Skill UI

This tool guides the process of positioning a comprehensive review essay within a highly technical and debated academic field. It helps users define the precise scope (corpus boundaries), map the major scholarly camps and core disagreements, and strategically locate their own intervention point, ensuring the review reads as authoritative and current.

This guide details the standards for submitting theoretical computer science papers, focusing on the required "formal-claims package." It emphasizes that the deliverable is not a runnable systems artifact, but a rigorous academic package containing a complete, self-contained proof appendix, a clear mapping of every stated claim to its proof location, and a full, publicly available version on arXiv.

This guide details the rigorous process for packaging and evaluating mechanized proofs (e.g., in Coq, Isabelle, Lean, Agda) submitted for academic conferences. It outlines criteria for achieving Functional, Reusable, and Available badges, focusing heavily on completing the formal development, auditing axioms, and ensuring perfect reproducibility by pinning toolchains and mapping theorems to source files.

This guide provides strict guidelines for authors writing the "Related Work" section for top-tier programming language theory conferences like POPL. It emphasizes moving beyond general literature reviews to pinpoint precise, technical "delta sentences" that prove novelty against neighboring formal systems. It covers citation standards, venue mapping, and attribution verification for maximum academic impact.

This comprehensive guide details the rigorous standards required to defend the identification of parameters, estimands, or causal effects in quantitative economic research. It provides specific, advanced methodologies and checklists for three major branches: structural modeling, empirical causal design (e.g., DID, IV, RDD), and experimental studies. It ensures the work meets the high quantitative bar of top-tier journals by demanding explicit data-to-object mapping and robust diagnostic testing.

This router guides authors through the entire lifecycle of submitting an applied microeconomics or Industrial Organization (IO) manuscript to the RAND Journal of Economics (RJE). It acts as an entry-point dispatcher, mapping specific writing, analysis, and revisions (e.g., framing, identification, rebuttal) to the correct stage, ensuring the paper meets the journal's stringent standards.

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.

Provides comprehensive guidelines for achieving high levels of scientific reproducibility for academic papers, particularly those in systems metrics. It covers mapping claims to verifiable evidence, ensuring simulators are seeded and deterministic, and documenting measurement provenance to allow reviewers to independently verify all reported results.

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 comprehensive guide provides an end-to-end planning framework for USENIX Security Symposium submissions. It assists researchers in selecting the optimal submission cycle, mapping backward timelines from key milestones (like registration), assigning ownership for venue-specific risks (e.g., ethics, reproducibility), and coordinating through the complex post-submission decision process (Accept/Reject).

A comprehensive guide detailing the standards for scientific reproducibility in visualization research, particularly for IEEE VIS submissions. It outlines the Graphics Replicability Stamp Initiative (GRSI) requirements, covering how independent volunteers must reproduce results using provided code, data, and environments (e.g., Docker containers). Learn how to package your work—including determinism aids, mapping tables, and licensed data—to pass rigorous, clean-machine testing.

This guide details best practices for achieving high standards of reproducibility in visual analytics papers, focusing on open materials, verifiable code, and rigorous documentation. It covers mapping figures to data sources, preregistering studies, documenting data provenance, and defining the level of reproducibility achieved (e.g., turnkey, scripted).