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Aeon

K-DenseCommunityCreated by K-Dense Inc.

  • Skill
  • Tool integration
  • Data and AI
  • MIT
  • Low risk
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From the author

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.
— the description by K-Dense Inc.

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

Aeon is a robust Python library designed for time series analysis, offering a unified estimator API for classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. It supports both univariate and multivariate data, providing specialized algorithms that go beyond standard machine learning approaches. The toolkit is built to be compatible with the scikit-learn ecosystem, making it accessible for researchers and data scientists to integrate temporal data processing into existing pipelines.

What it does

  • Classifies time series data using convolutional, deep learning, dictionary, and distance-based methods.
  • Performs time series regression and clustering with specialized distance metrics like DTW.
  • Detects anomalies and change points in temporal sequences using distance and distribution-based algorithms.
  • Provides forecasting capabilities for predicting future values in time series data.
  • Enables similarity search to find motifs and repeated patterns within temporal datasets.
  • Offers extensive feature extraction and transformation tools, including ROCKET and Catch22.

When to use it

  • When working with temporal data that requires specialized algorithms beyond standard ML.
  • When you need a scikit-learn compatible API for time series classification or regression.
  • When performing anomaly detection or motif discovery in sequential data.
  • When benchmarking different time series algorithms on standard datasets.
  • When you need to extract meaningful features from time series for use in traditional ML models.

Advantages

  • Consistent, scikit-learn compatible API reduces learning curve.
  • Wide range of specialized algorithms for diverse time series tasks.
  • Supports both univariate and multivariate time series analysis.
  • Includes benchmarking tools for evaluating model performance.
  • Highly modular with support for deep learning and feature-based approaches.

Usage examples

“Classify time series data using the RocketClassifier.”
The agent initializes a RocketClassifier, fits it to the training data, and evaluates its accuracy on the test set using the standard scikit-learn fit/score pattern.
“Detect anomalies in a time series using STOMP.”
The agent applies the STOMP detector to the series, calculates anomaly scores, and identifies points exceeding a specified percentile threshold.
“Perform time series clustering with TimeSeriesKMeans.”
The agent configures a TimeSeriesKMeans clusterer with a chosen distance metric and returns cluster labels and centers for the input data.
“Extract features from time series using ROCKET.”
The agent uses the ROCKET transformer to convert raw time series into a feature matrix, which is then used to train a standard classifier like RandomForest.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/k-dense-ai/aeon/download

  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip aeon.zip -d .agents/skills

Documentation by K-Dense Inc.

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: Reviewed against aeon 1.6.0 (Python 3.13). Small synthetic checks cover classification, regression, clustering, forecasts, preprocessing, distances, search, segmentation, matrix profiles, metrics, and local dataset I/O. Remote archive and TensorFlow training snippets are illustrative; they were not executed during this review. Reference catalogs are selected methods, not exhaustive lists. See the 1.6 release notes.

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 3.11-3.14. Pin the reviewed release for reproducibility:

uv pip install "aeon==1.6.0"

Install only the extras required by the chosen estimator. The broad optional set is available as:

uv pip install "aeon[all_extras]==1.6.0"

On zsh, quote the extras: uv pip install "aeon[all_extras]==1.6.0".

For the matrix-profile examples: uv pip install "aeon==1.6.0" stumpy. Range precision/recall/F-score depend on prts, whose current NumPy<2 requirement conflicts with aeon 1.6; use the runnable AUC metrics or a separately validated environment. Deep learning estimators use TensorFlow. Inspect an estimator's python_dependencies tag before installing optional packages.

Experimental modules

Upstream treats forecasting, anomaly_detection, segmentation, similarity_search, 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 selected methods.

