Skill UI

A comprehensive tool for interacting with the arXiv database. It allows users to search for academic papers by topic or ID, retrieve detailed metadata (abstracts, authors, categories), and download the PDF files directly. Ideal for researchers and students needing to efficiently find, save, and summarize scientific literature.

This advanced skill systematically verifies the novelty of a proposed academic method or research idea against the latest scientific literature. It performs multi-source searches across databases like arXiv, Google Scholar, and major conference proceedings (ICML, NeurIPS). Using a multi-step process, it extracts core claims, searches multiple sources, performs cross-model verification via LLM, and outputs a structured Novelty Report, detailing overlap, prior work, and overall recommendation.

Deep research and scientific writing assistant that produces LaTeX/BibTeX documents with verified citations for papers, proposals, reports, and reviews, ensuring well-formatted, citation-backed outputs for academic or clinical contexts.

This skill guides the process of evaluating research results against intended scientific claims. After running experiments, it systematically collects data from various sources (W&B, logs, etc.), performs a deterministic evidence pre-check to detect hallucinated evidence, and finally uses a Codex judgment to determine if the results support the claims, requiring next actions like pivoting or supplementing the findings.

Leverages 78 public scientific, biomedical, materials, and economic REST APIs to find compounds, genes, pathways, clinical trials, patents, economic indicators, weather, and other structured data, returning raw JSON plus the databases/endpoints used.

Manim is a powerful Python library used for creating high-quality mathematical and scientific animations. This skill facilitates the generation of educational videos and complex visualizations for presentations. It is ideal for researchers, educators, or content creators who need to visualize complex concepts in an engaging animated format. Note: This specific file is related to localization/translation.

This skill teaches users to apply the scientific method to software debugging. Instead of random changes, it guides the process through five steps: Observation, Hypothesizing, Prediction, Testing, and Analysis. By systematically forming and testing specific, measurable hypotheses, users can efficiently isolate failing components and confirm the true root cause of technical issues.

A persistent, structured knowledge base designed to accumulate and connect all research artifacts—including papers, ideas, experiments, and claims—across the entire research lifecycle. Inspired by LLM Wiki patterns, it builds a comprehensive, evolving graph of knowledge by tracking typed relationships (e.g., supports, contradicts, extends), ensuring that findings are compounded and never re-derived. Ideal for complex, multi-stage scientific projects.

This skill is designed to translate vague, colloquial, or descriptive biological details (such as behaviors, appearances, or cultural uses) into precise scientific or common species names. It is essential for constructing effective search queries in biological databases when the question does not use formal taxonomic names. By accurately identifying candidate species names from descriptive clues, users can drastically improve search relevance and accuracy.

A powerful tool for scientific literature review and evidence synthesis. This skill searches a curated database of full-text scientific papers, going beyond traditional abstracts. It extracts and returns highly structured, quantitative data, including methodologies, quantitative results, sample sizes, quality scores, and 25+ metadata fields per paper, making it ideal for meta-analyses and systematic reviews.

Provides comprehensive tools for academic research, allowing users to search databases like Google Scholar and PubMed. It extracts accurate metadata, validates citations, and generates standard formats like BibTeX. The skill ensures reference accuracy and supports the creation of scientific schematics to enhance document visualization.

This skill provides comprehensive support for generating high-stakes clinical documentation, including case reports (CARE guidelines), diagnostic summaries (radiology/pathology), clinical trial reports (ICH-E3), and patient progress notes (SOAP/H&P). It mandates adherence to strict regulatory standards (HIPAA, FDA, ICH-GCP) and integrates AI-generated scientific schematics to ensure the final output is accurate, compliant, and publication-ready.