Skill UI

A comprehensive guide for researchers in finance and economics to rigorously assess their research proposals. It helps pressure-test an idea against the high standards of top academic journals, focusing on establishing genuine novelty (new questions, mechanisms, or data) and methodological rigor. Ideal for structuring the contribution claim and refining the research design.

This guide acts as a comprehensive workflow router for developing manuscripts intended for The Review of Financial Studies (RFS). It diagnoses the current stage of the research process—from initial topic selection and literature positioning to empirical design, robustness checking, writing polish, and rebuttal. It does not replace specialized skills but directs the user to the precise skill needed next, ensuring novelty and rigor are maintained throughout the entire academic publishing cycle.

A comprehensive guide designed for academics writing papers in Industrial Organization (IO). Instead of summarizing existing literature, this method teaches authors how to strategically position their unique contribution against the current IO research frontier. It covers identifying the relevant IO strand, citing anchor literature correctly, and concisely defining the precise academic gap the manuscript fills, adhering to high-level journal style guides.

This skill bridges theoretical econometric designs (DiD, IV, RDD, SCM, DML) to concrete, audited statistical estimates. It orchestrates the necessary tool calls to fit models, generate comprehensive diagnostics, and report reliable findings, ensuring that every academic claim is backed by executable, traceable code. Essential for advanced causal inference research.

This skill is designed to help researchers assess the suitability and structure of their academic manuscripts for the journal 'Scientific Management Research.' It analyzes the alignment of the topic with core areas like innovation policy, technology management, and regional science. Users can utilize it to understand journal requirements, identify common rejection pitfalls, and receive suggestions for improving the narrative and scope before submission.

This guide provides a comprehensive audit checklist for designing and evaluating sensor-based systems (SenSys). It mandates rigorous, real-world measurements across five core axes: energy efficiency, on-device latency distribution, ground truth accuracy, deployment realism, and embedded AI footprint. It ensures that research results are based on measured hardware performance, not simulations or idealized baselines.

A comprehensive guide on methodological rigor for submitting manuscripts to Social Forces. It covers designing causal inference (panel/DID/IV/RDD), demographic analysis, comparative-historical case selection, and network/computational methods. The focus is on strengthening the design by rigorously defending assumptions and ruling out alternative explanations, ensuring methodological soundness beyond mere association.

This tool acts as an orchestrator for academic manuscripts targeting general social-science journals. It guides authors through the entire writing and revision lifecycle, ensuring the paper maintains a general audience focus and adheres to strict structural constraints, such as 10,000-word limits (including references) and 10 figure/table panels. It routes the user to the most necessary next skill, whether for initial topic selection, theoretical positioning, research design, or final rebuttal.

This guide outlines the rigorous standards required for submitting top-tier academic papers, such as those for ACM SIGCOMM. It instructs authors on how to transition from general claims to highly evidence-based arguments by focusing on measured operational pain, defining design principles, and pairing every performance claim with concrete testbed, trace, or deployment evidence. Essential for researchers in networking and systems.

This comprehensive guide details the rigorous methodology for designing, executing, and reporting experimental results in Information Retrieval (IR). It covers best practices for selecting appropriate test collections, choosing defensible metrics, performing proper statistical significance testing (e.g., paired t-tests), ensuring baseline fairness, and proactively addressing modern pitfalls like LLM contamination. Essential for publishing high-quality, auditable research in IR/NLP.

A comprehensive guide designed for planning major academic conference submissions (e.g., SIGIR). It provides a detailed backward-planning calendar, mapping milestones from initial scoping and evidence building through to final camera-ready submission and presentation. This tool helps researchers manage complex multi-track coordination, identify potential failure modes, and utilize fallback conference pathways.

This comprehensive guide outlines best practices for designing data systems evaluations, ensuring results are robust, reproducible, and convincing to expert reviewers. It covers workload realism, baseline fairness, necessary ablation studies, and advanced metric visualization techniques (e.g., sweeping curves, failure analysis) required for top-tier data systems research.