Assistant for creating, editing, and debugging reactive Python notebooks with marimo. Use when you need to build marimo notebooks, debug reactive execution, add interactive UI elements, or convert traditional notebooks to marimo format. Provides code patterns, utility functions, and best practices for marimo development.
Test at extremes (1000x bigger/smaller, instant/year-long) to expose fundamental truths hidden at normal scales
World-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics. Expertise in Python (NumPy, Pandas, Scikit-learn), R, SQL, statistical methods, A/B testing, time series, and business intelligence. Includes experiment design, feature engineering, model evaluation, and stakeholder communication. Use when designing experiments, building predictive models, performing causal analysis, or driving data-driven decisions.
Linear algebra operations in NumPy, including matrix multiplication, SVD, system solving, and least squares fitting. Triggers: linalg, matrix multiplication, SVD, eigenvalues, matrix decomposition, lstsq, multi_dot.
Reference for how I like to dance and call square dances.
Analyze Excel spreadsheet formulas to build dependency DAGs (Directed Acyclic Graphs) and understand calculation chains. This skill should be used when the user wants to reverse-engineer Excel formula dependencies, trace how values are calculated from inputs to outputs, validate formula logic, or create reusable calculators from spreadsheet logic.
Generate a markdown summary report from candidate_profile.csv with statistics and insights
Debug Pandas issues systematically. Use when encountering DataFrame errors, SettingWithCopyWarning, KeyError on column access, merge and join mismatches with unexpected NaN values, memory errors with large DataFrames, dtype conversion issues, index alignment problems, or any data manipulation errors in Python data analysis workflows.
Validate data quality in CSV/Excel files for vehicle insurance platform. Use when checking required fields, validating data formats, detecting quality issues, or generating quality reports. Mentions "validate", "check fields", "data quality", "missing values", or "quality score".
This skill should be used when the user asks "how to install Chart.js", "Chart.js setup", "getting started with Chart.js", "Chart.js CDN", "Chart.js npm install", "tree-shaking Chart.js", "Chart.js bundle optimization", "import Chart.js", "Chart.js module loaders", "Chart.js CommonJS", "Chart.js RequireJS", "chart.js/auto vs manual registration", "Chart.js defaults", "update chart data", "chart instance methods", "destroy chart", "Chart.js helpers", "resize chart", "responsive chart configuration", "Chart.js global configuration", "getRelativePosition", or needs help with initial Chart.js v4.5.1 project setup, configuration, and chart manipulation.
This skill should be used when the user asks "Chart.js cheat sheet", "Chart.js quick reference", "Chart.js snippets", "common Chart.js patterns", "Chart.js copy paste", "Chart.js quick tips", "Chart.js one-liners", "quick Chart.js examples", "Chart.js recipes", or needs copy-paste ready code snippets for common Chart.js v4.5.1 patterns without deep documentation.
Generates professional reports from research and data
Эксперт SQL window functions. Используй для аналитических запросов, OVER clauses и ranking functions.
Create reactive Python notebooks for IMSA racing data analysis using marimo. Use for building interactive filtering UIs (seasons, classes, events), connecting to DuckDB databases, creating reactive visualizations, and performing data analysis with automatic cell re-execution. Includes templates, patterns, and IMSA-specific workflows.
This skill should be used when the user asks "Chart.js animations", "Chart.js easing", "Chart.js animation duration", "Chart.js animation callbacks", "Chart.js transitions", "Chart.js animation loop", "progressive line animation", "Chart.js animation delay", "disable Chart.js animation", "Chart.js onComplete", "Chart.js onProgress", or needs help configuring animations in Chart.js v4.5.1.
Automate statistical validation, hypothesis testing, and confidence interval calculations required by TAR UMT thesis standards and top ML research conferences. Use when you need to (1) calculate 95% confidence intervals for model performance metrics, (2) perform hypothesis testing to compare models (McNemar's test, paired t-test, Bonferroni correction), (3) generate APA-formatted results tables for thesis chapters, (4) create reproducibility statements for experimental setup, (5) validate statistical significance of model improvements, or (6) format results according to academic publishing standards. Essential for FYP Chapter 4 (experimental setup documentation) and Chapter 5 (results with statistical validation).
Analyzes CSV files, generates summary stats, and plots quick visualizations using Python and pandas.
Analyzes CSV files, generates summary stats, and plots quick visualizations using Python and pandas.
Create geographic maps for Belgian data visualizations. Use when the user asks to add a map, create a choropleth map, visualize geographic data, show data by region/province/municipality, or add a MunicipalityMap component. This skill covers Belgian geographic data (regions, provinces, municipalities) using NIS codes.
Use when you have CSV/Excel data files and need PM insights (retention, funnel, segmentation) via Python analysis.
