Skill UI

Curated catalog of scientific datasets, models, and interactive Spaces on Hugging Face across 17 domains like biology, chemistry, physics, and climate. Helps researchers discover, select, and load scientific ML resources via datasets, transformers, and gradio_client.

This utility evaluates complex arithmetic expressions using arbitrary-precision decimal mathematics. It is specifically designed for scientific, financial, or engineering calculations where standard floating-point arithmetic causes unacceptable rounding errors (e.g., 0.1 + 0.2). Supports addition, subtraction, multiplication, division, exponents, and parentheses.

Search scientific and technical web content with Exa and batch-extract text from webpages or PDFs. Supports scholarly discovery via publication category and academic domain filters for research tasks.

Build, run, and debug Nextflow data pipelines and nf-core workflows end to end. Supports reproducible scientific/bioinformatics workflows, HPC/cloud scaling, and nf-core module testing.

This stub experiment generator is designed for the meta-paper-write demo. It takes a topic phrase and deterministically produces a 20-row CSV file containing baseline and generated results. This ensures stable and reproducible results for demonstration purposes, but should not be used for real scientific data.

This tool reads a CSV containing three columns (x, y_baseline, y_ours). It uses matplotlib to generate a two-line plot visualizing the results and saves the final output as a PDF file. It is specifically designed for visualizing performance comparisons in scientific papers.

This skill generates detailed, structured research protocols for various academic, clinical, or market research studies. It ensures all critical components—such as objectives, study design, participant criteria, methodology, data management, statistical analysis plan, and ethical considerations—are comprehensively documented, providing a robust framework for any scientific investigation.

A specialized tool for scientific data visualization, acting as a visualization advisor to analyze data, recommend chart types, prevent common errors, and generate publication-grade figures.

A comprehensive guide detailing the process of responding to major or minor revisions from academic journals (like AAAG). It teaches authors how to structure a point-by-point rebuttal letter, address editor and referee concerns methodically, strengthen the core academic argument, and incorporate required methodological and cartographic fixes, ensuring the response is both respectful and scientifically rigorous.

A comprehensive set of guidelines for generating maps, figures, and tables for academic journals. This guide emphasizes cartographic standards, statistical rigor, and maximizing the visual argument, ensuring that all exhibits are reproducible, legible in print, and adhere to strict word count limitations. It covers everything from projection choice and data normalization to model plot interpretation and handling spatial uncertainty.

A comprehensive guide for structuring and writing high-quality AI research manuscripts intended for major academic conferences (e.g., AAAI). It teaches how to structure the first page for maximum impact, clearly state contributions (method, theory, system, etc.), ensure two-column readability, and address ethical limitations to appeal to a broad, non-specialist committee audience.

Provides comprehensive guidelines for achieving scientific reproducibility in academic research, particularly for control systems, automation, and pattern recognition. It covers fixing environment dependencies, data handling, random seeds, and process pipelines, helping authors withstand rigorous peer review questioning and build verifiable research artifacts.