Quick Start:

from aeon.classification.convolution_based import RocketClassifier
from aeon.datasets import load_classification

# Load data
X_train, y_train = load_classification("GunPoint", split="train")
X_test, y_test = load_classification("GunPoint", split="test")

# Train classifier
clf = RocketClassifier(n_kernels=10000)
clf.fit(X_train, y_train)
accuracy = clf.score(X_test, y_test)

Algorithm Selection:

  • Speed + Performance: MiniRocketClassifier, Arsenal

  • Accuracy candidates to validate: HIVECOTEV2, InceptionTimeClassifier

  • Interpretability: ShapeletTransformClassifier, Catch22Classifier

  • Small Datasets: KNeighborsTimeSeriesClassifier with DTW distance

2. Time Series Regression

Predict continuous values from time series. See references/regression.md for algorithms.

Quick Start:

from aeon.regression.convolution_based import RocketRegressor
from aeon.datasets import load_regression

X_train, y_train = load_regression("Covid3Month", split="train")
X_test, y_test = load_regression("Covid3Month", split="test")

reg = RocketRegressor()
reg.fit(X_train, y_train)
predictions = reg.predict(X_test)
3. Time Series Clustering

Group similar time series without labels. See references/clustering.md for methods.

Quick Start:

from aeon.clustering import TimeSeriesKMeans

clusterer = TimeSeriesKMeans(
    n_clusters=3,
    distance="dtw",
    averaging_method="ba"
)
labels = clusterer.fit_predict(X_train)
centers = clusterer.cluster_centers_
4. Forecasting

Predict future time series values (experimental module in aeon 1.x). See references/forecasting.md for forecasters.

Quick Start:

import numpy as np
from aeon.forecasting import NaiveForecaster
from aeon.forecasting.stats import ARIMA

y_train = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0])

# predict returns one value at the configured horizon, not a 5-value vector
naive = NaiveForecaster(strategy="drift", horizon=5)
naive.fit(y_train)
y_at_5 = naive.predict(y_train)  # 15.0

# ARIMA uses p/d/q (not order=); multi-step via iterative_forecast
arima = ARIMA(p=1, d=1, q=1)
y_pred = arima.iterative_forecast(y_train, prediction_horizon=5)  # fits once
5. Anomaly Detection

Identify unusual patterns or outliers. See references/anomaly_detection.md for detectors.

Quick Start:

from aeon.anomaly_detection.series.distance_based import STOMP

detector = STOMP(window_size=50)
anomaly_scores = detector.fit_predict(y)

# Higher scores indicate anomalies
threshold = np.percentile(anomaly_scores, 95)
anomalies = anomaly_scores > threshold
6. Segmentation

Partition time series into regions with change points. See references/segmentation.md.

Quick Start:

from aeon.segmentation import ClaSPSegmenter

segmenter = ClaSPSegmenter()
change_points = segmenter.fit_predict(y)
7. Similarity Search

Find similar patterns within or across time series. See references/similarity_search.md.

Quick Start:

from aeon.similarity_search.subsequence import MASS

# X_train: (n_cases, n_channels, n_timepoints); query: (n_channels, 20)
searcher = MASS(length=20, normalize=True).fit(X_train)
query = X_train[0, :, :20]
indices, distances = searcher.predict(query, k=3, X_index=(0, 0))
# indices rows are (case_index, window_start); self-match is excluded

Feature Extraction and Transformations

Transform time series for feature engineering. See references/transformations.md.

ROCKET Features:

from aeon.transformations.collection.convolution_based import Rocket

rocket = Rocket()
X_features = rocket.fit_transform(X_train)

# Use features with any sklearn classifier
from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier()
clf.fit(X_features, y_train)

Statistical Features:

from aeon.transformations.collection.feature_based import Catch22

catch22 = Catch22()
X_features = catch22.fit_transform(X_train)

Preprocessing:

from aeon.transformations.collection import MinMaxScaler, Normalizer

scaler = Normalizer()  # Z-normalization
X_normalized = scaler.fit_transform(X_train)

Distance Metrics

Specialized temporal distance measures. See references/distances.md for selected distances.

