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Found 1,836 Skills
Process multiple video generation requests efficiently with Kling AI. Use when generating multiple videos or building content pipelines. Trigger with phrases like 'klingai batch', 'kling ai bulk', 'multiple videos klingai', 'klingai parallel generation'.
Generate a custom trace annotation web app for open coding during LLM error analysis. Use when the user wants to review LLM traces, annotate failures with freeform comments, and do first-pass qualitative labeling (open coding). Also use when the user mentions "annotate traces", "trace review tool", "open coding tool", "label traces", "build an annotation interface", "review LLM outputs", or wants to manually inspect pipeline traces before building a failure taxonomy. This skill produces a tailored Python web application using FastHTML, TailwindCSS, and HTMX.
Design-to-code pipeline: extract copy from URLs, extract design tokens from images, then build React components or HTML preview variants. Use when: extracting content from websites, extracting design systems, generating frontend code, previewing design variants, sending to Figma via MCP. Triggers on "extract copy", "extract design", "build frontend", "generate variants", "export design", "send to Figma".
Build LLM applications using Dify's visual workflow platform. Use when creating AI chatbots, implementing RAG pipelines, developing agents with tools, managing knowledge bases, deploying LLM apps, or building workflows with drag-and-drop. Supports hundreds of LLMs, Docker/Kubernetes deployment.
Build, test, and deploy applications using GitHub Actions workflows. Create CI/CD pipelines, configure runners, manage secrets, and automate software delivery. Use when working with GitHub repositories, automating builds, running tests, or deploying applications.
Launch automated multi-skill pipeline that chains skills into a loop. Use when user says "run pipeline", "automate research to PRD", "full pipeline", "research and validate", "scaffold to build", "loop until done", or "chain skills". Do NOT use for single skills (use the skill directly).
Use when adding multi-format RAG ingest, chunk, embed, and retrieval pipelines; pair with architect-python-uv-batch or architect-python-uv-fastapi-sqlalchemy.
Expert guidance for Azure DevOps CLI (az devops) - automation, pipelines, repos, boards, and artifacts management. Use when working with Azure DevOps, managing pipelines, creating work items, or when user mentions ADO, builds, releases, or Azure repos.
End-to-end test-fix workflow generate test sessions with progressive layers (L0-L3), then execute iterative fix cycles until pass rate >= 95%. Combines test-fix-gen and test-cycle-execute into a unified pipeline. Triggers on "workflow:test-fix-cycle".
Multi-agent orchestration layer for OpenAI Codex CLI. Provides 30 specialized agents, 40+ workflow skills, team orchestration in tmux, persistent MCP servers, and staged pipeline execution.
Builds ASP.NET Core APIs, EF Core data access, gRPC, SignalR, and backend services with middleware, security (OAuth, JWT, OWASP), resilience, messaging, OpenAPI, .NET Aspire, Semantic Kernel, HybridCache, YARP reverse proxy, output caching, Office documents (Excel, Word, PowerPoint), PDF, and architecture patterns. Spans 32 topic areas. Do not use for UI rendering patterns or CI/CD pipeline authoring.
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).