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このリポジトリの skills
SkillsMP は majiayu000/claude-skill-registry から 5,417 件の skill を収集しています。skill を開くとソースと詳細を確認できます。
majiayu000/claude-skill-registry収集済み skill 5,417 件中 40 件を表示しています。
複雑な問題を分解し、推論の流れを整理して短い根拠サマリーを作成するスキル。 前提整理、論点分解、選択肢比較、判断理由の要約を通じて、説明責任のあるアウトプットを作る。 Anchors: • The Pragmatic Programmer / 適用: 問題分解 / 目的: 実践的な整理 • Thinking, Fast and Slow / 適用: 判断バイアス確認 / 目的: 判断根拠の明確化 • Critical Thinking / 適用: 論点整理 / 目的: 根拠の一貫性 Trigger: Use…
Strange attractor with sensitive dependence on initial conditions
原文の言語: 英語
Cirq is Google Quantum AI's open-source framework for designing, simulating, and running quantum circuits on quantum computers and simulators.
原文の言語: 英語
Expert assistant for conducting remote experiments with Claude-Light - a web-accessible RGB LED and spectral sensor instrument for statistics, regression, optimization, and design of experiments
原文の言語: 英語
Semantic image-text matching with CLIP and alternatives. Use for image search, zero-shot classification, similarity matching. NOT for counting objects, fine-grained classification (celebrities, car models), spatial reasoning, or compositional queries.…
原文の言語: 英語
Neural network training and deployment in Flow Nexus cloud. Use for distributed ML training, model inference, and neural network lifecycle management.
原文の言語: 英語
Dimensionality reduction and clustering for single-cell RNA-seq using Seurat (R) and Scanpy (Python). Use for running PCA, computing neighbors, clustering with Leiden/Louvain algorithms, generating UMAP/tSNE embeddings, and visualizing clusters.
原文の言語: 英語
Quotient redundant skill paths via coequalizers, preserving GF(3) conservation
原文の言語: 英語
ComfyUI node-based Stable Diffusion interface. GPU-accelerated image generation with custom node support and CivitAI model downloads. Use 'ujust comfyui' for configuration, lifecycle management, and model/node operations.
原文の言語: 英語
SOTA Computer Vision Expert. Specialized in YOLO, Segment Anything, Vision Language Models, and real-time spatial analysis. Use when building or optimizing computer vision systems.
原文の言語: 英語
Build computer vision systems using CNNs and modern architectures. Use for image classification, object detection (YOLO, Faster R-CNN), image segmentation, face recognition, and visual analysis tasks.
原文の言語: 英語
Build production computer vision pipelines for object detection, tracking, and video analysis. Handles drone footage, wildlife monitoring, and real-time detection. Supports YOLO, Detectron2, TensorFlow, PyTorch. Use for archaeological surveys, conservation,…
原文の言語: 英語
See the main Model Explainability skill for comprehensive coverage of confidence scoring and calibration.
原文の言語: 英語
Inspect classifier errors with confusion matrices after predictions already exist. Use for per-class error analysis, threshold tradeoffs, and label-confusion diagnosis; not for regression metrics or full model training ownership.
原文の言語: 英語
Interacting dynamical systems
原文の言語: 英語
Riehl-Shulman covariant fibrations for dependent types over directed
原文の言語: 英語
Use AgentPMT external API to run the Create 3D Model From Image tool with wallet signatures, credits purchase, or credits earned from jobs.
原文の言語: 英語
Train task-specific classifiers for the extractor pipeline. Supports vision, text, and hybrid classifiers with GRPO training and execution feedback. Includes data collection, confidence-based routing, and shadow deployment.
原文の言語: 英語
Designs guide RNA (gRNA) sequences for CRISPR-Cas9 editing, including off-target analysis. Use when a user needs to edit a gene or asks for gRNA sequences.
