and Dockerfiles based on application type and deployment target Requires mandatory plan-first workflow: analyze workspace, gather requirements, select recipe (AZD, AZCLI, Bicep, or Terraform), design architecture, then execute only after user approval Covers new app creation, modernization, and updates; routes specialized requests (copilot SDK, cloud migration, AI gateway) to dedicated skills before resuming preparation Hands off to azure-validate for infrastructure validation and azure-deploy for deployment execution; plan status must be updated to "Ready for Validation" before validation begins

Unified access to Azure blob storage, file shares, queues, tables, and data lake services. Supports five storage service types: Blob Storage for objects and backups, File Shares for SMB access, Queue Storage for async messaging, Table Storage for NoSQL key-value data, and Data Lake for big data analytics MCP server tools enable listing storage accounts, containers, and blobs, plus downloading and uploading blob content directly Configurable access tiers (hot, cool, cold, archive) and redundancy options (LRS, ZRS, GRS, GZRS) for cost and durability optimization CLI fallback available via az storage commands when MCP is not enabled; SDK references provided for Python, TypeScript, Java, Rust, and Go