Skill UI

This guide identifies and provides solutions for common anti-patterns and mistakes when working with Snowflake. It covers best practices in SQL writing, warehouse resource management, data loading strategies (e.g., using MERGE), and access control setup. It is essential for auditing existing deployments, onboarding new users, or performing code reviews to ensure cost efficiency and data integrity.

This tool processes raw search results and bibliography entries to generate a highly structured and curated source pack for long-form academic papers. It categorizes sources into primary, supporting, and weak/excluded sources, ensuring the resulting literature review is reliable, diverse, and methodologically sound. Essential for academic writing workflows.

This advanced helper assists users in writing, debugging, and validating complex formulas, pivot tables, and array functions for platforms like Excel and Google Sheets. It ensures formulas are robust by handling common edge cases (e.g., blank rows, mixed data types, time zone quirks) and provides detailed, worked examples. It also manages cross-platform dialect translation.

This guide details the process of creating advanced, static visualizations in Power BI using Python libraries like matplotlib and seaborn. It covers the full workflow, from script writing and data injection to best practices and limitations, enabling users to render complex, custom charts not supported by native Power BI visuals.

A comprehensive guide and workflow for embedding R scripts, primarily using the ggplot2 library, into Power BI Reports (PBIR). This tool enables the creation of high-quality, static, publication-ready statistical visualizations directly within the report canvas by executing R code on the defined dataset. It covers the full lifecycle: adding the visual, writing the script, injecting the code, and validating the output.

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.

This guide provides a comprehensive, advanced strategy for academic researchers writing microeconometric papers for top-tier journals (e.g., AEJ: Applied). It teaches authors how to anticipate sophisticated, technical objections from expert referees (such as issues with causal inference design, selection bias, or parallel trends). The focus is on pre-empting these objections by integrating solutions directly into the manuscript text, rather than waiting for a formal revision letter.

This guide details the complex requirements for writing the 'Related Work' section for CIKM submissions. It emphasizes the need for 'three-front coverage' (Retrieval, Mining, and Knowledge Management/Databases) and provides specific instructions on writing the 'boundary-work paragraph.' It also covers critical academic hygiene, such as avoiding venue misattribution, maintaining recency baselines, and accurately stating novel contributions.

A comprehensive guide for achieving experimental reproducibility and verifiability in academic writing, specifically targeting top-tier journals. It covers crucial aspects like fixing computing environments, managing random seeds, ensuring data provenance, preventing data leakage, and structuring scripts for external review, transforming research rigor into actionable steps.

This guide provides a rigorous strategy for authors responding to major or minor revisions in cognitive psychology journals. It teaches how to systematically address every reviewer comment, strengthen model-driven inferences through techniques like model comparison and recovery analysis, and maintain the coherence and reproducibility of the theoretical argument. Essential reading for scholarly publication.

A comprehensive guide detailing how to structure and populate the supplementary material section of technical research papers, especially in NLP and Machine Learning. It distinguishes between load-bearing elements (main text) and audit trails (appendix), covering prompt verbatim, decoding configurations, full result grids, and code anonymization protocols to ensure high reproducibility and academic rigor.

A comprehensive methodological guide designed for researchers writing economics and finance papers. It specifies the rigorous requirements for conducting heterogeneity analysis, emphasizing the need for theoretical justification, sufficient sample size, and alignment with the underlying economic or institutional mechanisms (especially within the Chinese finance and trade context).