Skill UI

Provides GraphQL queries to the Open Targets Platform for target-disease associations, tractability, safety, drugs, and multi-omics evidence to guide therapeutic target identification, safety assessment, and repurposing research workflows.

Probabilistic deep-learning framework built on PyTorch for single-cell omics; handles batch correction, integration, multimodal and spatial data, differential expression, and transfer learning with unified APIs.

Access multi-dimensional cancer genomics data via cBioPortal REST API for mutation, copy-number, expression, and survival insights across TCGA/GENIE studies, enabling target validation and clinical association analyses programmatically with Python clients.

A comprehensive toolkit for reconstructing and analyzing the evolutionary history of biological sequences (genes, proteins, genomes). The standard workflow includes multiple sequence alignment using MAFFT, robust maximum likelihood tree inference with IQ-TREE 2 and FastTree, and advanced visualization/manipulation using ETE3. This skill is essential for studies in viral phylodynamics, microbial genomics, protein family evolution, and molecular clock dating.

scvi-tools is a comprehensive Python framework for probabilistic deep generative models in single-cell genomics. Built on PyTorch and PyTorch Lightning, it enables batch correction, transfer learning, differential expression analysis with uncertainty, and multi-modal data integration. Best for advanced modeling of single-cell RNA-seq, ATAC-seq, CITE-seq, spatial transcriptomics, and specialized modalities like methylation and cytometry.

This guide assists researchers targeting top journals like China Rural Economy (CRE). It provides a structured self-check process for manuscripts, ensuring the topic strictly adheres to the "Three Rural Issues" scope, establishes deep theoretical grounding, and articulates clear, multi-faceted marginal contributions suitable for high-impact academic publication.

A comprehensive guide for ensuring rigorous statistical inference in experimental economics. It covers critical areas such as power justification, determining the correct unit of observation (cluster-robust SEs), handling multiple comparisons, and applying non-parametric tests. This framework helps researchers transition from preliminary findings to statistically robust, publishable conclusions.

This skill guides the polishing of prose, abstract, and introduction for manuscripts intended for Games and Economic Behavior (GEB). It enforces a rigorous, idea-first writing style, ensuring the work is highly readable for a broad, multi-disciplinary academic audience (including economics, CS, and biology). Key guidelines include maintaining an abstract under 250 words, structuring the introduction around the core question and result, and motivating formal arguments with clear prose.

This package ensures full reproducibility for international macroeconomics research. It is designed for projects utilizing complex, multi-source, and often restricted data (such as IMF or central bank files). It mandates capturing the entire data lineage, pinning the execution environment (R, Python, Stata), and documenting access instructions, allowing any authorized replicator to rebuild the analysis from scratch.

This router guides authors through the complex, multi-stage process of optimizing a manuscript for submission to the IMF Economic Review (IMFER). It systematically diagnoses bottlenecks—from topic selection and literature positioning to model robustness and rebuttal strategy—ensuring the paper meets the high standards of international macroeconomics and policy relevance.

This skill guides authors to evaluate if their mechanistic immunology study meets the high standards of the journal Immunity. It serves as a comprehensive pre-submission checklist, detailing required evidence (in vivo validation, single-cell genomics, multi-omic data), necessary mechanistic depth, structural requirements, and strict submission guidelines to ensure the core advance is the immune mechanism.

This skill guides researchers on how to position their academic paper for top-tier journals in entrepreneurship. Instead of merely identifying 'gaps,' the focus is on joining an active scholarly conversation, problematizing established assumptions, and engaging with multiple disciplinary lenses (e.g., economics, psychology, sociology). It ensures the manuscript's contribution is clearly framed against the existing literature.