Loading...
Loading...
Found 7 Skills
Computational text analysis for sociology research using R or Python. Guides you through topic models, sentiment analysis, classification, and embeddings with systematic validation. Supports both traditional (LDA, STM) and neural (BERT, BERTopic) methods.
Transform Claude Code into an AI Scientist that orchestrates research workflows using tree-based hypothesis exploration. Triggers on "research project", "scientific experiment", "run experiments", "AI scientist", "tree search experimentation", "systematic study".
Prepare organized packages of project files for sharing at different levels - from summary PDFs to fully reproducible archives. Creates copies with cleaned notebooks, documentation, and appropriate file selection. After creating sharing package, all work continues in the main project directory.
R programming for data analysis, visualization, and statistical workflows. Use when working with R scripts (.R), Quarto documents (.qmd), RMarkdown (.Rmd), or R projects. Covers tidyverse workflows, ggplot2 visualizations, statistical analysis, epidemiological methods, and reproducible research practices.
Conduct a systematic literature review following the PRISMA framework with explicit search strategy, inclusion and exclusion criteria, quality assessment, and transparent synthesis. Use this skill when the user needs to design a reproducible literature search, apply PRISMA flow documentation, develop inclusion and exclusion criteria, assess study quality, or when they ask 'how do I do a systematic review', 'what is PRISMA', or 'how do I make my literature review reproducible'.
Write structured experiment report documents from ML/research experiment notes, configs, logs, metrics, tables, and figures. Use this skill whenever the user asks to write an experiment report, research update, mentor update, weekly experiment summary, result analysis document, or presentation-ready experiment writeup, especially when the output should explain motivation, setup, algorithms, metrics, results, figures, interpretation, conclusions, limitations, and next steps.
Use when implementing data analysis pipelines, statistical tests, or bioinformatics workflows in code (Python/R), particularly for genomics, transcriptomics, proteomics, or other -omics data.