[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6AF9YGRX68JBQDEDY3XZHNJ":3,"skill-same:01M6AF9YGRX68JBQDEDY3XZHNJ":121},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":23,"publishedAt":46,"linkOnly":47,"markdownContent":48,"markdownBody":49,"frontMatter":50,"refName":51,"commitSha":52,"skillDirectory":53,"forksCount":54,"repositoryHtmlUrl":55,"repositoryHomepageUrl":56,"ownerHtmlUrl":57,"ownerBlogUrl":58,"ownerDescription":59,"updatedAt":60,"install":61,"resources":74},"01M6AF9YGRX68JBQDEDY3XZHNJ","aeon",null,"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.","davila7\u002Fclaude-code-templates","davila7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6216945?v=4","01M6ADEWWFPS191NFM0Y1EX1JH","Daniel Avila","01M6ADJHGM78PHQRD3XZTFDVKK",33481,"MIT","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Faeon\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[5,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45],"when","machine learning","anomaly","for","time series analysis","scikit-learn","algorithms","time","patterns","specialized","regression","analysis","segmentation","detection","clustering","Similarity Search","data","time series","search","learn","tasks","ml","2026-11-10 10:12:30",false,"---\nname: aeon\ndescription: 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.\n---\n\n# Aeon Time Series Machine Learning\n\n## Overview\n\nAeon is a scikit-learn compatible Python toolkit for time series machine learning. It provides state-of-the-art algorithms for classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search.\n\n## When to Use This Skill\n\nApply this skill when:\n- Classifying or predicting from time series data\n- Detecting anomalies or change points in temporal sequences\n- Clustering similar time series patterns\n- Forecasting future values\n- Finding repeated patterns (motifs) or unusual subsequences (discords)\n- Comparing time series with specialized distance metrics\n- Extracting features from temporal data\n\n## Installation\n\n```bash\nuv pip install aeon\n```\n\n## Core Capabilities\n\n### 1. Time Series Classification\n\nCategorize time series into predefined classes. See `references\u002Fclassification.md` for complete algorithm catalog.\n\n**Quick Start:**\n```python\nfrom aeon.classification.convolution_based import RocketClassifier\nfrom aeon.datasets import load_classification\n\n# Load data\nX_train, y_train = load_classification(\"GunPoint\", split=\"train\")\nX_test, y_test = load_classification(\"GunPoint\", split=\"test\")\n\n# Train classifier\nclf = RocketClassifier(n_kernels=10000)\nclf.fit(X_train, y_train)\naccuracy = clf.score(X_test, y_test)\n```\n\n**Algorithm Selection:**\n- **Speed + Performance**: `MiniRocketClassifier`, `Arsenal`\n- **Maximum Accuracy**: `HIVECOTEV2`, `InceptionTimeClassifier`\n- **Interpretability**: `ShapeletTransformClassifier`, `Catch22Classifier`\n- **Small Datasets**: `KNeighborsTimeSeriesClassifier` with DTW distance\n\n### 2. Time Series Regression\n\nPredict continuous values from time series. See `references\u002Fregression.md` for algorithms.\n\n**Quick Start:**\n```python\nfrom aeon.regression.convolution_based import RocketRegressor\nfrom aeon.datasets import load_regression\n\nX_train, y_train = load_regression(\"Covid3Month\", split=\"train\")\nX_test, y_test = load_regression(\"Covid3Month\", split=\"test\")\n\nreg = RocketRegressor()\nreg.fit(X_train, y_train)\npredictions = reg.predict(X_test)\n```\n\n### 3. Time Series Clustering\n\nGroup similar time series without labels. See `references\u002Fclustering.md` for methods.\n\n**Quick Start:**\n```python\nfrom aeon.clustering import TimeSeriesKMeans\n\nclusterer = TimeSeriesKMeans(\n    n_clusters=3,\n    distance=\"dtw\",\n    averaging_method=\"ba\"\n)\nlabels = clusterer.fit_predict(X_train)\ncenters = clusterer.cluster_centers_\n```\n\n### 4. Forecasting\n\nPredict future time series values. See `references\u002Fforecasting.md` for forecasters.