原文の言語: 英語
Computer vision ML pipelines for image classification, object detection, semantic segmentation, and image generation. Activates for "computer vision", "image classification", "object detection", "CNN", "ResNet", "YOLO", "image segmentation", "image…
原文の言語: 英語
Cybernetic immune system with Varela+Friston+Powers for Self/Non-Self discrimination via reafference, GF(3) trit encoding, and information geometry
原文の言語: 英語
Master Python programming, Data Science, AI/LLM engineering, and Database administration. Use this as a central index to access specialized sub-skills.
原文の言語: 英語
Advanced analytics and machine learning for data-driven insights. Use when performing statistical analysis, building predictive models, designing experiments, or creating data visualizations.
原文の言語: 英語
Datenbankrecht für Musik-, Film- und Bildarchive: §§ 87a-87e UrhG für Mediendatenbanken, Schichtenschutz (Datenbankherstellerrecht + Urheberrecht an Einzelwerken), Lizenzmodelle für Stock-Media-Portale und Verwertungsgesellschaften (GEMA, VGBild),…
原文の言語: ドイツ語
Guide for debugging distributed training issues in AReaL. Use when user encounters hangs, wrong results, OOM, or communication errors.
原文の言語: 英語
Build and train deep neural networks including CNNs, RNNs, Transformers, and advanced architectures. Use for image classification, object detection, NLP, sequence modeling, transfer learning, and complex pattern recognition tasks.
原文の言語: 英語
Deep learning-based variant calling with Google DeepVariant. Provides high accuracy for germline SNPs and indels from Illumina, PacBio, and ONT data.
原文の言語: 英語
Expert guidance for Design of Experiments (DOE) in Python - interactive goal-driven design selection, classical DOE (factorial, response surface, screening), Bayesian optimization with Gaussian processes, model-driven optimal designs, active learning, and…
原文の言語: 英語
Diffusion model training and inference patterns including UNet/DiT architectures, noise schedules, CFG, ControlNet, and LoRA. Use when building or fine-tuning image generation models.
原文の言語: 英語
Build digital twin patient models to test drug efficacy and toxicity in virtual environments
原文の言語: 英語
DiscoPy Operads Skill
原文の言語: 英語
Automatically discover machine learning and AI skills when working with machine learning, PyTorch, training, inference, RAG, embeddings, fine-tuning, LLM, DSPy, HuggingFace, or diffusion models. Activates for ML development tasks.
原文の言語: 英語
Gradient-free optimization via discrete perturbations and trit-based learning
原文の言語: 英語
Compute evolutionary distances and build phylogenetic trees using Biopython Bio.Phylo.TreeConstruction. Use for creating distance matrices from alignments, building NJ/UPGMA trees, parsimony analysis, and generating bootstrap consensus trees.
原文の言語: 英語
Use when building ML/AI apps in Rust. Keywords: machine learning, ML, AI, tensor, model, inference, neural network, deep learning, training, prediction, ndarray, tch-rs, burn, candle, 机器学习, 人工智能, 模型推理
原文の言語: 判定不能
Get the correct command syntax for downloading models from HuggingFace. Use when downloading models, running "hf download", or pulling models from HuggingFace repos.
原文の言語: 英語
Direct Preference Optimization for learning from preference pairs. Covers DPOTrainer, preference dataset preparation, implicit reward modeling, and beta tuning for stable preference learning without explicit reward models. Includes thinking quality patterns.
原文の言語: 英語
This is an **alias skill** so docs can reference `77-mlops-data-engineering/drift-detection`. In this repo, drift guidance is covered by: - `77-mlops-data-engineering/drift-detection-retraining` (trig
原文の言語: 英語
Expert in drone systems, computer vision, and autonomous navigation. Specializes in flight control, SLAM, object detection, sensor fusion, and path planning. Activate on "drone", "UAV", "SLAM", "visual odometry", "PID control", "MAVLink", "Pixhawk", "path…
原文の言語: 英語
Self-regulating Goblins actor implementing Ivan Illich's dynamic sufficiency
原文の言語: 英語