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Found 50 Skills
Formal mathematical reasoning for research papers — derive equations, write proofs, formalize problem settings, select statistical tests, and generate LaTeX math notation. Use when the user needs mathematical derivations, theorem proofs, notation tables, or statistical analysis formalization.
Bayesian statistical modeling with PyMC v5+. Use when building probabilistic models, specifying priors, running MCMC inference, diagnosing convergence, or comparing models. Covers PyMC, ArviZ, pymc-bart, pymc-extras, nutpie, and JAX/NumPyro backends. Triggers on tasks involving: Bayesian inference, posterior sampling, hierarchical/multilevel models, GLMs, time series, Gaussian processes, BART, mixture models, prior/posterior predictive checks, MCMC diagnostics, LOO-CV, WAIC, model comparison, or causal inference with do/observe.
Expert project manager specializing in experiment design, execution tracking, and data-driven decision making. Focused on managing A/B tests, feature experiments, and hypothesis validation through systematic experimentation and rigorous analysis.
SQL for data analysis with exploratory analysis, advanced aggregations, statistical functions, outlier detection, and business insights. 50+ real-world analytics queries.
When the user wants to build or improve a sales bot's ability to automatically test message variations to optimize conversion. Also use when the user mentions "message testing," "A/B testing bots," "optimizing bot messages," "testing variations," or "message optimization."
Use when selecting statistical methods, performing power analysis, guiding uncertainty quantification, or validating MCMC/Monte Carlo implementations.
Fetches trending skills directly from skills.sh, generates statistics, and creates a trend summary. Optimized for ultra-fast, direct execution without complex setup.
Expert in statistical analysis, predictive modeling, machine learning, and data storytelling to drive business insights.
Data journalism workflows for analysis, visualization, and storytelling. Use when analyzing datasets, creating charts and maps, cleaning messy data, calculating statistics or building data-driven stories. Essential for reporters, newsrooms and researchers working with quantitative information.
Design, plan, and analyze A/B tests with statistical rigor. Use when the user asks about A/B testing, split testing, experiment design, statistical significance, sample size calculation, test duration, multivariate testing, or conversion experiments. Trigger phrases include "A/B test", "split test", "experiment", "statistical significance", "sample size", "test duration", "which version wins", "conversion experiment", "hypothesis test", "variant testing".
Use when implementing data analysis pipelines, statistical tests, or bioinformatics workflows in code (Python/R), particularly for genomics, transcriptomics, proteomics, or other -omics data.
Explore, interpret, and draw conclusions from football data. Use when the user wants to analyse match events, compare teams or players, understand tactical patterns, build visualisations, or needs guidance on what questions to ask of their data. Adapts to the user's experience level.