\n\n**Quick Start:**\n```python\nfrom aeon.forecasting.arima import ARIMA\n\nforecaster = ARIMA(order=(1, 1, 1))\nforecaster.fit(y_train)\ny_pred = forecaster.predict(fh=[1, 2, 3, 4, 5])\n```\n\n### 5. Anomaly Detection\n\nIdentify unusual patterns or outliers. See `references\u002Fanomaly_detection.md` for detectors.\n\n**Quick Start:**\n```python\nfrom aeon.anomaly_detection import STOMP\n\ndetector = STOMP(window_size=50)\nanomaly_scores = detector.fit_predict(y)\n\n# Higher scores indicate anomalies\nthreshold = np.percentile(anomaly_scores, 95)\nanomalies = anomaly_scores > threshold\n```\n\n### 6. Segmentation\n\nPartition time series into regions with change points. See `references\u002Fsegmentation.md`.\n\n**Quick Start:**\n```python\nfrom aeon.segmentation import ClaSPSegmenter\n\nsegmenter = ClaSPSegmenter()\nchange_points = segmenter.fit_predict(y)\n```\n\n### 7. Similarity Search\n\nFind similar patterns within or across time series. See `references\u002Fsimilarity_search.md`.\n\n**Quick Start:**\n```python\nfrom aeon.similarity_search import StompMotif\n\n# Find recurring patterns\nmotif_finder = StompMotif(window_size=50, k=3)\nmotifs = motif_finder.fit_predict(y)\n```\n\n## Feature Extraction and Transformations\n\nTransform time series for feature engineering. See `references\u002Ftransformations.md`.\n\n**ROCKET Features:**\n```python\nfrom aeon.transformations.collection.convolution_based import RocketTransformer\n\nrocket = RocketTransformer()\nX_features = rocket.fit_transform(X_train)\n\n# Use features with any sklearn classifier\nfrom sklearn.ensemble import RandomForestClassifier\nclf = RandomForestClassifier()\nclf.fit(X_features, y_train)\n```\n\n**Statistical Features:**\n```python\nfrom aeon.transformations.collection.feature_based import Catch22\n\ncatch22 = Catch22()\nX_features = catch22.fit_transform(X_train)\n```\n\n**Preprocessing:**\n```python\nfrom aeon.transformations.collection import MinMaxScaler, Normalizer\n\nscaler = Normalizer()  # Z-normalization\nX_normalized = scaler.fit_transform(X_train)\n```\n\n## Distance Metrics\n\nSpecialized temporal distance measures. See `references\u002Fdistances.md` for complete catalog.\n\n**Usage:**\n```python\nfrom aeon.distances import dtw_distance, dtw_pairwise_distance\n\n# Single distance\ndistance = dtw_distance(x, y, window=0.1)\n\n# Pairwise distances\ndistance_matrix = dtw_pairwise_distance(X_train)\n\n# Use with classifiers\nfrom aeon.classification.distance_based import KNeighborsTimeSeriesClassifier\n\nclf = KNeighborsTimeSeriesClassifier(\n    n_neighbors=5,\n    distance=\"dtw\",\n    distance_params={\"window\": 0.2}\n)\n```\n\n**Available Distances:**\n- **Elastic**: DTW, DDTW, WDTW, ERP, EDR, LCSS, TWE, MSM\n- **Lock-step**: Euclidean, Manhattan, Minkowski\n- **Shape-based**: Shape DTW, SBD\n\n## Deep Learning Networks\n\nNeural architectures for time series. See `references\u002Fnetworks.md`.\n\n**Architectures:**\n- Convolutional: `FCNClassifier`, `ResNetClassifier`, `InceptionTimeClassifier`\n- Recurrent: `RecurrentNetwork`, `TCNNetwork`\n- Autoencoders: `AEFCNClusterer`, `AEResNetClusterer`\n\n**Usage:**\n```python\nfrom aeon.classification.deep_learning import InceptionTimeClassifier\n\nclf = InceptionTimeClassifier(n_epochs=100, batch_size=32)\nclf.fit(X_train, y_train)\npredictions = clf.predict(X_test)\n```\n\n## Datasets and Benchmarking\n\nLoad standard benchmarks and evaluate performance. See `references\u002Fdatasets_benchmarking.md`.\n\n**Load Datasets:**\n```python\nfrom aeon.datasets import load_classification, load_regression\n\n# Classification\nX_train, y_train = load_classification(\"ArrowHead\", split=\"train\")\n\n# Regression\nX_train, y_train = load_regression(\"Covid3Month\", split=\"train\")\n```\n\n**Benchmarking:**\n```python\nfrom aeon.benchmarking import get_estimator_results\n\n# Compare with published results\npublished = get_estimator_results(\"ROCKET\", \"GunPoint\")\n```\n\n## Common Workflows\n\n### Classification Pipeline\n\n```python\nfrom aeon.transformations.collection import Normalizer\nfrom aeon.classification.convolution_based import RocketClassifier\nfrom sklearn.pipeline import Pipeline\n\npipeline = Pipeline([\n    ('normalize', Normalizer()),\n    ('classify', RocketClassifier())\n])\n\npipeline.fit(X_train, y_train)\naccuracy = pipeline.score(X_test, y_test)\n```\n\n### Feature Extraction + Traditional ML\n\n```python\nfrom aeon.transformations.collection import RocketTransformer\nfrom sklearn.ensemble import GradientBoostingClassifier\n\n# Extract features\nrocket = RocketTransformer()\nX_train_features = rocket.fit_transform(X_train)\nX_test_features = rocket.transform(X_test)\n\n# Train traditional ML\nclf = GradientBoostingClassifier()\nclf.fit(X_train_features, y_train)\npredictions = clf.predict(X_test_features)\n```\n\n### Anomaly Detection with Visualization\n\n```python\nfrom aeon.anomaly_detection import STOMP\nimport matplotlib.pyplot as plt\n\ndetector = STOMP(window_size=50)\nscores = detector.fit_predict(y)\n\nplt.figure(figsize=(15, 5))\nplt.subplot(2, 1, 1)\nplt.plot(y, label='Time Series')\nplt.subplot(2, 1, 2)\nplt.plot(scores, label='Anomaly Scores', color='red')\nplt.axhline(np.percentile(scores, 95), color='k', linestyle='--')\nplt.show()\n```\n\n## Best Practices\n\n### Data Preparation\n\n1. **Normalize**: Most algorithms benefit from z-normalization\n   ```python\n   from aeon.transformations.collection import Normalizer\n   normalizer = Normalizer()\n   X_train = normalizer.fit_transform(X_train)\n   X_test = normalizer.transform(X_test)\n   ```\n\n2. **Handle Missing Values**: Impute before analysis\n   ```python\n   from aeon.transformations.collection import SimpleImputer\n   imputer = SimpleImputer(strategy='mean')\n   X_train = imputer.fit_transform(X_train)\n   ```\n\n3. **Check Data Format**: Aeon expects shape `(n_samples, n_channels, n_timepoints)`\n\n### Model Selection\n\n1. **Start Simple**: Begin with ROCKET variants before deep learning\n2. **Use Validation**: Split training data for hyperparameter tuning\n3. **Compare Baselines**: Test against simple methods (1-NN Euclidean, Naive)\n4. **Consider Resources**: ROCKET for speed, deep learning if GPU available\n\n### Algorithm Selection Guide\n\n**For Fast Prototyping:**\n- Classification: `MiniRocketClassifier`\n- Regression: `MiniRocketRegressor`\n- Clustering: `TimeSeriesKMeans` with Euclidean\n\n**For Maximum Accuracy:**\n- Classification: `HIVECOTEV2`, `InceptionTimeClassifier`\n- Regression: `InceptionTimeRegressor`\n- Forecasting: `ARIMA`, `TCNForecaster`\n\n**For Interpretability:**\n- Classification: `ShapeletTransformClassifier`, `Catch22Classifier`\n- Features: `Catch22`, `TSFresh`\n\n**For Small Datasets:**\n- Distance-based: `KNeighborsTimeSeriesClassifier` with DTW\n- Avoid: Deep learning (requires large data)\n\n## Reference Documentation\n\nDetailed information available in `references\u002F`:\n- `classification.md` - All classification algorithms\n- `regression.md` - Regression methods\n- `clustering.md` - Clustering algorithms\n- `forecasting.md` - Forecasting approaches\n- `anomaly_detection.md` - Anomaly detection methods\n- `segmentation.md` - Segmentation algorithms\n- `similarity_search.md` - Pattern matching and motif discovery\n- `transformations.md` - Feature extraction and preprocessing\n- `distances.md` - Time series distance metrics\n- `networks.md` - Deep learning architectures\n- `datasets_benchmarking.md` - Data loading and evaluation tools\n\n## Additional Resources\n\n- Documentation: https:\u002F\u002Fwww.aeon-toolkit.org\u002F\n- GitHub: https:\u002F\u002Fgithub.com\u002Faeon-toolkit\u002Faeon\n- Examples: https:\u002F\u002Fwww.aeon-toolkit.org\u002Fen\u002Fstable\u002Fexamples.html\n- API Reference: https:\u002F\u002Fwww.aeon-toolkit.org\u002Fen\u002Fstable\u002Fapi_reference.html\n","# Aeon Time Series Machine Learning\n\n## Overview\n\nAeon is a scikit-learn compatible Python toolkit for time series machine learning. It provides state-of-the-art algorithms for classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search.