Skill UI

A comprehensive guide to navigating the UIST (User Interface Software and Technology) academic review pipeline. It details every stage, from initial submission and expert review to the rebuttal phase and final committee decisions. The guide teaches authors how to critically analyze reviewer feedback, identify evidence gaps, strengthen novelty claims, and strategically plan for conditional acceptance or resubmission.

A specialized guide for authors aiming to submit to the Conference on Uncertainty in Artificial Intelligence (UAI). It provides expert advice on framing the core contribution—such as probabilistic reasoning, causal inference, and uncertainty modeling—and calibrating the manuscript for highly technical, statistical ML reviewers. Use this skill to refine your submission strategy, identify evidence gaps, and properly position your work relative to other top-tier AI venues like AISTATS and IJCAI.

This detailed guide helps researchers determine the optimal top-tier venue (e.g., USENIX Security, IEEE S&P, ACM CCS) for their security research paper. It emphasizes that venue selection must be based on the committee's core expertise and the existence of reusable artifacts, measurements, or systems evidence, ensuring the work has the necessary technical depth to succeed.

This guide provides a comprehensive overview of the VLDB submission and review pipeline. It details the rolling monthly structure, the decision outcomes (Accept, Revise, Reject), and the specific expectations for authors at each stage. Use this to set realistic expectations regarding review timelines, the nature of expert feedback, and optimizing your revision strategy, contrasting it with traditional batch venues.

This guide provides an expert methodology for writing the 'Related Work' section for top-tier web conferences (like WWW). It teaches authors how to establish the paper's academic lineage, contrast the work against competing venues (e.g., SIGIR, KDD), and manage citation hygiene, ensuring maximum impact within strict page limits.

This guide provides a comprehensive framework for authors to articulate the unique and marginal contribution of their research within the highly specialized field of health economics. It teaches how to frame findings against canonical literature, identify specific gaps, and structure the argument to satisfy expert reviewers, ensuring the paper's novelty is clearly demonstrated.

This guide provides expert strategies for positioning academic research, particularly in urban economics. It teaches how to precisely articulate a manuscript's marginal contribution against established theoretical frontiers (e.g., agglomeration, spatial equilibrium, housing supply) and related canonical literature, ensuring the paper is clearly differentiated and compelling to expert referees.

This guide outlines the strategic process for authors responding to major or minor revisions (R&R) for academic journals, particularly in public administration. It teaches how to structure a diplomatic, point-by-point rebuttal letter that satisfies both expert reviewers and handling editors. Key strategies include reconciling conflicting demands, explicitly addressing every comment, and protecting the paper's core scholarly contribution and practical evidence.

This expert, interactive assistant provides comprehensive troubleshooting for complex issues encountered with Claude Code. It systematically scans the user's environment, checks global and project configurations, and cross-references symptoms against a deep knowledge base to pinpoint root causes and provide actionable, step-by-step solutions. Ideal for resolving integration errors, permission issues, or unexpected behavior.

This skill provides expert capabilities for professional HTML development, emphasizing semantic markup, WCAG accessibility, and modern HTML5 standards. Use it to build structurally sound, accessible, and responsive web pages, incorporating complex forms, multimedia (SVG, Canvas), and adhering to industry best practices.

This skill maps free-text laboratory and clinical observation names to standardized LOINC codes. It resolves ambiguous terms by considering the full six-axis model: Component, Property, Time, System (specimen), Scale, and Method. It integrates with FHIR terminology services to ensure accuracy, supporting US Core Laboratory Results and UCUM units.

This skill calculates and documents the residual re-identification risk of de-identified datasets. It goes beyond simple direct identifier removal by assessing the risk posed by quasi-identifiers (age, ZIP, dates). It computes structural metrics like k-anonymity and l-diversity, and runs empirical linkage attacks to provide a defensible memo supporting HIPAA Expert Determination for determining if a dataset is safe for release.