pymc-modeling
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flare-fassets
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Provides domain knowledge and guidance for Flare FAssets—wrapped tokens (FXRP, FBTC, etc.), minting, redemption, agents, collateral, and smart contract integration. Use when working with FAssets,...
ml-pipeline
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Advanced Machine Learning Pipeline skill - Data preprocessing, model selection, training workflows, real-time inference, and MLOps integration. Use when building ML models, analyzing data, or...
Build binary and multiclass classification models using logistic regression, decision trees, and ensemble methods for categorical prediction and classification
Use when writing recommendation letters, reference letters, or award nominations for students, postdocs, or colleagues. Invoke when user mentions recommendation letter, reference, nomination,...
nightscout-cgm
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Analyze CGM blood glucose data from Nightscout. Use this skill when asked about current glucose levels, blood sugar trends, A1C estimates, time-in-range statistics, glucose variability, or...
n8n expression syntax validation, context-aware testing, common pitfalls detection, and performance optimization. Use when validating n8n expressions and data transformations.
typst-writer
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Write correct and idiomatic Typst code for document typesetting. Use when creating or editing Typst (.typ) files, working with Typst markup, or answering questions about Typst syntax and features....
scikit-learn
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Machine learning in Python with scikit-learn. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model...
scikit-learn
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Machine learning in Python with scikit-learn. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model...
pymoo
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Multi-objective optimization framework. NSGA-II, NSGA-III, MOEA/D, Pareto fronts, constraint handling, benchmarks (ZDT, DTLZ), for engineering design and optimization problems.
pymoo
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Multi-objective optimization framework. NSGA-II, NSGA-III, MOEA/D, Pareto fronts, constraint handling, benchmarks (ZDT, DTLZ), for engineering design and optimization problems.
statsmodels
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Statistical modeling toolkit. OLS, GLM, logistic, ARIMA, time series, hypothesis tests, diagnostics, AIC/BIC, for rigorous statistical inference and econometric analysis.
statsmodels
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Statistical models library for Python. Use when you need specific model classes (OLS, GLM, mixed models, ARIMA) with detailed diagnostics, residuals, and inference. Best for econometrics, time...
python_data_stack
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Statistical modeling toolkit. OLS, GLM, logistic, ARIMA, time series, hypothesis tests, diagnostics, AIC/BIC, for rigorous statistical inference and econometric analysis.
python_data_stack
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Statistical modeling toolkit. OLS, GLM, logistic, ARIMA, time series, hypothesis tests, diagnostics, AIC/BIC, for rigorous statistical inference and econometric analysis.
gget
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CLI/Python toolkit for rapid bioinformatics queries. Preferred for quick BLAST searches. Access to 20+ databases: gene info (Ensembl/UniProt), AlphaFold, ARCHS4, Enrichr, OpenTargets, COSMIC,...
gget
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Fast CLI/Python queries to 20+ bioinformatics databases. Use for quick lookups: gene info, BLAST searches, AlphaFold structures, enrichment analysis. Best for interactive exploration, simple...
Use when asked to compare multiple ML models, perform cross-validation, evaluate metrics, or select the best model for a classification/regression task.
Best practices for scikit-learn machine learning, model development, evaluation, and deployment in Python