\n\n## When to Use This Skill\n\nApply this skill when:\n- Classifying or predicting from time series data\n- Detecting anomalies or change points in temporal sequences\n- Clustering similar time series patterns\n- Forecasting future values\n- Finding repeated patterns (motifs) or unusual subsequences (discords)\n- Comparing time series with specialized distance metrics\n- Extracting features from temporal data\n\n## Installation\n\n```bash\nuv pip install aeon\n```\n\n## Core Capabilities\n\n### 1. Time Series Classification\n\nCategorize time series into predefined classes. See `references\u002Fclassification.md` for complete algorithm catalog.\n\n**Quick Start:**\n```python\nfrom aeon.classification.convolution_based import RocketClassifier\nfrom aeon.datasets import load_classification\n\n# Load data\nX_train, y_train = load_classification(\"GunPoint\", split=\"train\")\nX_test, y_test = load_classification(\"GunPoint\", split=\"test\")\n\n# Train classifier\nclf = RocketClassifier(n_kernels=10000)\nclf.fit(X_train, y_train)\naccuracy = clf.score(X_test, y_test)\n```\n\n**Algorithm Selection:**\n- **Speed + Performance**: `MiniRocketClassifier`, `Arsenal`\n- **Maximum Accuracy**: `HIVECOTEV2`, `InceptionTimeClassifier`\n- **Interpretability**: `ShapeletTransformClassifier`, `Catch22Classifier`\n- **Small Datasets**: `KNeighborsTimeSeriesClassifier` with DTW distance\n\n### 2. Time Series Regression\n\nPredict continuous values from time series. See `references\u002Fregression.md` for algorithms.\n\n**Quick Start:**\n```python\nfrom aeon.regression.convolution_based import RocketRegressor\nfrom aeon.datasets import load_regression\n\nX_train, y_train = load_regression(\"Covid3Month\", split=\"train\")\nX_test, y_test = load_regression(\"Covid3Month\", split=\"test\")\n\nreg = RocketRegressor()\nreg.fit(X_train, y_train)\npredictions = reg.predict(X_test)\n```\n\n### 3. Time Series Clustering\n\nGroup similar time series without labels. See `references\u002Fclustering.md` for methods.\n\n**Quick Start:**\n```python\nfrom aeon.clustering import TimeSeriesKMeans\n\nclusterer = TimeSeriesKMeans(\n    n_clusters=3,\n    distance=\"dtw\",\n    averaging_method=\"ba\"\n)\nlabels = clusterer.fit_predict(X_train)\ncenters = clusterer.cluster_centers_\n```\n\n### 4. Forecasting\n\nPredict future time series values. See `references\u002Fforecasting.md` for forecasters.\n\n**Quick Start:**\n```python\nfrom aeon.forecasting.arima import ARIMA\n\nforecaster = ARIMA(order=(1, 1, 1))\nforecaster.fit(y_train)\ny_pred = forecaster.predict(fh=[1, 2, 3, 4, 5])\n```\n\n### 5. Anomaly Detection\n\nIdentify unusual patterns or outliers. See `references\u002Fanomaly_detection.md` for detectors.\n\n**Quick Start:**\n```python\nfrom aeon.anomaly_detection import STOMP\n\ndetector = STOMP(window_size=50)\nanomaly_scores = detector.fit_predict(y)\n\n# Higher scores indicate anomalies\nthreshold = np.percentile(anomaly_scores, 95)\nanomalies = anomaly_scores > threshold\n```\n\n### 6. Segmentation\n\nPartition time series into regions with change points. See `references\u002Fsegmentation.md`.\n\n**Quick Start:**\n```python\nfrom aeon.segmentation import ClaSPSegmenter\n\nsegmenter = ClaSPSegmenter()\nchange_points = segmenter.fit_predict(y)\n```\n\n### 7. Similarity Search\n\nFind similar patterns within or across time series. See `references\u002Fsimilarity_search.md`.\n\n**Quick Start:**\n```python\nfrom aeon.similarity_search import StompMotif\n\n# Find recurring patterns\nmotif_finder = StompMotif(window_size=50, k=3)\nmotifs = motif_finder.fit_predict(y)\n```\n\n## Feature Extraction and Transformations\n\nTransform time series for feature engineering. See `references\u002Ftransformations.md`.\n\n**ROCKET Features:**\n```python\nfrom aeon.transformations.collection.convolution_based import RocketTransformer\n\nrocket = RocketTransformer()\nX_features = rocket.fit_transform(X_train)\n\n# Use features with any sklearn classifier\nfrom sklearn.ensemble import RandomForestClassifier\nclf = RandomForestClassifier()\nclf.fit(X_features, y_train)\n```\n\n**Statistical Features:**\n```python\nfrom aeon.transformations.collection.feature_based import Catch22\n\ncatch22 = Catch22()\nX_features = catch22.fit_transform(X_train)\n```\n\n**Preprocessing:**\n```python\nfrom aeon.transformations.collection import MinMaxScaler, Normalizer\n\nscaler = Normalizer()  # Z-normalization\nX_normalized = scaler.fit_transform(X_train)\n```\n\n## Distance Metrics\n\nSpecialized temporal distance measures. See `references\u002Fdistances.md` for complete catalog.