Skill UI

Transforms data into publication-ready figures with multi-panel layouts, precise annotations, colorblind-friendly palettes, and journal formatting using matplotlib, seaborn, and plotly plus export helpers for PDF/EPS/TIFF.

Seaborn is a powerful Python library for creating publication-quality statistical graphics. It is designed for dataset-oriented plotting, making it ideal for exploratory data analysis, multivariate analysis, and visualizing complex relationships, distributions, and comparisons with minimal code. It builds upon Matplotlib, offering high-level interfaces for functions like scatterplots, heatmaps, and pair plots.

A comprehensive guide for systematic statistical analysis, designed for academic research. This skill assists in selecting the correct hypothesis tests (t-test, ANOVA, regression, chi-square), checking assumptions (normality, homogeneity), performing advanced analyses (Bayesian methods, power analysis), calculating effect sizes, and generating full APA-formatted reports for publication.

Statsmodels is a comprehensive Python library dedicated to advanced statistical modeling and econometrics. It provides tools for rigorous statistical inference, including Ordinary Least Squares (OLS), Generalized Linear Models (GLM), mixed-effects models, and time series analysis (ARIMA). Use this skill for detailed diagnostics, coefficient estimation, hypothesis testing, and producing publication-ready statistical reports in fields like econometrics and biostatistics.

This skill transforms flawed writing—whether pasted text or uploaded documents—into publication-ready prose. It meticulously eliminates issues related to grammar, spelling, punctuation, clarity, and tone, all while strictly preserving the original author's unique voice and intent. It operates in two modes: inline text correction and structured file processing (supporting formats like .docx, .pdf, .txt). Ideal for academic papers, business reports, and any text requiring a professional polish.

Auto-generates publication-quality plots and comparison tables from experiment data, following a shared matplotlib style, respecting figure plans, and preserving manual assets for ML papers.

A comprehensive tool for generating publication-quality figures essential for Machine Learning and AI academic papers. It supports two distinct workflows: generating complex system architecture and workflow diagrams using AI (Gemini), and creating precise, data-driven visualizations (line charts, heatmaps, etc.) using standard libraries like matplotlib and seaborn. Use it when preparing any figure for a conference submission.

Automatically detects typosquatting in npm and PyPI by generating candidate variants from popular dependencies, querying each registry, and scoring matches with Levenshtein distance, publication recency, and download anomalies so teams can flag suspicious packages before installation.

Generate CVPR/NeurIPS-ready publication figures through a multi-stage Claude-and-Gemini workflow that iterates layout, style, and rendering until the strict reviewer score meets the high-quality threshold.

A powerful tool for searching published, peer-reviewed academic literature from major venues like IEEE, ACM, and Springer. Unlike preprint archives (like ArXiv), this skill provides robust metadata, including citation counts, official publication venue details, and TLDR summaries. Use it when you need verified, finalized research papers and advanced filtering capabilities.

Provides REST API access to PubMed for building advanced Boolean/MeSH queries, filtering by publication details, and programmatically fetching citations through E-utilities, making it ideal for systematic reviews and automated literature workflows.

Automates preparing private repos for safe public release through a three-stage pipeline that forks the project, scans and sanitizes secrets, and generates CLAUDE.md, setup scripts, and documentation for GitHub publication.