Skill UI

A comprehensive guide detailing the rigorous, journal-style review process for IEEE VIS full-paper submissions. It explains the two-phase evaluation structure, the significance of a 'Conditional Accept' (treating it like a major R&R), and how the process differs fundamentally from single-decision conference models (e.g., CVPR, CHI). Essential reading for authors navigating academic publication.

This guide models the comprehensive two-round, journal-style peer review process used at major computer vision conferences like WACV. It details the distinct stages: Round 1 outcomes (Accept, Revise and Resubmit, Reject), the optional R1 rebuttal phase, and the final R2 decision. It emphasizes that the most critical phase for authors is executing high-quality revisions during the Revise-and-Resubmit stage, rather than solely relying on rebuttals.

A comprehensive guide detailing when and how to structure theoretical models in development economics research, particularly for World Bank Economic Review submissions. It emphasizes that models must serve a functional purpose—such as interpreting empirical coefficients, running policy counterfactuals, or aggregating to welfare statements—rather than being decorative. Key principles cover parameter identification, matching structural frictions to the local setting, and handling counterfactual assumptions (Lucas critique).

Provides comprehensive guidelines for packaging and submitting research artifacts (datasets, models, code) for major conferences. It details the difference between mere 'existence' (badge verification) and true academic 'reproducibility,' covering archival strategies, web data licensing, takedown resilience, and minimal viable package structure for long-term scholarly impact.

This skill models the entire peer-review pipeline for Web Conferences (WWW), detailing the stages from abstract submission to final notification. It guides authors and reviewers on the implications of track-scoped double-blind review, emphasizing criteria such as Web-centricity, evidence scale, and ethical considerations required for acceptance.

This comprehensive guide outlines best practices for packaging research artifacts, including code, proprietary data logs, and models, for submission to top-tier academic venues. It addresses the crucial need for verifiability and reproducibility, providing strategies for handling limited space, proprietary datasets, and the absence of formal artifact grading systems, ensuring high academic credibility.

This guide teaches researchers how to strategically position a paper's contribution within established academic lineages (e.g., click models, LTR). It emphasizes moving beyond simple citation coverage to writing precise 'contrast sentences' that articulate the technical delta, ensuring the work's novelty is clearly defined relative to neighboring venues like SIGIR and KDD. Ideal for authors refining their technical writing for top-tier conferences.

This skill helps users determine which marketing efforts truly drive conversions and revenue. It covers interpreting various attribution models (First-touch, Last-touch, Data-driven) and reconciling conflicting data reported by multiple marketing tools. It also guides users on implementing first-party, self-hosted attribution tracking when they own the site or app.

This guide provides a structured framework for presenting complex empirical results in academic manuscripts, particularly for international marketing journals. It details the essential 'spine exhibits' (conceptual models, invariance tables, cross-national patterns) and teaches techniques like the 'claim test' and 'triage' to ensure arguments are clear, rigorous, and adhere to strict page limits.

An AI SDK specifically designed for Dart and Flutter applications. Genkit provides a unified interface for building sophisticated AI agents, defining complex data flows, and integrating various Large Language Models (LLMs) like Gemini, OpenAI, and Anthropic. Use it to manage code generation, structured outputs, tools, and complex operational workflows in Dart.

This skill generates high-quality images, textures, icons, and visual assets from text prompts using advanced AI models. It supports multiple providers, including OpenAI (DALL-E) and Google Gemini, offering flexibility in quality and speed. Ideal for rapid prototyping, game asset creation, artwork, and visual mockups across various projects. Requires configuration with an API key.

Statsmodels is Python's premier library for rigorous statistical modeling. It provides comprehensive tools for estimation, inference, and diagnostics across a wide range of statistical methods. Use this skill for advanced analysis, including linear regression (OLS, WLS), generalized linear models (Logistic, Poisson), and complex time series forecasting (ARIMA). Ideal for econometric studies and producing publication-ready results.