Ciza Basic

Azure API Management SDK for Python. Use for managing APIM services, APIs, products, subscriptions, and policies. Triggers: "azure-mgmt-apimanagement", "ApiManagementClient", "APIM", "API gateway", "API Management".

Azure Bot Service Management SDK for Python. Use for creating, managing, and configuring Azure Bot Service resources. Triggers: "azure-mgmt-botservice", "AzureBotService", "bot management", "conversational AI", "bot channels".

Azure Fabric Management SDK for Python. Use for managing Microsoft Fabric capacities and resources. Triggers: "azure-mgmt-fabric", "FabricMgmtClient", "Fabric capacity", "Microsoft Fabric", "Power BI capacity".

Azure Monitor Ingestion SDK for Python. Use for sending custom logs to Log Analytics workspace via Logs Ingestion API. Triggers: "azure-monitor-ingestion", "LogsIngestionClient", "custom logs", "DCR", "data collection rule", "Log Analytics".

Azure Monitor OpenTelemetry Exporter for Python. Use for low-level OpenTelemetry export to Application Insights. Triggers: "azure-monitor-opentelemetry-exporter", "AzureMonitorTraceExporter", "AzureMonitorMetricExporter", "AzureMonitorLogExporter".

Azure Monitor OpenTelemetry Distro for Python. Use for one-line Application Insights setup with auto-instrumentation. Triggers: "azure-monitor-opentelemetry", "configure_azure_monitor", "Application Insights", "OpenTelemetry distro", "auto-instrumentation".

Azure Monitor Query SDK for Python. Use for querying Log Analytics workspaces and Azure Monitor metrics. Triggers: "azure-monitor-query", "LogsQueryClient", "MetricsQueryClient", "Log Analytics", "Kusto queries", "Azure metrics".

Azure AI Search SDK for Python. Use for vector search, hybrid search, semantic ranking, indexing, and skillsets. Triggers: "azure-search-documents", "SearchClient", "SearchIndexClient", "vector search", "hybrid search", "semantic search".

Azure Service Bus SDK for Python messaging. Use for queues, topics, subscriptions, and enterprise messaging patterns. Triggers: "service bus", "ServiceBusClient", "queue", "topic", "subscription", "message broker".

Azure Speech to Text REST API for short audio (Python). Use for simple speech recognition of audio files up to 60 seconds without the Speech SDK. Triggers: "speech to text REST", "short audio transcription", "speech recognition REST API", "STT REST", "recognize speech REST". DO N

Azure Blob Storage SDK for Python. Use for uploading, downloading, listing blobs, managing containers, and blob lifecycle. Triggers: "blob storage", "BlobServiceClient", "ContainerClient", "BlobClient", "upload blob", "download blob".

Azure Data Lake Storage Gen2 SDK for Python. Use for hierarchical file systems, big data analytics, and file/directory operations. Triggers: "data lake", "DataLakeServiceClient", "FileSystemClient", "ADLS Gen2", "hierarchical namespace".

Azure Storage File Share SDK for Python. Use for SMB file shares, directories, and file operations in the cloud. Triggers: "azure-storage-file-share", "ShareServiceClient", "ShareClient", "file share", "SMB".

Azure Queue Storage SDK for Python. Use for reliable message queuing, task distribution, and asynchronous processing. Triggers: "queue storage", "QueueServiceClient", "QueueClient", "message queue", "dequeue".

Create FastAPI routers with CRUD operations, authentication dependencies, and proper response models. Use when building REST API endpoints, creating new routes, implementing CRUD operations, or adding authenticated endpoints in FastAPI applications.

Microsoft 365 Agents SDK for Python. Build multichannel agents for Teams/M365/Copilot Studio with aiohttp hosting, AgentApplication routing, streaming responses, and MSAL-based auth. Triggers: "Microsoft 365 Agents SDK", "microsoft_agents", "AgentApplication", "start_agent_proces

Create Pydantic models following the multi-model pattern with Base, Create, Update, Response, and InDB variants. Use when defining API request/response schemas, database models, or data validation in Python applications using Pydantic v2.

Azure AI Agents Persistent SDK for .NET. Low-level SDK for creating and managing AI agents with threads, messages, runs, and tools. Use for agent CRUD, conversation threads, streaming responses, function calling, file search, and code interpreter. Triggers: "PersistentAgentsClien

Azure AI Document Intelligence SDK for .NET. Extract text, tables, and structured data from documents using prebuilt and custom models. Use for invoice processing, receipt extraction, ID document analysis, and custom document models. Triggers: "Document Intelligence", "DocumentIn

Azure OpenAI SDK for .NET. Client library for Azure OpenAI and OpenAI services. Use for chat completions, embeddings, image generation, audio transcription, and assistants. Triggers: "Azure OpenAI", "AzureOpenAIClient", "ChatClient", "chat completions .NET", "GPT-4", "embeddings"

Generate a Node.js build script that converts the VitePress wiki to Azure DevOps Wiki-compatible markdown in dist/ado-wiki/. Transforms Mermaid syntax, strips front matter, fixes links.

Generate AGENTS.md files for pertinent repository folders — only where missing. Provides coding agents with build commands, testing instructions, code style, project structure, and boundaries.

Ask a question about the repository using wiki context and source file references

Package generated wiki pages into a VitePress site with dark theme, dark-mode Mermaid diagrams, and click-to-zoom

Generate only the hierarchical wiki structure (table of contents) as JSON for the current repository

Generate a structured changelog from recent git commits, categorized by change type

Generate a fast, concise wiki for the current repository — optimized for speed, minimal tool calls, and avoiding rate limits. Produces a complete VitePress site without running builds.

Generate a GitHub Actions workflow to deploy the VitePress wiki site to GitHub Pages

Generate a complete wiki for the current repository — catalogue + all pages + onboarding guides + VitePress site with dark-mode Mermaid diagrams

Generate llms.txt and llms-full.txt files for the wiki — LLM-friendly project summaries following the llms.txt specification

Generate four audience-tailored onboarding guides in an onboarding/ folder — Contributor, Staff Engineer, Executive, and Product Manager

Generate a single wiki page with dark-mode Mermaid diagrams, source citations, and first-principles depth

Conduct multi-turn deep research on a specific topic — traces actual code paths with zero tolerance for shallow analysis