\n\n**Usage:**\n```python\nfrom aeon.distances import dtw_distance, dtw_pairwise_distance\n\n# Single distance\ndistance = dtw_distance(x, y, window=0.1)\n\n# Pairwise distances\ndistance_matrix = dtw_pairwise_distance(X_train)\n\n# Use with classifiers\nfrom aeon.classification.distance_based import KNeighborsTimeSeriesClassifier\n\nclf = KNeighborsTimeSeriesClassifier(\n    n_neighbors=5,\n    distance=\"dtw\",\n    distance_params={\"window\": 0.2}\n)\n```\n\n**Available Distances:**\n- **Elastic**: DTW, DDTW, WDTW, ERP, EDR, LCSS, TWE, MSM\n- **Lock-step**: Euclidean, Manhattan, Minkowski\n- **Shape-based**: Shape DTW, SBD\n\n## Deep Learning Networks\n\nNeural architectures for time series. See `references\u002Fnetworks.md`.\n\n**Architectures:**\n- Convolutional: `FCNClassifier`, `ResNetClassifier`, `InceptionTimeClassifier`\n- Recurrent: `RecurrentNetwork`, `TCNNetwork`\n- Autoencoders: `AEFCNClusterer`, `AEResNetClusterer`\n\n**Usage:**\n```python\nfrom aeon.classification.deep_learning import InceptionTimeClassifier\n\nclf = InceptionTimeClassifier(n_epochs=100, batch_size=32)\nclf.fit(X_train, y_train)\npredictions = clf.predict(X_test)\n```\n\n## Datasets and Benchmarking\n\nLoad standard benchmarks and evaluate performance. See `references\u002Fdatasets_benchmarking.md`.\n\n**Load Datasets:**\n```python\nfrom aeon.datasets import load_classification, load_regression\n\n# Classification\nX_train, y_train = load_classification(\"ArrowHead\", split=\"train\")\n\n# Regression\nX_train, y_train = load_regression(\"Covid3Month\", split=\"train\")\n```\n\n**Benchmarking:**\n```python\nfrom aeon.benchmarking import get_estimator_results\n\n# Compare with published results\npublished = get_estimator_results(\"ROCKET\", \"GunPoint\")\n```\n\n## Common Workflows\n\n### Classification Pipeline\n\n```python\nfrom aeon.transformations.collection import Normalizer\nfrom aeon.classification.convolution_based import RocketClassifier\nfrom sklearn.pipeline import Pipeline\n\npipeline = Pipeline([\n    ('normalize', Normalizer()),\n    ('classify', RocketClassifier())\n])\n\npipeline.fit(X_train, y_train)\naccuracy = pipeline.score(X_test, y_test)\n```\n\n### Feature Extraction + Traditional ML\n\n```python\nfrom aeon.transformations.collection import RocketTransformer\nfrom sklearn.ensemble import GradientBoostingClassifier\n\n# Extract features\nrocket = RocketTransformer()\nX_train_features = rocket.fit_transform(X_train)\nX_test_features = rocket.transform(X_test)\n\n# Train traditional ML\nclf = GradientBoostingClassifier()\nclf.fit(X_train_features, y_train)\npredictions = clf.predict(X_test_features)\n```\n\n### Anomaly Detection with Visualization\n\n```python\nfrom aeon.anomaly_detection import STOMP\nimport matplotlib.pyplot as plt\n\ndetector = STOMP(window_size=50)\nscores = detector.fit_predict(y)\n\nplt.figure(figsize=(15, 5))\nplt.subplot(2, 1, 1)\nplt.plot(y, label='Time Series')\nplt.subplot(2, 1, 2)\nplt.plot(scores, label='Anomaly Scores', color='red')\nplt.axhline(np.percentile(scores, 95), color='k', linestyle='--')\nplt.show()\n```\n\n## Best Practices\n\n### Data Preparation\n\n1. **Normalize**: Most algorithms benefit from z-normalization\n   ```python\n   from aeon.transformations.collection import Normalizer\n   normalizer = Normalizer()\n   X_train = normalizer.fit_transform(X_train)\n   X_test = normalizer.transform(X_test)\n   ```\n\n2. **Handle Missing Values**: Impute before analysis\n   ```python\n   from aeon.transformations.collection import SimpleImputer\n   imputer = SimpleImputer(strategy='mean')\n   X_train = imputer.fit_transform(X_train)\n   ```\n\n3. **Check Data Format**: Aeon expects shape `(n_samples, n_channels, n_timepoints)`\n\n### Model Selection\n\n1. **Start Simple**: Begin with ROCKET variants