Skill UI

A comprehensive guide for authors submitting to academic conferences, addressing strict page limitations where appendices count against the core budget. It details best practices for distributing supplementary evidence across three external homes: the main paper, dedicated arXiv technical reports, and artifact repositories. The guide emphasizes maintaining self-containment, managing version discipline, and strategically cutting content to ensure the core claims are reviewable.

This comprehensive guide provides researchers with structured methodologies for designing and auditing Computer Vision (CV) experiments for major conferences like WACV. It details the critical differences between the Applications and Algorithms tracks, emphasizing the need to report performance under real deployment constraints (e.g., power, latency) and ensuring fair, rigorous comparisons with baselines. Ideal for revising submissions to meet high review standards.

A comprehensive guide for strengthening the reproducibility of academic papers, especially those involving systems claims (e.g., power usage, latency). It details maintaining a 'recipe ledger' for all reported metrics, ensuring constraint-aware measurement of environmental variables, and managing data/artifact consistency across the entire two-round revision process.

A detailed guide for structuring and positioning development economics research for high-impact journals like The World Bank Economic Review (WBER). It teaches researchers to reframe their work to satisfy two crucial criteria: rigorous empirical design (credible empirics) and actionable policy relevance in developing countries, ensuring the work transcends being a mere project report.

This guide provides best practices for optimizing the presentation of data exhibits—including tables, figures, and qualitative displays—in development economics manuscripts. It ensures that complex findings are highly legible and actionable for a multidisciplinary, policy-oriented audience, moving beyond simple statistical reporting to communicate clear, impactful narratives.

This skill provides comprehensive norms for executing and reporting robust, reproducible data analysis in comparative, cross-national, or formal-empirical studies. It guides authors on honestly reporting uncertainty, ensuring statistical robustness, performing rigorous cross-national inference, and integrating qualitative evidence through triangulation to meet the demanding standards of top academic journals.

This guide provides a comprehensive framework for designing rigorous and defensible evaluation sections for machine learning and information retrieval papers, particularly for top-tier conferences like WSDM. It covers the four critical evidence quadrants—Effectiveness, Validity, Efficiency, and Robustness—and details necessary protocols, such as temporal splits, proper baseline selection (including heuristics), ablation studies, and hardware-aware performance reporting. Use this when auditing or writing the experimental section of a research paper.

A comprehensive framework for ensuring reproducibility in Web Search and Data Mining (WSDM) research. It addresses unique challenges associated with behavioral data, covering data provenance, temporal splitting discipline, bias assumption reporting, statistical rigor (seeds, variance), and the 'honesty ladder' for non-reproducible results, helping researchers publish robust and verifiable work.

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 tool assists researchers in assembling and rigorously validating a complete replication package for academic submission. It checks the master script, pinned environment, data plan, and methodology against specific journal policies, ensuring that every reported number is reproducible and deterministic, preventing 'my machine' errors.

Provides a comprehensive, step-by-step workflow for conducting rigorous literature reviews across academic, biomedical, and technical domains. It guides users through defining research questions (PICO), developing systematic search protocols, evidence logging, source screening, structured data extraction, and synthesizing findings to build high-quality, reproducible review reports.