Agent skill · research science · k-dense-ai
aeon
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.
Why this skill is useful
Provides specialized algorithms for time series analysis that the AI wouldn't reliably generate on its own.
What it needs
Requires aeon, python installed locally. About 6k tokens when loaded. Last updated 2026-08-03. 32,849 stars on the source repository.
What this skill does
Aeon Time Series Machine Learning Overview Aeon is a scikit-learn compatible Python toolkit for time series machine learning (aeon-toolkit.org). It provides algorithms across classification, regression, clustering, forecasting, anomaly detection, segmentation, similarity search, distances, transformations, benchmarking, and visualization — with a consistent estimator API. Version note: Examples target aeon 1.x (stable docs: v1.4.0, March 2026). The v1.0 release reworked forecasting and transformations; import paths differ from aeon 0.x/sktime-era code. When to Use This Skill Apply this skill when: Classifying or predicting from time series data Detecting anomalies or change points in temporal sequences Clustering similar time series patterns Forecasting future values Finding repeated patterns (motifs) or unusual subsequences (discords) Comparing time series with specialized distance metrics Extracting features from temporal data Installation Requires Python 3.10+ (3.11+ recommended). Pin a 1.x release for reproducibility: For deep learning forecasters/classifiers and other optional estimators: On zsh, quote the extras: uv pip install "aeon[allextras]>=1.4,<2". Experimental modules Upstream treats forecasting, anomalydetection, segmentation, similaritysearch, and visualisation as experimental — interfaces may change between minor releases. Prefer stable modules (classification, regression, clustering, distances, transformations) for production pipelines unless you need these tasks. Core Capabilities 1. Time Series Classification Categorize time series into predefined classes. See references/classification.md for complete algorithm catalog. Quick Start: Algorithm Selection: Speed + Performance: MiniRocketClassifier, Arsenal Maximum Accuracy: HIVECOTEV2, InceptionTimeClassifier Interpretability: ShapeletTransformClassifier, Catch22Classifier Small Datasets: KNeighborsTimeSeriesClassifier with DTW distance 2. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills k-dense-ai/aeon