Skill UI

This guide helps researchers evaluate the scope and general significance of their theoretical computer science work. It introduces the 'breadth test' (corridor, lineage, technique) and a detailed routing map to advise whether the paper is best suited for a flagship, broad-scope venue like STOC, or a more specialized conference (e.g., SODA, ITCS, CCC). It provides a structured decision procedure for optimizing academic publication success.

This comprehensive guide details the specific structural and stylistic requirements for submitting high-quality theoretical computer science papers to top-tier conferences like STOC. It addresses how to frame the initial theorem statement, structure the technical overview, maintain notation economy, and effectively budget the limited page space to ensure maximum academic rigor and readability for diverse expert audiences.

A comprehensive guide for authors aiming to submit manuscripts to 'Studies in Science of Science.' It details the journal's focus areas, such as science metrics, knowledge networks, and technology policy, emphasizing the need for deep theoretical and methodological contributions. The skill helps authors optimize structure, avoid common rejection traps, and ensure adherence to academic publishing standards.

A comprehensive guide for authors submitting technical papers, detailing methods to ensure that all reported claims, figures, and benchmark numbers can be independently regenerated. This involves creating VM-ready, self-contained artifacts, mapping every claim to a specific script, and defining clear reproducibility levels (Turnkey, Scripted) to satisfy conference requirements.

This guide provides comprehensive rules for structuring academic technical papers, specifically addressing the division of content between the main body, appendices, supplementary websites, and mandatory artifacts. It ensures that all decision-critical arguments, soundness claims, and core comparisons are contained within the primary reviewed pages, making the paper fully judgeable without external resources.

A comprehensive methodological guide for designing rigorous academic studies in accounting. It details advanced econometric techniques—including Difference-in-Differences (DiD), Instrumental Variables (IV), Regression Discontinuity Design (RDD), and experimental setups—to establish credible causal identification. Use this guide when your research challenge is solving endogeneity or isolating a specific economic channel from complex, non-random variables.

This skill assists software engineers through the entire technical job search cycle. It provides structured analysis for job descriptions (identifying must-haves, red flags), optimizes CVs for specific roles, guides writing compelling cover letters, evaluates total compensation packages, and crafts professional post-interview follow-up emails, ensuring the job search process is professional and highly targeted.

This comprehensive skill guides authors submitting research to The Burlington Magazine. It outlines the rigorous academic standards required for object-based art history, focusing on connoisseurship, attribution, and provenance. It ensures manuscripts are tightly argued using primary source evidence, technical findings, and close visual examination, helping authors align their work with the journal's specific evidentiary bar and house style.

This tool helps authors determine if their food science work is best suited for a critical review or trend analysis within the scope of this specific journal. It guides authors to focus on synthesizing existing literature, identifying research gaps, and charting future directions, rather than presenting original primary experimental data.

This guide provides a strategic framework for drafting author responses to challenging peer reviews in academic conferences. It teaches authors how to structure replies by addressing four key axes (Correctness, Novelty, Backing, Clarity), ensuring technical precision, maintaining anonymity, and framing commitments that survive the subsequent Area Chair (AC) discussion phase.

A comprehensive guide for authors submitting to UAI (Uncertainty in Artificial Intelligence). It details how to position research by ensuring coverage across diverse academic communities, including Machine Learning, Statistics, Causal Inference, and foundational mathematics. Focuses on establishing the technical 'delta' rather than merely listing citations, helping authors avoid common review failures.

This comprehensive pre-upload audit guide ensures submissions meet the strict technical and anonymity requirements for UAI OpenReview. It covers critical aspects like the 8-page main body constraint, combining all content into a single PDF, handling file size caps (15MB/50MB), and performing an identity sweep to maintain double-blind review standards.