Usage:

from aeon.distances import dtw_distance, dtw_pairwise_distance

# Single distance
distance = dtw_distance(x, y, window=0.1)

# Pairwise distances
distance_matrix = dtw_pairwise_distance(X_train)

# Use with classifiers
from aeon.classification.distance_based import KNeighborsTimeSeriesClassifier

clf = KNeighborsTimeSeriesClassifier(
    n_neighbors=5,
    distance="dtw",
    distance_params={"window": 0.2}
)

Available Distances:

  • Elastic: DTW, DDTW, WDTW, ERP, EDR, LCSS, TWE, MSM

  • Lock-step: Euclidean, Manhattan, Minkowski

  • Shape-based: Shape DTW, SBD

Deep Learning Networks

Neural architectures for time series. See references/networks.md.

Architectures:

  • Convolutional: FCNClassifier, ResNetClassifier, InceptionTimeClassifier

  • Recurrent: RecurrentNetwork; temporal convolution: TCNNetwork

  • Autoencoders: AEFCNClusterer, AEResNetClusterer

Usage:

from aeon.classification.deep_learning import InceptionTimeClassifier

clf = InceptionTimeClassifier(n_epochs=100, batch_size=32)
clf.fit(X_train, y_train)
predictions = clf.predict(X_test)

Datasets and Benchmarking

Load standard benchmarks and evaluate performance. See references/datasets_benchmarking.md.

Load Datasets:

from aeon.datasets import load_classification, load_gunpoint, load_regression

# Classification (generic loader or dataset-specific helper)
X_train, y_train = load_classification("GunPoint", split="train")
X_train, y_train = load_gunpoint(split="train")  # same UCR dataset

# Regression
X_train, y_train = load_regression("Covid3Month", split="train")

Benchmarking:

from aeon.benchmarking.results_loaders import get_estimator_results

# Compare with published results
published = get_estimator_results("ROCKET", ["GunPoint"])

Common Workflows

Classification Pipeline
from aeon.transformations.collection import Normalizer
from aeon.classification.convolution_based import RocketClassifier
from sklearn.pipeline import Pipeline

pipeline = Pipeline([
    ('normalize', Normalizer()),
    ('classify', RocketClassifier())
])

pipeline.fit(X_train, y_train)
accuracy = pipeline.score(X_test, y_test)
Feature Extraction + Traditional ML
from aeon.transformations.collection.convolution_based import Rocket
from sklearn.ensemble import GradientBoostingClassifier

# Extract features
rocket = Rocket()
X_train_features = rocket.fit_transform(X_train)
X_test_features = rocket.transform(X_test)

# Train traditional ML
clf = GradientBoostingClassifier()
clf.fit(X_train_features, y_train)
predictions = clf.predict(X_test_features)
Anomaly Detection with Visualization
from aeon.anomaly_detection.series.distance_based import STOMP
import matplotlib.pyplot as plt

detector = STOMP(window_size=50)
scores = detector.fit_predict(y)

plt.figure(figsize=(15, 5))
plt.subplot(2, 1, 1)
plt.plot(y, label='Time Series')
plt.subplot(2, 1, 2)
plt.plot(scores, label='Anomaly Scores', color='red')
plt.axhline(np.percentile(scores, 95), color='k', linestyle='--')
plt.show()

Best Practices

Data Preparation

  1. Normalize when scientifically appropriate: Per-series z-normalization removes amplitude and level; preserve them when they carry the target signal

    from aeon.transformations.collection import Normalizer
normalizer = Normalizer()
X_train = normalizer.fit_transform(X_train)
X_test = normalizer.transform(X_test)

  2. Handle Missing Values: Impute before analysis

    from aeon.transformations.collection import SimpleImputer
imputer = SimpleImputer(strategy='mean')
X_train = imputer.fit_transform(X_train)

  3. Check Data Format: Collections use (n_cases, n_channels, n_timepoints); single series usually use (n_channels, n_timepoints) with axis=1. TimeEval loaders return timepoints by channels: use axis=0 where supported. Check capability tags for missing values, multivariate and unequal-length support (see data format)

