Skill UI

A comprehensive template designed for quarterly financial reporting. It integrates key performance indicators (KPIs), revenue and burn charts, a detailed Profit & Loss (P&L) summary, top-line highlights, and future outlook sections. This is ideal for presenting structured financial performance to stakeholders.

Used to verify the actual billing reality, revenue, and pricing mechanics—such as team billing, quotas, and refunds—based on underlying product code, rather than sales documents or simple customer fixes. This skill separates billing facts, customer impact, and the true product implementation logic.

This skill generates a structured, non-advisory worksheet for planning trade ideas, basket adjustments, or comparing prediction markets. It helps users methodically review venues, underliers, constraints, and manual execution steps without placing any trades or providing financial advice. Ideal for the research phase of quantitative finance workflows.

A comprehensive guide for academic researchers aiming to submit papers to the top accounting journal, *Accounting Research*. This skill helps authors identify if their research focus aligns with core accounting mechanisms, such as disclosure quality, auditing standards, and regulatory changes, distinguishing it from general corporate finance or economics.

A comprehensive guide for researchers to determine the academic positioning of their manuscripts. It differentiates between core accounting issues (e.g., disclosure quality, audit standards, earnings management) and corporate finance mechanisms (e.g., pricing, funding constraints). Use this tool to ensure your paper aligns perfectly with the scope of specialized academic journals.

This guide provides comprehensive econometric methodologies for establishing causality in accounting and finance research. It covers advanced techniques such as Difference-in-Differences (DID), Propensity Score Matching (PSM), Heckman selection models, and Instrumental Variables (IV), emphasizing the correct handling of regulatory changes and external shocks to meet top-tier academic standards.

This guide helps authors strategically position their research within active academic debates, particularly in accounting. It ensures the contribution advances a live accounting conversation, clearly distinguishes the work from related finance or economics papers, and adheres to necessary disclosure rules regarding the authors' own concurrent publications. It transforms a manuscript from merely reporting findings into actively joining and advancing a scholarly dialogue.

This tool guides researchers in transforming initial phenomena or datasets into sharp, high-impact accounting research questions. It assesses the fit of the topic with broad academic scopes (like CAR), determines the most appropriate research tradition (e.g., archival, experimental, field), and helps articulate the core academic tension. It ensures the research advances understanding of accounting itself, rather than just using accounting data for general finance or economics questions.

A comprehensive guide for drafting and revising highly rigorous academic abstracts for top-tier finance and economics journals. It enforces a strict five-sentence structure (Problem, Data/Method, Findings, Mechanism, Contribution) while ensuring quantitative depth and semantic alignment between Chinese and English versions. It teaches how to eliminate common academic filler phrases and elevate the contribution narrative.

This detailed framework guides researchers in conducting rigorous and academically sound heterogeneity analysis for finance and economics manuscripts. It covers dimension prioritization, theoretical justification, sample size requirements, and statistical significance testing protocols (e.g., Chow test). Use this guide to strengthen the robustness of main findings and deepen the paper's contribution by identifying the specific conditions under which the effect operates.

A comprehensive guide for strengthening causal identification strategies in empirical finance and economics. It covers advanced quasi-experimental designs like Difference-in-Differences (DID), Instrumental Variables (IV), Regression Discontinuity (RDD), Propensity Score Matching (PSM), and Double Machine Learning (DML). Use this framework to stress-test your research design, address common econometric pitfalls, and ensure your paper meets the rigorous standards of top academic journals.

A comprehensive guide for structuring and refining the literature review section of academic manuscripts for top-tier finance and economics journals. It covers the mandatory three-layer structure (Theory, Empirical Evidence, Contribution Gap), optimal Chinese/English citation ratios, and techniques for developing a 'dialogue-based' narrative rather than a simple list, ensuring clear positioning relative to the field's classic theories and recent publications.