before deep learning\n2. **Use Validation**: Split training data for hyperparameter tuning\n3. **Compare Baselines**: Test against simple methods (1-NN Euclidean, Naive)\n4. **Consider Resources**: ROCKET for speed, deep learning if GPU available\n\n### Algorithm Selection Guide\n\n**For Fast Prototyping:**\n- Classification: `MiniRocketClassifier`\n- Regression: `MiniRocketRegressor`\n- Clustering: `TimeSeriesKMeans` with Euclidean\n\n**For Maximum Accuracy:**\n- Classification: `HIVECOTEV2`, `InceptionTimeClassifier`\n- Regression: `InceptionTimeRegressor`\n- Forecasting: `ARIMA`, `TCNForecaster`\n\n**For Interpretability:**\n- Classification: `ShapeletTransformClassifier`, `Catch22Classifier`\n- Features: `Catch22`, `TSFresh`\n\n**For Small Datasets:**\n- Distance-based: `KNeighborsTimeSeriesClassifier` with DTW\n- Avoid: Deep learning (requires large data)\n\n## Reference Documentation\n\nDetailed information available in `references\u002F`:\n- `classification.md` - All classification algorithms\n- `regression.md` - Regression methods\n- `clustering.md` - Clustering algorithms\n- `forecasting.md` - Forecasting approaches\n- `anomaly_detection.md` - Anomaly detection methods\n- `segmentation.md` - Segmentation algorithms\n- `similarity_search.md` - Pattern matching and motif discovery\n- `transformations.md` - Feature extraction and preprocessing\n- `distances.md` - Time series distance metrics\n- `networks.md` - Deep learning architectures\n- `datasets_benchmarking.md` - Data loading and evaluation tools\n\n## Additional Resources\n\n- Documentation: https:\u002F\u002Fwww.aeon-toolkit.org\u002F\n- GitHub: https:\u002F\u002Fgithub.com\u002Faeon-toolkit\u002Faeon\n- Examples: https:\u002F\u002Fwww.aeon-toolkit.org\u002Fen\u002Fstable\u002Fexamples.html\n- API Reference: https:\u002F\u002Fwww.aeon-toolkit.org\u002Fen\u002Fstable\u002Fapi_reference.html\n",{"name":5,"description":7},"main","2b749c82b5d11fac39d9a6e021ce3f5b8a541bd1","cli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Faeon",3931,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates","https:\u002F\u002Faitmpl.com","https:\u002F\u002Fgithub.com\u002Fdavila7","https:\u002F\u002Fdanielavila.me","Building AI dev tools with LLMs","2026-11-14 06:14:46",{"source":8,"skillName":5,"commands":62,"prompt":72,"zipAvailable":73},[63,66,69],{"tool":64,"command":65},"npx","npx skills add davila7\u002Fclaude-code-templates --skill aeon",{"tool":67,"command":68},"bunx","bunx skills add davila7\u002Fclaude-code-templates --skill aeon",{"tool":70,"command":71},"pnpm","pnpm dlx skills add davila7\u002Fclaude-code-templates --skill aeon","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Faeon\u002FSKILL.md",true,[75,81,85,89,93,97,101,105,109,113,117],{"path":76,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":79,"sourceHtmlUrl":80},"references\u002Fanomaly_detection.md","REFERENCE","参考文档",4912,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Faeon\u002Freferences\u002Fanomaly_detection.md",{"path":82,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":83,"sourceHtmlUrl":84},"references\u002Fclassification.md",5377,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Faeon\u002Freferences\u002Fclassification.md",{"path":86,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":87,"sourceHtmlUrl":88},"references\u002Fclustering.md",3744,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Faeon\u002Freferences\u002Fclustering.md",{"path":90,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":91,"sourceHtmlUrl":92},"references\u002Fdatasets_benchmarking.md",8871,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Faeon\u002Freferences\u002Fdatasets_benchmarking.md",{"path":94,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":95,"sourceHtmlUrl":96},"references\u002Fdistances.md",6409,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Faeon\u002Freferences\u002Fdistances.md",{"path":98,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":99,"sourceHtmlUrl":100},"references\u002Fforecasting.md",3879,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