Skill ディレクトリ

AI Agent のための再利用可能な Skill を見つける。

タスクで実際の GitHub Skill を検索し、利用前に Stars、Trust、監査、カテゴリ、インストール経路を確認できます。

すべての推奨は、リポジトリ、監査、インストール経路に明確につながっています。

検索結果: anomaly

英語版ディレクトリ

A Python library for anomaly detection across tabular, time series, graph, text, and image data. 60+ detectors, benchmark-backed ADEngine orchestration, and an agentic workflow for AI agents.

9.9K
Stars
86/100
信頼
カテゴリ: ml-automation監査

A python library for user-friendly forecasting and anomaly detection on time series.

9.4K
Stars
86/100
信頼
カテゴリ: ml-automation監査

A Python toolkit/library for reality-centric machine/deep learning & data mining on partially-observed time series, with 50+ SOTA neural network models for scientific analysis tasks (imputation, classification, clustering, forecasting, anomaly detection, cleaning) on incomplete industrial irregularly-sampled multivariate TS with NaN missing values

2.0K
Stars
83/100
信頼
カテゴリ: data-analysis監査

Anomaly detection related books, papers, videos, and toolboxes. Last update late 2025 for LLM and VLM works!

9.3K
Stars
85/100
信頼
カテゴリ: ml-automation監査

A curated list of Graph/Transformer-based fraud, anomaly, and outlier detection papers & resources

1.9K
Stars
75/100
信頼
カテゴリ: ml-automation監査

This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

34K
Stars
80/100
信頼
カテゴリ: research監査

Jupyter Notebook tutorials on solving real-world problems with Machine Learning & Deep Learning using PyTorch. Topics: Face detection with Detectron 2, Time Series anomaly detection with LSTM Autoencoders, Object Detection with YOLO v5, Build your first Neural Network, Time Series forecasting for Coronavirus daily cases, Sentiment Analysis with BER

2.5K
Stars
71/100
信頼
カテゴリ: robotics-iot監査

Find big moving stocks before they move using machine learning and anomaly detection

1.9K
Stars
72/100
信頼
カテゴリ: finance監査

Anomaly detection using LoOP: Local Outlier Probabilities, a local density based outlier detection method providing an outlier score in the range of [0,1].

330
Stars
67/100
信頼
カテゴリ: ml-automation監査

Streaming Anomaly Detection Framework in Python (Outlier Detection for Streaming Data)

290
Stars
70/100
信頼
カテゴリ: ml-automation監査

A collection of official Agent Skills for querying, diagnosing, and managing VictoriaMetrics products (metrics, logs, traces, and alerts).

45
Stars
69/100
信頼
カテゴリ: data監査