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Found 1,203 Skills
Decision framework for choosing between regex and LLM when parsing structured text — start with regex, add LLM only for low-confidence edge cases.
Technical documentation discovery via context7 and web search. Capabilities: library/framework docs lookup, topic-specific search. Keywords: llms.txt, context7, documentation, library docs, API docs. Use when: searching library documentation, finding framework guides, looking up API references.
ML supply chain security scanner. Scans model files, scores risk (0-100), maps to 5 global compliance frameworks (ISM-2072, EU AI Act, OWASP LLM, MITRE ATLAS, NIST AI RMF), and provides remediation steps. Zero-config, auto-installs scanners. Use when the user asks to scan a model, check if a model is safe, audit ML security posture, check compliance, inspect pickle/safetensors/pytorch files, or mentions model supply chain security. Also trigger on ISM-2072, EU AI Act, OWASP LLM06, model risk score, "is this model safe", "scan my models", "check compliance".
Eino framework overview, concepts, and navigation. Use when a user asks general questions about Eino, needs help getting started, wants to understand the architecture, or is unsure which Eino skill to use. Eino is a Go framework for building LLM applications with components, orchestration graphs, and an agent development kit.
Provides PyTorch-native distributed LLM pretraining using torchtitan with 4D parallelism (FSDP2, TP, PP, CP). Use when pretraining Llama 3.1, DeepSeek V3, or custom models at scale from 8 to 512+ GPUs with Float8, torch.compile, and distributed checkpointing.
Trigger when the user wants to create a new dashboard, set up monitoring for a service or infrastructure component, or import a pre-built dashboard template. Includes requests like "create a dashboard for PostgreSQL", "monitor my Redis cluster", "set up observability for my k8s cluster", "I need a dashboard for tracking LLM costs".
This skill should be used when the user asks to "quantize a model", "run PTQ", "post-training quantization", "NVFP4 quantization", "FP8 quantization", "INT8 quantization", "INT4 AWQ", "quantize LLM", "quantize MoE", "quantize VLM", or needs to produce a quantized HuggingFace or TensorRT-LLM checkpoint from a pretrained model using ModelOpt.
This skill should be used to search the local Obsidian vault / markdown knowledge base by meaning, not just keywords, using the on-device qmd engine (BM25 + vector + LLM rerank). Trigger when the user asks to "search my vault/notes", "find notes about X", "what do my notes say about Y", "do I have anything on Z", "semantic search my knowledge base", or wants concept/cross-lingual retrieval over markdown. Fully local — nothing leaves the machine.
Test PydanticAI agents using TestModel, FunctionModel, VCR cassettes, and inline snapshots. Use when writing unit tests, mocking LLM responses, or recording API interactions.
Senior Agile Facilitator & Delivery Architect for 2026. Specialized in AI-enhanced Scrum orchestration, automated ticket management, and high-velocity sprint coordination. Expert in utilizing LLMs to synthesize daily updates, detect blockers before they arise, and maintain a high-integrity backlog across GitHub Issues, Jira, and linear.
Use when integrating Foundation Models framework, implementing on-device AI with Apple Intelligence, building tool-calling AI features, working with guided generation schemas, converting models with Core ML and coremltools, or running open-source LLMs on Apple Silicon. Covers Foundation Models (LanguageModelSession, @Generable, @Guide, SystemLanguageModel, structured output, tool calling), Core ML (coremltools, model conversion, quantization, palettization, pruning, Neural Engine, MLTensor), MLX Swift (transformer inference, unified memory), and llama.cpp (GGUF, cross-platform LLM).
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).