AI-powered career biographer that conducts empathetic interviews, extracts structured career narratives, and transforms professional stories into portfolios, CVs, and personal brand assets. This...

Build predictive models using linear regression, polynomial regression, and regularized regression for continuous prediction, trend forecasting, and relationship quantification

>

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,...

XGBoost machine learning best practices for training, tuning, and deploying gradient boosted models. Use when writing, reviewing, or implementing XGBoost models for classification, regression, or...

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,...

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.

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....

Machine learning in Python with scikit-learn. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model...

Machine learning in Python with scikit-learn. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model...

Machine learning in Python with scikit-learn. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model...

Multi-objective optimization framework. NSGA-II, NSGA-III, MOEA/D, Pareto fronts, constraint handling, benchmarks (ZDT, DTLZ), for engineering design and optimization problems.

Multi-objective optimization framework. NSGA-II, NSGA-III, MOEA/D, Pareto fronts, constraint handling, benchmarks (ZDT, DTLZ), for engineering design and optimization problems.

Multi-objective optimization framework. NSGA-II, NSGA-III, MOEA/D, Pareto fronts, constraint handling, benchmarks (ZDT, DTLZ), for engineering design and optimization problems.

Statistical modeling toolkit. OLS, GLM, logistic, ARIMA, time series, hypothesis tests, diagnostics, AIC/BIC, for rigorous statistical inference and econometric analysis.

Statistical modeling toolkit. OLS, GLM, logistic, ARIMA, time series, hypothesis tests, diagnostics, AIC/BIC, for rigorous statistical inference and econometric analysis.

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...