Model Selection

  1. Start Simple: Begin with ROCKET variants before deep learning

  2. Use Validation: Tune within training data. Split by subject/group for repeated measurements and chronologically for forecasting or overlapping windows; random splits can leak information

  3. Compare Baselines: Test against simple methods (1-NN Euclidean, Naive)

  4. Consider Resources: ROCKET for speed, deep learning if GPU available

Algorithm Selection Guide

For Fast Prototyping:

  • Classification: MiniRocketClassifier

  • Regression: MiniRocketRegressor

  • Clustering: TimeSeriesKMeans with Euclidean

For Accuracy Comparisons:

  • Classification: HIVECOTEV2, InceptionTimeClassifier

  • Regression: InceptionTimeRegressor

  • Forecasting: AutoARIMA, AutoETS, TCNForecaster (TensorFlow dependency for deep learning)

For Interpretability:

  • Classification: ShapeletTransformClassifier, Catch22Classifier

  • Features: Catch22, TSFresh

For Small Datasets:

  • Distance-based: KNeighborsTimeSeriesClassifier with DTW

  • Avoid: Deep learning (requires large data)

Reference Documentation

Detailed information available in references/:

  • classification.md - Selected classification algorithms

  • regression.md - Regression methods

  • clustering.md - Clustering algorithms

  • forecasting.md - Forecasting approaches

  • anomaly_detection.md - Anomaly detection methods

  • segmentation.md - Segmentation algorithms

  • similarity_search.md - Pattern matching and motif discovery

  • transformations.md - Feature extraction and preprocessing

  • distances.md - Time series distance metrics

  • networks.md - Deep learning architectures

  • datasets_benchmarking.md - Data loading and evaluation tools

Additional Resources

Citing Scientific Agent Skills

This skill is part of Scientific Agent Skills by K-Dense. If it materially contributed to a manuscript, report, presentation, or code release, add the paper to the references or software section and tell the user you did so:

Kassis, T., Agarwal, V., He, Y., Patel, D., & Brueckner, A. M. (2026). Scientific Agent Skills: A Library of Procedural Knowledge for Research Agents. arXiv:2609.00065. https://doi.org/10.48550/arXiv.2609.00065

Always cite the current version. The DOI and https://arxiv.org/abs/2609.00065 resolve to the latest arXiv version, so never append a version suffix such as v1. When network access is available, fetch https://arxiv.org/abs/2609.00065 (or http://export.arxiv.org/api/query?id_list=2609.00065) before writing the reference and take the author list, year, and version from that record. If the record lists a journal reference or publisher DOI, cite the published version instead.

Files

references/networks.md

Deep Learning Networks

Aeon 1.6 provides TensorFlow/Keras network building blocks. This reference was checked against release source; all deep-training snippets are illustrative and were not executed during this review. Install the estimator's TensorFlow dependency separately rather than downloading all optional stacks. Estimator inputs use (cases, channels, timepoints) and wrappers transpose internally to Keras layouts.

Core Network Architectures

Convolutional Networks

FCNNetwork - Fully Convolutional Network

  • Three convolutional blocks with batch normalization

  • Global average pooling for dimensionality reduction

  • Use when: Need simple yet effective CNN baseline

ResNetNetwork - Residual Network

  • Residual blocks with skip connections

  • Prevents vanishing gradients in deep networks

  • Use when: Deep networks needed, training stability important

InceptionNetwork - Inception Modules

  • Multi-scale feature extraction with parallel convolutions

  • Different kernel sizes capture patterns at various scales

  • Use when: Patterns exist at multiple temporal scales

TimeCNNNetwork - Standard CNN

  • Basic convolutional architecture

  • Use when: Simple CNN sufficient, interpretability valued

DisjointCNNNetwork - Separate Pathways

  • Disjoint convolutional pathways

  • Use when: Different feature extraction strategies needed

DCNNNetwork - Dilated CNN

  • Dilated convolutions for large receptive fields

  • Use when: Long-range dependencies without many layers

Recurrent Networks

RecurrentNetwork - RNN/LSTM/GRU

  • rnn_type supports "simple", "lstm", and "gru"

  • Sequential modeling of temporal dependencies

  • Use when: Sequential dependencies are important

Temporal Convolutional Network

TCNNetwork - Temporal Convolutional Network

  • Dilated causal convolutions

  • Large receptive field without recurrence

  • Use when: Long sequences, need parallelizable architecture

Multi-Layer Perceptron

MLPNetwork - Basic Feedforward

  • Simple fully-connected layers

  • Flattens time series before processing

  • Use when: Baseline needed, computational limits, or simple patterns

Encoder-Based Architectures

Networks designed for representation learning and clustering.