Faeon\u002Freferences\u002Fforecasting.md",{"path":102,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":103,"sourceHtmlUrl":104},"references\u002Fnetworks.md",7849,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Faeon\u002Freferences\u002Fnetworks.md",{"path":106,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":107,"sourceHtmlUrl":108},"references\u002Fregression.md",3960,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Faeon\u002Freferences\u002Fregression.md",{"path":110,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":111,"sourceHtmlUrl":112},"references\u002Fsegmentation.md",4924,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Faeon\u002Freferences\u002Fsegmentation.md",{"path":114,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":115,"sourceHtmlUrl":116},"references\u002Fsimilarity_search.md",5242,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Faeon\u002Freferences\u002Fsimilarity_search.md",{"path":118,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":119,"sourceHtmlUrl":120},"references\u002Ftransformations.md",7725,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Faeon\u002Freferences\u002Ftransformations.md",[122,148,175,208,243,267],{"skillNo":123,"name":124,"nameZh":6,"description":125,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":126,"categories":127,"keywords":129,"publishedAt":46},"01M70NHBTQ8BV0A2SCAZXEPHCX","wealthville-pool-scoring","Score DeFi liquidity pools before providing liquidity. Returns a 0-100 pool score and an Enter\u002FHold\u002FExit\u002FReduce\u002FAvoid verdict with calibrated confidence for ~68,800 Solana pools and 575 EVM pools, plus a public miss-inclusive track record. Use when asked whether a pool is worth LPing into, which pools look best right now, or how accurate the scores have actually been.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fweb-data\u002Fwealthville-pool-scoring\u002FSKILL.md",[128],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[130,131,132,133,134,27,135,136,137,24,138,139,140,141,142,143,144,145,146,147],"scoring","pool","wealthville pool scoring","liquidity","which","exit","now","use","reduce","with","record","track","verdict","evm","solana","how","public","look",{"skillNo":149,"name":150,"nameZh":6,"description":151,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":152,"categories":153,"keywords":155,"publishedAt":46},"01M70MB6VR8ERJ4EM8D5KHBMEV","technical-debt-auditor","Scans a codebase and produces a quantified technical debt report with a prioritized remediation plan. Analyzes dead code, TODO\u002FFIXME\u002FHACK markers, dependency staleness, complexity hotspots, code duplication signals, test coverage gaps, unused exports, and outdated patterns. Use this skill when the user asks to audit their codebase for tech debt, wants a health check of their code quality, says things like \"where should I focus refactoring\", \"find code smells\", \"audit my codebase\", \"what technical debt do we have\", \"prioritize cleanup work\", or asks about code quality metrics. Also trigger when the user mentions \"code rot\", \"legacy cleanup\", \"refactoring priorities\", or wants to understand the overall health of a project before a major effort. Trigger even if the user doesn't say \"technical debt\" explicitly — any request to systematically assess code quality and identify improvement priorities belongs here.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Ftechnical-debt-auditor\u002FSKILL.md",[154],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[156,157,158,159,160,161,162,163,164,165,166,24,167,168,169,170,171,172,173,174],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","code","todo","work","plan","focus","signals","audit","dependency",{"skillNo":176,"name":177,"nameZh":6,"description":178,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":179,"categories":180,"keywords":190,"publishedAt":46},"01M70KPYH9QNHCNME5GJQGE22K","readonly-sql-guard","Make an agent's database access provably read-only. Use when an agent runs caller-supplied SQL against a production\u002FERP\u002Fanalytics database (a \"query\" MCP tool, text-to-SQL, an admin console), or when reviewing DB-access code for write\u002FDDL\u002Fside-effect\u002Ftransaction-escape risks. Checks what a statement CALLS, not just its shape.