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.

This comprehensive guide details techniques for performing forensic investigations on Linux systems. It covers analyzing critical logs—including auth.log, syslog, systemd journal, and auditd—to reconstruct user sessions, trace unauthorized access, detect privilege escalation, and establish detailed event timelines following a suspected security compromise.

Create truthful, accessible, publication-ready scientific figures using Matplotlib, Seaborn, or Plotly. Ensures data integrity, proper encoding, accessibility compliance, and journal export planning for research publications. Includes guidelines for uncertainty displays, color/contrast review, and metadata validation.

Search 18 scholarly APIs for papers, preprints, citations, open-access full text, repository records, and journal OA status with reproducible provenance.

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.

This skill is a comprehensive strategic guide for authors targeting the ACM Conference on Recommender Systems (RecSys). It helps assess the fit of a manuscript, guides the re-framing of work from journal or arXiv styles into a specialized conference narrative, and outlines specific requirements for evidence, evaluation (including offline metrics, user context, and leakage-aware methods), and contribution framing necessary for top-tier CS venues.

A comprehensive guide for authors submitting data mining papers to the ACM SIGKDD Conference. It helps authors assess the manuscript's fit, reframe the contribution (e.g., from journal to conference style), and strengthens the evidence bar by focusing on novelty, scale, reproducibility, and clear real-world impact required by top-tier data science venues.

A comprehensive guide for writing and verifying the institutional and accounting standard background section in academic manuscripts. This tool emphasizes the critical need for precision, requiring accurate standard names, official document numbers (文号), effective dates, and applicable scopes. Essential for empirical studies relying on regulatory changes, ensuring the manuscript meets the high standards of authoritative accounting journals.

A comprehensive guide for constructing academically robust and reproducible accounting metrics, such as discretionary accruals, real earnings management, and conservatism scores. This resource details the necessary steps—including estimation models, granularity, sample requirements, and standardized protocols—to ensure submissions meet the rigorous standards of top accounting journals.

This skill serves as a comprehensive guide for authors submitting manuscripts to highly selective pure mathematics journals like Acta Mathematica. It assesses the work's significance, intellectual fit, required level of rigor, structural integrity, and adherence to top-tier academic standards, ensuring the research is ready for the global mathematical research community.

This skill guides authors through the rigorous requirements of American Economic Journal: Economic Policy. It helps evaluate whether an applied economics manuscript meets the journal's high bar, focusing specifically on the joint criteria of sound causal identification (DiD, IV, RDD) and clear, impactful policy relevance. Use it to re-frame arguments and check against best practices for policy evaluation.

This guide provides a rigorous framework for defending causal identification claims in empirical microeconomics, tailored for top-tier journals like AEJ: Applied. It covers modern techniques and anti-patterns for RCTs, Difference-in-Differences (DiD), Regression Discontinuity (RDD), and Instrumental Variables (IV), ensuring the mapping from variation to the causal estimand is explicit, defensible, and falsifiable.