Skill UI

Guides structured hypothesis generation by combining literature searches, evidence synthesis, competing explanations, and experimental design, with mandatory AI-generated scientific schematics for complete reports.

Conduct comprehensive systematic literature reviews across biomedical and scientific domains using multiple academic databases. Synthesize findings, verify citations, and generate professional markdown or PDF documents. Includes AI-generated scientific schematics for visual enhancement. Ideal for research synthesis, meta-analyses, and thesis writing.

This powerful router analyzes complex document types (scanned, medical, table-heavy, etc.) and determines the optimal conversion pipeline (tools, flags, cleanup) to extract data into various formats like Markdown, JSON, or DOCX. It goes beyond simple text extraction by prioritizing the preservation of document structure, relational data, and high fidelity, ensuring the generated output is accurate for downstream machine processing.

A comprehensive guide for submitting high-quality replication packages for academic journals like the American Economic Journal (AEJ: Micro). This package covers two essential components: self-contained, verifiable proofs for theoretical claims (the Proof Appendix), and a fully reproducible code and data deposit (the Code/Data Repository). It ensures that all numerical results, simulations, and empirical findings can be regenerated by the journal's editor, maintaining the highest standards of scientific transparency.

This guide details the rigorous data analysis and reporting standards required for top-tier academic journals, such as AJPS. It focuses intensely on reproducibility, ensuring that all results—from primary findings to exploratory models—are generated by a single, seeded master script. Key requirements include reporting uncertainty intervals, proving robustness, adhering to pre-registration discipline, and accommodating mandatory third-party code verification. Use this when conducting complex empirical research in social sciences or political science, needing to withstand intensive methodolo...

This guide outlines the stringent standards for creating tables and figures (exhibits) for the American Journal of Political Science (AJPS). It emphasizes that exhibits must be self-contained, highly accessible, and, critically, regenerated exactly by deposited code for third-party verification. It provides best practices for structuring research results, managing word counts by differentiating between core arguments in the main text and supporting details in the Supporting Information (SI).

This guide details the strict data and transparency policy required by the American Political Science Review (APSR). It outlines how to build a comprehensive reproducibility package—including raw data, master scripts, and detailed documentation—to allow the journal to verify that all reported tables and figures can be regenerated before publication. It covers quantitative methods, qualitative evidence sharing, and necessary exemption procedures.

A comprehensive guide for generating and packaging reproducible evidence for signal processing submissions to ICASSP. It details what reviewers prioritize (scoring scripts, dataset splits, logs) and outlines best practices to ensure that headline metrics are easily regenerated, thereby maximizing review credibility and post-publication impact.

This guide assists researchers in assembling a comprehensive, reproducible data and code supplement for submissions to statistical journals, adhering to the American Statistical Association (ASA) data-sharing policies. It outlines the required structure (README, code, data), provenance tracking, environment pinning, and necessary steps (like setting random seeds and drafting data availability statements) to ensure that published results can be fully regenerated and verified by reviewers.

This comprehensive guide provides detailed standards for creating tables and figures for submission to academic journals, specifically referencing The Journal of Politics (JOP). It emphasizes that all exhibits must be self-contained, highly legible in print, and, critically, regenerated by deposited code to ensure strict reproducibility and manage page limits effectively.

A comprehensive guide to ensuring the quantitative rigor and full reproducibility of econometrics manuscripts, particularly for submissions to high-tier journals like QE. Covers structural estimation (GMM, MSM), moment construction, data cleaning best practices, and comprehensive validation checks (e.g., multi-start, Monte Carlo recovery). It ensures that all results can be regenerated from raw data using pinned environments and documented scripts.

This guide outlines the comprehensive policy and architecture required for submitting a fully reproducible replication package to The Review of Economics and Statistics (REStat). It details how to structure the repository, including data, master scripts, and the README file, ensuring that every result can be traced and regenerated by other researchers.