content-analysis или everyday-causal-skills

Scientific content analysis for academic research. Supports the full workflow from research question to publication-ready output: codebook development, qualitative and quantitative coding, thematic analysis, and inter-coder reliability assessment. Follows established methodology from Krippendorff, Hsieh & Shannon, Mayring, and Braun & Clarke. Produces structured output files including coded data tables (.xlsx), theme summaries (.md), and reliability reports.

A causal inference plugin for AI agents. Describe a problem in plain language and it helps you choose a method, check assumptions, write the analysis in R or Python, and stress-test the results. Who this is for: Anyone who needs to measure whether something actually worked, such as Marketing and growth teams; Product managers and BI analysts; Data scientists; Revenue and operations teams; Policy researchers; Students and self-taught practitioners. What you get from this plugin The plugin works in five steps, from refining the question you want to answer, to writing the report. You are free to pick and start from any step you like. → Describe your problem → Get a method recommendation → Check assumptions and structure the analysis → Stress-test the results → Write the executive report