Skill UI

Simulates a highly structured, sequential peer-review process using multiple specialized AI agents (e.g., Skeptic, Constraint Guardian, User Advocate). This skill is designed to rigorously stress-test a design, surface hidden assumptions, identify potential failure modes, and enforce non-functional constraints before implementation. It transforms initial concepts into robust, validated specifications by simulating formal critique and achieving consensus via a mandatory Decision Log.

This skill automates the creation of high-quality, structured pull request descriptions. By analyzing git diffs, commit lists, or raw changes, it generates comprehensive sections covering the summary, motivation, specific changes, detailed testing procedures, and crucial reviewer guidance. Use it to ensure every code change is fully documented for effective peer review.

A comprehensive guide detailing the Annals of the American Association of Geographers (AAAG) review process. It covers double-anonymous peer review, subject editor criteria, common pitfalls (such as lack of theory or poor cartography), and how to interpret decision letters to maximize acceptance chances. Use this before submission to stress-test your manuscript and improve scholarly rigor.

Provides a comprehensive guide to the American Educational Research Journal (AERJ) manuscript review process. It details the stages, including fit framing, editorial screening, and double-anonymous peer review, helping authors understand how to structure their work to meet rigorous field-wide academic standards and maximize publication chances.

A comprehensive guide for researchers submitting to ASE (Automated Software Engineering). It details mandatory requirements for ensuring research reproducibility, covering the Data Availability Statement, providing anonymized and runnable tools, pinning system provenance (commits, versions, SHAs), and managing cached LLM outputs. Essential for passing rigorous peer review.

This guide provides comprehensive strategies for drafting and revising highly technical academic papers for top-tier conferences like ASPLOS. It focuses on structuring the core argument around cross-layer insights, ensuring claims are quantifiable and bounded, and optimizing the entire manuscript to survive rapid peer review and strict page limitations.

This guide details the rigorous peer review process used by top sociology journals, such as the American Sociological Review (ASR). It teaches authors how to prepare manuscripts for anonymous, cross-disciplinary evaluation by preempting common failure points related to broad significance, methodological rigor, and evidence presentation.

This guide details the comprehensive review process for Contemporary Accounting Research (CAR), covering double-anonymous peer review, the roles of the Subject Editor and Editor-in-Chief (EIC), and the formal appeals process. It helps authors understand how manuscripts are evaluated, interpret decision letters (Reject, R&R, Acceptance), and prioritize revision responses based on the Subject Editor's guidance.

This guide outlines the stringent standards for academic paper reproducibility in theoretical machine learning (e.g., COLT). It provides a comprehensive pre-submission audit checklist covering formal proof completeness, assumption bookkeeping, constant tracking, and ensuring numerical illustrations are fully traceable. Use this methodology to strengthen your theoretical claims and withstand rigorous peer review.

A comprehensive final checklist for developmental psychology manuscripts submitting through Editorial Manager (APA). It guides authors through critical areas like length-tier compliance, developing credible developmental claims (JARS), transparency (TOP), and preparing for masked peer review, ensuring submission quality before final upload.

This resource details the rigorous evaluation process for Global Change Biology (GCB) submissions. It covers the criteria for high-bar desk rejection (scope, significance) and the depth of expert peer review. It is designed to guide authors on strengthening their manuscripts by pre-empting reviewer concerns, ensuring data reproducibility, and optimizing scope fit.

This comprehensive guide outlines the strategic approach for authors revising an academic article following a 'revise-and-resubmit' (R&R) decision. It teaches how to synthesize conflicting feedback from diverse reviewers (e.g., textual scholars, theorists, ethnographers), ensuring every comment is addressed point-by-point. The goal is to strengthen the argument while protecting the core field significance and maintaining scholarly anonymity.