llm▌

Multi-agent planning council that orchestrates independent implementation plans, anonymizes them, then merges into one final plan. \n \n Supports configurable planner agents (Codex, Claude, Gemini, OpenCode, or custom CLI commands) running in parallel, with optional judge override \n Conducts structured intake questioning before plan generation to clarify ambiguities, constraints, and success criteria \n Produces validated Markdown outputs with automatic retry logic (up to 2 attempts) and failur

Behavioral guidelines to reduce common LLM coding mistakes, focusing on simplicity and clarity in coding practices.

Security rules for building secure LLM applications, based on the OWASP Top 10 for LLM Applications 2025.

Production-ready patterns for RAG pipelines, agent architectures, prompt management, and LLMOps monitoring. \n \n Covers five core RAG strategies: document chunking, embedding selection, retrieval methods (semantic, hybrid, multi-query, compression), and context-aware generation with citations \n Includes four agent patterns: ReAct (reasoning + acting), function calling, plan-and-execute, and multi-agent collaboration with specialized roles \n Provides prompt engineering practices: templating wi

Auto-generates a data-driven LLM usage monitoring dashboard with cost tracking, user ranking, and PM insights. \n \n Tracks token counts, API costs, and latency across OpenAI, Anthropic, Gemini, and OpenRouter using Tokuin CLI; stores metrics in JSONL format with user context and prompt categorization \n Provides two deployment options: Next.js admin dashboard with per-user drilldown pages, or lightweight single-file HTML dashboard with charts, ranking tables, and search \n Includes automatic PM

Overrides default LLM truncation behavior. Enforces complete code generation, bans placeholder patterns, and handles token-limit splits cleanly. Apply to any task requiring exhaustive, unabridged output.

Run and debug C# MCP servers locally. Covers IDE configuration, MCP Inspector testing, GitHub Copilot Agent Mode integration, logging setup, and troubleshooting. USE FOR: running MCP servers locally with dotnet run, configuring VS Code or Visual Studio for MCP debugging, testing tools with MCP Inspector, testing with GitHub Copilot Agent Mode, diagnosing tool registration issues, setting up mcp.json configuration, debugging MCP protocol messages, configuring logging for stdio and HTTP servers. DO NOT USE FOR: creating new MCP servers (use mcp-csharp-create), writing automated tests (use mcp-csharp-test), publishing or deploying to production (use mcp-csharp-publish).

Publish and deploy C# MCP servers. Covers NuGet packaging for stdio servers, Docker containerization for HTTP servers, Azure Container Apps and App Service deployment, and publishing to the official MCP Registry. USE FOR: packaging stdio MCP servers as NuGet tools, creating Dockerfiles for HTTP MCP servers, deploying to Azure Container Apps or App Service, publishing to the MCP Registry at registry.modelcontextprotocol.io, configuring server.json for MCP package metadata, setting up CI/CD for MCP server publishing. DO NOT USE FOR: publishing general NuGet libraries (not MCP-specific), general Docker guidance unrelated to MCP, creating new servers (use mcp-csharp-create), debugging (use mcp-csharp-debug), writing tests (use mcp-csharp-test).

Guides technology selection and implementation of AI and ML features in .NET 8+ applications using ML.NET, Microsoft.Extensions.AI (MEAI), Microsoft Agent Framework (MAF), GitHub Copilot SDK, ONNX Runtime, and OllamaSharp. Covers the full spectrum from classic ML through modern LLM orchestration to local inference. Use when adding classification, regression, clustering, anomaly detection, recommendation, LLM integration (text generation, summarization, reasoning), RAG pipelines with vector search, agentic workflows with tool calling, Copilot extensions, or custom model inference via ONNX Runtime to a .NET project. DO NOT USE FOR projects targeting .NET Framework (requires .NET 8+), the task is pure data engineering or ETL with no ML/AI component, or the project needs a custom deep learning training loop (use Python with PyTorch/TensorFlow, then export to ONNX for .NET inference).

Create MCP servers using the C# SDK and .NET project templates. Covers scaffolding, tool/prompt/resource implementation, and transport configuration for stdio and HTTP. USE FOR: creating new MCP server projects, scaffolding with dotnet new mcpserver, adding MCP tools/prompts/resources, choosing stdio vs HTTP transport, configuring MCP hosting in Program.cs, setting up ASP.NET Core MCP endpoints with MapMcp. DO NOT USE FOR: debugging or running existing servers (use mcp-csharp-debug), writing tests (use mcp-csharp-test), publishing or deploying (use mcp-csharp-publish), building MCP clients, non-.NET MCP servers.