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 eliminates grammatical, spelling, punctuation, clarity, and tone issues while strictly preserving the author’s original intent and unique voice. Use it for professional document refinement, academic paper polishing, or general text review, ensuring the corrected version maintains your authentic style.

This skill provides comprehensive tools for AI engineers and researchers to publish, manage, and link research papers directly on the Hugging Face Hub. It supports creating dedicated paper pages, linking papers to corresponding models and datasets, claiming authorship, and generating professional, markdown-based research articles with LaTeX support. Use it to streamline the entire academic publishing workflow from initial draft to final publication.

This tool systematically generates, validates, and ranks concrete, publishable research ideas based on a broad research direction provided by the user. It guides the user through the entire idea discovery pipeline, from initial landscape survey (Phase 1) to validation (novelty checks, experiments) and review, ensuring the generated ideas are novel and relevant for academic publication. It manages complex workflow orchestration and utilizes multiple external tools.

Automates the generation of high-quality, publication-ready figures and comparison tables from raw experimental data (JSON, CSV). This skill is essential for academic writing workflows, handling various plot types including line graphs, bar charts, scatter plots, and heatmaps, ensuring consistency with professional style guides like LaTeX and specific DPI requirements.

This skill automates the end-to-end academic paper writing process. It transforms raw narrative reports, research ideas, or existing plans into polished, submission-ready LaTeX source and PDF. The comprehensive pipeline includes planning, figure generation, structured writing, compilation, and an advanced improvement loop, allowing users to achieve publication-quality results. Users can optionally guide the structural style using a reference paper.

This skill automates the entire academic research lifecycle. It orchestrates stages from broad idea discovery and literature review to experimental implementation, rigorous cross-model review, and finally, generating a polished research paper PDF. Ideal for researchers needing an end-to-end, autonomous workflow for academic publication preparation.

This skill transforms vague or fuzzy research directions into highly concrete, elegant, and academically rigorous proposals. It operates through multi-phase, iterative refinement, anchoring the process to the core problem while ensuring the proposed method is minimal, cutting-edge, and ready for publication or implementation.

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.