Total 58,116 skills, AI & Machine Learning has 9659 skills
Showing 12 of 9659 skills
Host simulated panel discussions and debates among AI-simulated domain experts. Supports roundtable, Oxford-style, and Socratic formats with heterogeneous expert personas, anti-groupthink mechanisms, and structured synthesis. Use when exploring complex topics from multiple expert perspectives, testing argument strength, academic brainstorming, or understanding trade-offs in decisions. NOT for one-on-one conversations, simple Q&A, or real-time debates.
The foundational knowledge distillation pattern for building and maintaining an AI-powered Obsidian wiki. Based on Andrej Karpathy's LLM Wiki architecture. Use this skill whenever the user wants to understand the wiki pattern, set up a new knowledge base, or needs guidance on the three-layer architecture (raw sources → wiki → schema). Also use when discussing knowledge management strategy, wiki structure decisions, or how to organize distilled knowledge. This is the "theory" skill — other skills handle specific operations (ingesting, querying, linting).
Start a new learning episode in the self-learning memory system with proper context. Use this skill when beginning a new task that should be tracked for learning from execution patterns.
Generate optimized prompts for YouThumb.ai YouTube thumbnails. Guided 4-step workflow: collect person name, map visual assets, describe the video, then generate 5 distinct ready-to-paste prompts. Use when the user says "thumbnail prompt", "YouThumb prompt", "generate thumbnail", "miniature YouTube", "prompt for my thumbnail", "help me with YouThumb", or when preparing YouTube thumbnail prompts.
Produce a token-bounded context pack from the Obsidian wiki — a compact, structured slice of the most relevant pages for a topic or recent activity, designed for downstream consumption by another agent or skill. Use when the user says "/wiki-context-pack", "make a context pack", "give me a context slice for X", "pack the wiki for my agent", or "bounded context for Y". Different from wiki-query (which answers a question) — this produces reusable input material for a downstream task.
Analise de mercado imobiliario para leiloes. Liquidez, desagio tipico, ROI, estrategias de saida (flip/reforma/renda), Selic 2025 e benchmark CDI/FII.
Guides beginner-to-intermediate developers through web development, Claude Code skills creation, and AI-assisted coding workflows. Use when the user asks about "vibe coding," "learning to code," "web development basics," "Claude skills," "building websites," "frontend," "backend," or wants help with HTML, CSS, JavaScript, or deployment.
Integrate a HuggingFace Computer Vision model into the NVIDIA TAO Toolkit ecosystem (tao-core config, tao-pytorch trainer, tao-deploy TensorRT pipeline). Use when the user asks to "integrate a HuggingFace model into TAO", "add an HF model to TAO Toolkit", "wire a HuggingFace ViT/DETR/ SegFormer into tao-pytorch", "build a TAO trainer + deploy pipeline for an HF CV model", or pastes a HuggingFace model URL/ID and wants it turned into a TAO model. Covers the full 7-phase loop: prerequisites check, HuggingFace inspection and validation, codebase exploration, tao-core configuration and native trainer implementation, ONNX export plus TensorRT deploy integration, packaging and L0 testing, container-based end-to-end validation, and (conditional) accuracy/latency tuning. Supports classification, object detection, semantic / instance / panoptic segmentation, zero-shot detection, and depth estimation.
Guide for building Graph Neural Networks with PyTorch Geometric (PyG). Use this skill whenever the user asks about graph neural networks, GNNs, node classification, link prediction, graph classification, message passing networks, heterogeneous graphs, neighbor sampling, or any task involving torch_geometric / PyG. Also trigger when you see imports from torch_geometric, or the user mentions graph convolutions (GCN, GAT, GraphSAGE, GIN), graph data structures, or working with relational/network data. Even if the user just says 'graph learning' or 'geometric deep learning', use this skill.
OneFormer for universal image segmentation. Unifies panoptic, instance, and semantic segmentation with a single architecture using task-conditioned queries. Use when training, evaluating, exporting, quantizing, or running inference for a TAO OneFormer model. Trigger phrases include "train OneFormer", "universal segmentation", "task-conditioned segmentation", "panoptic / instance / semantic in one model".
Performs deep Root Cause Analysis (RCA) on NVIDIA TAO Visual ChangeNet classification experiments with image-evidence-driven investigation. Use when analyzing ChangeNet model failures, investigating poor recall / FAR / PASS-NO_PASS metrics, auditing visual inspection pipeline quality, or running an RCA report for an AOI defect-detection model. Trigger phrases include "RCA on my ChangeNet model", "why is my AOI model failing", "audit ChangeNet predictions", "investigate FAR regressions", "root cause analysis on visual-changenet".
Decide how to split skill content between SKILL.md and reference files for context efficiency and reliable triggering. Use this whenever creating a new Claude skill, refactoring an existing one, or when a SKILL.md is growing past 300-400 lines. Also trigger when the user mentions "progressive disclosure", "reference files", "splitting skills", "skill bundling", "context window for skills", "SKILL.md too long", "what goes in references/", "skill structure", or expresses any uncertainty about where to put content within a skill. Use this even if the user phrases the question as a triggering problem ("how do I make my skill trigger better"), because that question is often confused with the splitting question and needs to be disentangled first.