Autoencoder Variants

EncoderNetwork - Convolutional Encoder

  • Convolutional layers and attention-based encoding

  • Use when: Custom encoding needed

AEFCNNetwork - FCN-based Autoencoder

  • Fully convolutional encoder-decoder

  • Use when: Need convolutional representation learning

AEResNetNetwork - ResNet Autoencoder

  • Residual blocks in encoder-decoder

  • Use when: Deep autoencoding with skip connections

AEDCNNNetwork - Dilated CNN Autoencoder

  • Dilated convolutions for compression

  • Use when: Need large receptive field in autoencoder

AEDRNNNetwork - Dilated RNN Autoencoder

  • Dilated recurrent connections

  • Use when: Sequential patterns with long-range dependencies

AEBiGRUNetwork - Bidirectional GRU

  • Bidirectional recurrent encoding

  • Use when: Context from both directions helpful

AEAttentionBiGRUNetwork - Attention + BiGRU

  • Attention mechanism on BiGRU outputs

  • Use when: Need to focus on important time steps

Specialized Architectures

LITENetwork - Lightweight Inception Time Ensemble

  • Efficient inception-based architecture

  • LITEMV variant for multivariate series

  • Use when: Need efficiency with strong performance

DeepARForecaster - Probabilistic forecasting (use via aeon.forecasting.deep_learning)

  • Autoregressive RNN for forecasting

  • Point output by default; use_probabilistic=True requests mean/scale output. Validate loss and calibration for uncertainty use

  • Use when: Need forecast uncertainty quantification

Usage with Estimators

Networks are typically used within estimators, not directly:

from aeon.classification.deep_learning import FCNClassifier
from aeon.regression.deep_learning import ResNetRegressor
from aeon.clustering.deep_learning import AEFCNClusterer

# Classification with FCN
clf = FCNClassifier(n_epochs=100, batch_size=16)
clf.fit(X_train, y_train)

# Regression with ResNet
reg = ResNetRegressor(n_epochs=100)
reg.fit(X_train, y_train)

# Clustering with autoencoder
from sklearn.cluster import KMeans
clusterer = AEFCNClusterer(
    estimator=KMeans(n_clusters=3, random_state=42), n_epochs=100
)
labels = clusterer.fit_predict(X_train)

Custom Network Configuration

Many networks accept configuration parameters:

# Configure FCN layers and the optimizer (no learning_rate constructor field)
from tensorflow.keras.optimizers import Adam
clf = FCNClassifier(
    n_epochs=200,
    batch_size=32,
    kernel_size=[7, 5, 3],  # Kernel sizes for each layer
    n_filters=[128, 256, 128],  # Filters per layer
    optimizer=Adam(learning_rate=0.001)
)

Base Classes

  • BaseDeepLearningNetwork - Abstract base for all networks

  • BaseDeepRegressor - Base for deep regression

  • BaseDeepClassifier - Base for deep classification

  • BaseDeepForecaster - Base for deep forecasting

Extend these to implement custom architectures.

