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Found 8,794 Skills
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).
Deterministic plan lifecycle management via scripts/plan-manager.py: create, track, check, complete, and abandon task plans. Use when user says "/plans", needs to create a multi-phase plan, track progress on active plans, or manage plan lifecycle (complete, abandon, audit). Do NOT use for one-off tasks that need no tracking, feature implementation, or debugging workflows.
AI-assisted UI generation patterns for json-render, v0, Bolt, and Cursor workflows. Covers prompt engineering for component generation, review checklists for AI-generated code, design token injection, refactoring for design system conformance, and CI gates for quality assurance. Use when generating UI components with AI tools, rendering multi-surface MCP visual output, reviewing AI-generated code, or integrating AI output into design systems.
Implement the Syncfusion React Inline AI Assist component. Use this skill to add inline AI suggestions, integrate AI services such as OpenAI, Gemini, Lite-LLM, or Ollama, configure command and response actions, customize toolbars, handle events, and support real-time prompt-response workflows in React.
MiniMax multimodal model skill — use MiniMax Multi-Modal models for speech, music, video, and image. Create voice, music, video, and images with MiniMax AI: TTS (text-to-speech, voice cloning, voice design, multi-segment), music (songs, instrumentals), video (text-to-video, image-to-video, start-end frame, subject reference, templates, long-form multi-scene), image (text-to-image, image-to-image with character reference), and media processing (convert, concat, trim, extract). Use when the user mentions MiniMax, multimodal generation, or wants speech/music/video/image AI, MiniMax APIs, or FFmpeg workflows alongside MiniMax outputs.
Multi-agent systems with LangGraph - supervisor/swarm/handoff/router patterns, state coordination, Deep Agents, guardrails, testing, observability, deployment. Use when building multi-agent workflows, coordinating agents, or need cost-optimized orchestration. Uses Claude, DeepSeek, Gemini (no OpenAI).
Use when managing Function Compute AgentRun resources via OpenAPI (runtime, sandbox, model, memory, credentials), including creating runtimes/endpoints, querying status, and troubleshooting AgentRun workflows.
Parse et explique les messages HL7 v2.5 IHE PAM (Patient Administration Management). Identifie le type de message, extrait les segments (MSH, EVN, PID, PV1, PV2), valide la structure et fournit des explications détaillées des messages ADT pour les workflows d'administration des patients.
Génère des règles de design system personnalisées pour le codebase de l'utilisateur. À utiliser quand l'utilisateur dit « créer des règles de design system », « générer des règles pour mon projet », « configurer les règles de design », « personnaliser les guidelines du design system », ou veut établir des conventions spécifiques au projet pour les workflows Figma-vers-code. Nécessite une connexion au serveur MCP Figma.
Chef InSpec integration. Manage data, records, and automate workflows. Use when the user wants to interact with Chef InSpec data.
MetricFire integration. Manage data, records, and automate workflows. Use when the user wants to interact with MetricFire data.
Entry P1 category router for file access and upload workflows. Use when testing download endpoints, file paths, local file inclusion, upload flows, preview pipelines, archive extraction, or storage and sharing boundaries.