Data Science & ML Claudeスキル

Bayesian Modeling in R

Performs comprehensive Bayesian statistical modeling and posterior analysis using Stan-based R packages like brms and rstanarm.

Tidymodels Code Review Patterns

Analyzes R machine learning code to detect data leakage, resampling violations, and workflow anti-patterns using tidymodels principles.

R Recipes Feature Engineering

Streamlines data preprocessing and feature engineering using R's Tidymodels recipes framework.

Find Similar: Cross-Domain Pattern Search

Identifies and verifies recurring structural patterns across diverse domains to validate novelty and discover universal insights.

Bayesian Model Diagnostics

Performs comprehensive MCMC diagnostic checks and posterior predictive assessments for Bayesian models implemented in Stan or JAGS.

Clinical Trials Biostatistics

Implements industry-standard clinical trial design and statistical analysis workflows using regulatory-compliant R packages.

Dialectic Spiral

Stress-tests research findings and syntheses through recursive adversarial testing to uncover hidden biases and overlooked evidence.

Source Omission Analysis

Analyzes information gaps and silences across multiple sources to reveal structural biases, funding influences, and hidden narrative frames.

Claude Agent SDK Skill

Enables developers to programmatically build, design, and orchestrate autonomous AI agents using the Claude Agent SDK.

Clinical Trial Time-to-Event Analysis

Provides expert guidance and R implementation patterns for survival analysis methods and non-proportional hazards in clinical trials.

Google Gemini Embeddings

Generates high-performance text embeddings for RAG systems, semantic search, and document clustering using the Gemini API.

Tidymodels Hyperparameter Tuning

Optimizes machine learning models using advanced hyperparameter tuning strategies within the R tidymodels ecosystem.

Bayesian Survival Analysis Models

Implements advanced Bayesian survival and time-to-event models with support for censoring and frailty effects.

Mendelian Randomization in R

Performs causal inference using genetic variants as instrumental variables through comprehensive R-based Mendelian Randomization workflows.

ML-NMR Statistical Methodology

Provides comprehensive methodological guidance for Multilevel Network Meta-Regression (ML-NMR) using the multinma package and NICE DSU standards.

Running Coach AI Index

Manages a centralized catalog of AI coaching skills, TypeScript contracts, and safety guardrails for the Run-Smart platform.

Bayesian Regression Models

Implements advanced Bayesian regression models including linear, logistic, and robust variations using Stan and JAGS.