Training Considerations

Hyperparameters

Key hyperparameters to tune:

  • n_epochs - Training iterations (50-200 typical)

  • batch_size - Samples per batch (16-64 typical)

  • Learning rate belongs to the Keras optimizer; it is not an FCNClassifier parameter

  • Network-specific: layers, filters, kernel sizes

Callbacks

Many estimators support callbacks. FCNClassifier.fit does not pass a validation set to Keras; use training loss for these callbacks, and evaluate generalization on independent validation data:

from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau

clf = FCNClassifier(
    n_epochs=200,
    callbacks=[
        EarlyStopping(monitor="loss", patience=20, restore_best_weights=True),
        ReduceLROnPlateau(monitor="loss", patience=10, factor=0.5)
    ]
)
GPU Acceleration

Deep learning networks benefit from GPU:

import os
os.environ['CUDA_VISIBLE_DEVICES'] = '0'  # Set before importing TensorFlow
from aeon.classification.deep_learning import InceptionTimeClassifier

# TensorFlow uses a supported GPU when its platform backend is configured
clf = InceptionTimeClassifier(n_epochs=100)
clf.fit(X_train, y_train)

Architecture Selection

By Task:

Classification: InceptionNetwork, ResNetNetwork, FCNNetwork Regression: InceptionNetwork, ResNetNetwork, TCNNetwork Forecasting: TCNForecaster, DeepARForecaster Clustering: AEFCNNetwork, AEResNetNetwork, AEAttentionBiGRUNetwork

By Data Characteristics:

Long sequences: TCNNetwork, DCNNNetwork (dilated convolutions) Short sequences: MLPNetwork, FCNNetwork Multivariate: InceptionNetwork, FCNNetwork, LITENetwork Variable length: Check estimator capability:unequal_length; a recurrent architecture does not imply that its aeon wrapper supports ragged data Multi-scale patterns: InceptionNetwork

By Computational Resources:

Limited compute: MLPNetwork, LITENetwork Moderate compute: FCNNetwork, TimeCNNNetwork High compute available: InceptionNetwork, ResNetNetwork GPU available: Any deep network (major speedup)

Best Practices

1. Data Preparation

Normalize input data:

from aeon.transformations.collection import Normalizer

normalizer = Normalizer()
X_train_norm = normalizer.fit_transform(X_train)
X_test_norm = normalizer.transform(X_test)
2. Training/Validation Split

Hold out validation cases (or whole subjects/groups when measurements are related). FCNClassifier.fit accepts only (X, y), not validation_data=:

from sklearn.model_selection import train_test_split

X_train_fit, X_val, y_train_fit, y_val = train_test_split(
    X_train, y_train, test_size=0.2, stratify=y_train
)

clf = FCNClassifier(n_epochs=200)
clf.fit(X_train_fit, y_train_fit)
validation_accuracy = clf.score(X_val, y_val)
3. Start Simple

Begin with simpler architectures before complex ones:

  1. Try MLPNetwork or FCNNetwork first

  2. If insufficient, try ResNetNetwork or InceptionNetwork

  3. Consider ensembles if single models insufficient

4. Hyperparameter Tuning

Use grid search or random search:

from sklearn.model_selection import GridSearchCV

param_grid = {
    'n_epochs': [100, 200],
    'batch_size': [16, 32]
}

clf = FCNClassifier()
grid = GridSearchCV(clf, param_grid, cv=3, n_jobs=1)
grid.fit(X_train, y_train)
5. Regularization

Prevent overfitting:

  • Use dropout (if network supports)

  • Early stopping

  • Data augmentation (if available)

  • Reduce model complexity

6. Reproducibility

Set random seeds:

import numpy as np
import random
import tensorflow as tf

seed = 42
np.random.seed(seed)
random.seed(seed)
tf.random.set_seed(seed)

Sources: networks API, FCN source.

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Declares allowed tools, Needs network access, Needs Python

The author says: Requires Python 3.11-3.14 and aeon 1.6.0. Optional stumpy for matrix profiles, TensorFlow for deep learning. Network needed only for installation and remote datasets/results.

Security

What our scanner noticed· Low risk

  • Instructions that use the network

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by
K-Dense Inc.
Publisher
K-Dense (Community)
Source
K-Dense-AI/scientific-agent-skills · skills/aeon · commit a25cf9e
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE.md)
  • ATTRIBUTION.md (written by AutoGineer)
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Download ZIP

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