TobiasBlask

Guided statistical analysis with test selection and reporting. Use when you need help choosing appropriate tests for your data, assumption checking, power analysis, and APA-formatted results. Best for academic research reporting, test selection guidance. For implementing specific models programmatically use statsmodels.

Activate when the user needs to analyze how their paper positions itself relative to the closest existing work. Builds a differentiation matrix, identifies unique positioning, and generates a positioning statement that strengthens the contribution argumentation. Complements the theory-engine by focusing on the competitive landscape of related papers.

Activate when the user needs to evaluate whether a research idea is worth pursuing, brainstorm new research directions, or stress-test a paper concept before committing. This is Phase 0 of the paper machine — the gate that decides whether the full 8-phase pipeline should run. Also activates standalone via /evaluate-idea. Integrates Carlini's research philosophy (conclusion-first test, taste for pr

Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers

Statistical visualization with pandas integration. Use for quick exploration of distributions, relationships, and categorical comparisons with attractive defaults. Best for box plots, violin plots, pair plots, heatmaps. Built on matplotlib. For interactive plots use plotly; for publication styling use scientific-visualization.