A curated collection of skills, agents, and commands for AI coding assistants. These assets extend agent capabilities for data science, experimentation, and analytics workflows.
Skills follow the Agent Skills format. Specs available at https://agentskills.io/specification
Use when:
- Designing A/B tests or online controlled experiments
- Calculating sample sizes and power analysis
- Analyzing test results with statistical rigor
- Making decisions about experiment readiness or validity
- Interpreting statistical significance and practical significance
Categories: Experimentation, Statistics, A/B Testing
Use when:
- Evaluating research claims or experimental results
- Identifying logical fallacies or biases in analysis
- Applying the scientific method to data problems
- Peer-reviewing analysis or experiment designs
- Ensuring rigorous, objective reasoning
Categories: Scientific Method, Critical Thinking, Research
Use when:
- Starting analysis on a new dataset
- Performing exploratory data analysis (EDA)
- Understanding data distributions and relationships
- Identifying data quality issues or anomalies
- Creating initial visualizations and summaries
Categories: EDA, Data Analysis, Data Science
Use when:
- Validating data quality and integrity
- Checking data schema and types
- Identifying missing values, outliers, or inconsistencies
- Implementing data quality checks in pipelines
- Creating data validation reports
Categories: Data Quality, Validation, Data Engineering
Use when:
- Performing hypothesis testing (t-tests, ANOVA, chi-square)
- Calculating confidence intervals and effect sizes
- Generating APA-style statistical reports
- Choosing appropriate statistical tests for your data
- Interpreting p-values and statistical significance
Categories: Statistics, Hypothesis Testing, Inference
Use when:
- Performing basic statistical tests (t-test, proportion test)
- Computing descriptive statistics (mean, median, variance)
- Creating simple statistical summaries
- Learning fundamental statistical concepts
- Quick statistical validation without complexity
Categories: Statistics, Basics, Descriptive Stats
Use when:
- Creating interactive visualizations
- Building dashboards with hover interactions
- Making publication-quality figures
- Exporting plots to HTML
- Working with time series, scatter plots, or complex charts
Categories: Visualization, Interactive, Plotly
Use when:
- Creating statistical visualizations
- Making publication-quality static plots
- Using matplotlib-based plotting
- Creating categorical plots, distributions, or heatmaps
- Applying statistical transformations in plots
Categories: Visualization, Statistics, Seaborn
Use when:
- General data visualization best practices
- Choosing the right chart type for your data
- Designing clear, informative visualizations
- Creating accessible and colorblind-friendly plots
- Following data visualization principles
Categories: Visualization, Best Practices, Design
Use when:
- Creating flowcharts, sequence diagrams, or state machines
- Documenting processes or workflows
- Visualizing system architecture
- Generating diagrams from text descriptions
- Embedding diagrams in markdown documentation
Categories: Diagrams, Documentation, Mermaid
Use when:
- Presenting data insights to stakeholders
- Crafting narratives from analysis results
- Building compelling data-driven arguments
- Structuring analysis reports or presentations
- Translating technical findings to business context
Categories: Communication, Storytelling, Presentation
Use when:
- Writing technical documentation
- Drafting emails, reports, or presentations
- Simplifying complex explanations
- Improving clarity and reducing wordiness
- Following technical writing best practices
Categories: Writing, Communication, Documentation
Use when:
- Building machine learning models on tabular data
- Using gradient boosting (XGBoost, LightGBM, CatBoost)
- Performing feature engineering and selection
- Tuning hyperparameters
- Evaluating model performance
Categories: Machine Learning, Gradient Boosting, Modeling
Use when:
- Explaining machine learning model predictions
- Computing feature importance with SHAP values
- Generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap)
- Debugging or validating model behavior
- Analyzing model bias or fairness
- Implementing explainable AI in production
Categories: Machine Learning, Explainability, Model Interpretation, XAI
Use when:
- Using Nubank's fklearn functional ML library
- Building functional ML pipelines
- Working with immutable transformations
- Composing feature engineering steps
- Following functional programming patterns in ML
Categories: Machine Learning, fklearn, Functional Programming
Use when:
- Working with arrays, maps, nested data, or JSON/variant types
- Using window functions with QUALIFY
- Querying historical data with Delta Lake time travel
- Using higher-order functions (TRANSFORM, FILTER)
- Analyzing query execution plans with EXPLAIN
Categories: SQL, Databricks, Spark
Use when:
- Writing Scala code following best practices
- Building functional Scala applications
- Working with Scala collections and type systems
- Implementing type-safe designs
- Reviewing or refactoring Scala code
Categories: Scala, Programming, Software Engineering
A specialized agent for A/B testing and online controlled experiments. Expert in statistical test design, power analysis, sample size calculation, and result interpretation using both frequentist and Bayesian methods.
Use when:
- Designing new experiments
- Analyzing test results
- Troubleshooting unexpected experiment outcomes
- Making go/no-go decisions on tests
- Calculating test duration or sample requirements
- Place skills in
.claude/skills/ - Place agents in
.claude/agents/ - Skills and agents are automatically available when relevant tasks are detected
Each skill contains:
SKILL.md- Instructions and context for the agent (required)scripts/- Helper scripts for automation (optional)references/- Supporting documentation (optional)assets/- Templates or resources (optional)
Each agent is a single markdown file with:
- Frontmatter metadata (name, description, model, tools)
- Core expertise and responsibilities
- When to invoke the agent
- Workflow patterns and best practices
MIT