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdatabase\u002Freadonly-sql-guard\u002FSKILL.md",[181,184,185],{"categoryNo":182,"categoryName":183,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":186,"categoryName":187,"taxonomyType":188,"taxonomyTypeDesc":189},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[191,192,193,194,195,196,197,27,198,199,200,201,202,203,204,137,205,206,24,207],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","MCP","access","database","query",{"skillNo":209,"name":210,"nameZh":6,"description":211,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":212,"licenseFileUrl":213,"sourceHtmlUrl":214,"categories":215,"keywords":219,"publishedAt":46},"01M6AFY6F7CC2BBZP1QD6VTNVC","web-artifacts-builder","Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn\u002Fui). Use for complex artifacts requiring state management, routing, or shadcn\u002Fui components - not for simple single-file HTML\u002FJSX artifacts.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FSKILL.md",[216],{"categoryNo":217,"categoryName":218,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,27,242],"artifacts builder","web","web artifacts builder","artifacts","builder","state","routing","state management","component","css","frontend","ui","Tools","file","components","shadcn","react","Tailwind","suite","jsx","html","single-file","ui components",{"skillNo":244,"name":245,"nameZh":6,"description":246,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":212,"licenseFileUrl":247,"sourceHtmlUrl":248,"categories":249,"keywords":254,"publishedAt":46},"01M6AFWF33SFV9Z3SF4PEEE4BF","webapp-testing","Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FSKILL.md",[250,253],{"categoryNo":251,"categoryName":252,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":217,"categoryName":218,"taxonomyType":21,"taxonomyTypeDesc":22},[255,256,257,258,259,221,260,230,231,261,262,263,264,27,265,139,266],"webapp testing","testing","webapp","browser","playwright","applications","debugging","toolkit","logs","screenshots","local","behavior",{"skillNo":268,"name":269,"nameZh":6,"description":270,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":212,"licenseFileUrl":271,"sourceHtmlUrl":272,"categories":273,"keywords":281,"publishedAt":46},"01M6AF67Q67JJJ5V51JR2P52KA","discernment-nudge","After you give a substantive answer or draft that the user may act on — advice or recommendations, drafted artifacts such as goals, plans, pitches, proposals, or emails, estimates or projections, analysis or interpretation of data, factual claims they may rely on, or a multi-step argument — invoke this skill BEFORE finalizing your reply and then, if it applies, append 2-3 short follow-up questions, each tied to something specific in what you just produced, that help the user check key facts, probe the reasoning or assumptions, and notice missing context. Do this at most once per conversation. Skip it when the user asked a trivial how-to or simple lookup, wants a purely educational explanation, asked you only to format, convert, or assemble a file from content they provided, is writing code they will run, is doing creative writing or casual chat, or already asked you to double-check, cite, or review — the skill file explains these boundaries and the exact output format.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FSKILL.md",[274,277,278],{"categoryNo":275,"categoryName":276,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":279,"categoryName":280,"taxonomyType":188,"taxonomyTypeDesc":189},"01JBSEEDCAT000000000000036","内容创作者",[282,283,284,285,286,287,40,288,223,289,233,167,290,291,292,293,294,203,295,24,296,35,297,298,299,145,300],"discernment nudge","nudge","interpretation","help","run","reasoning","writing","lookup","content","review","conversation","short","up","invoke","you","claims","argument","assemble","recommendations"]