Skill UI

This skill guides the transformation of visual observations and documentary evidence into a sustained, contestable academic thesis, suitable for high-level scholarly publication. It teaches how to explicitly state a claim (the thesis), define its scholarly stakes, and rigorously warrant every interpretive leap using evidence chains and anticipating counter-readings. Use this when a draft is descriptive but lacks a clear, disputed argument.

This skill guides art historians on gathering, weighing, and citing non-visual, documentary, and material evidence for academic articles. It covers citing primary sources (archives, contracts), reconstructing provenance chains, interpreting technical reports, and ensuring that all scholarly claims are rigorously traceable to verifiable sources rather than mere assertion.

This skill guides authors through the rigorous process of securing illustrations and reproduction rights for art-historical publications. It covers identifying rights holders, differentiating between fair use and paid permissions, securing world rights, and prepping high-resolution assets (e.g., 300 dpi TIFFs). Crucially, it emphasizes starting the clearance process early, in parallel with writing, to avoid publication delays.

This guide details the rigorous double-blind peer review process used by major art history journals. It helps authors understand what expert referees and editors focus on—such as the originality of the argument, close visual analysis, and documentary evidence—to pre-empt common objections and significantly improve manuscript quality before submission. It emphasizes mastering academic standards and anonymity protocols.

This comprehensive guide details the process of revising a scholarly manuscript after an R&R decision. It instructs authors on how to structure their revision and response letter to referees, ensuring that all feedback is addressed point-by-point while maintaining the academic rigor, double-blind anonymity, and integrity of the core argument. It emphasizes strategic conflict resolution and managing new image permissions.

This comprehensive guide serves as the final pre-submission checklist for authors submitting to The Art Bulletin. It details critical requirements for maintaining double-blind anonymity, proper file structure, and adherence to specific academic constraints, including word counts, citation styles (Chicago), and image handling. It ensures submissions are in the required Microsoft Word format, preventing common rejection triggers.

This guide outlines the rigorous methodology of 'close looking' crucial for academic art writing, particularly for Art Bulletin articles. It teaches how to move beyond mere description by systematically analyzing composition, facture, medium, and scale. Every visual claim must be supported by reproducible evidence, such as numbered figures or detail crops, ensuring the argument is grounded in verifiable observation, not mere assertion.

This comprehensive guide details the strict academic formatting required for submitting art historical articles, particularly for journals adhering to The Chicago Manual of Style. It covers the use of CMOS endnotes (not footnotes), precise descriptive prose, and mandatory, structured formatting for captions, credit lines, and figure references.

A comprehensive guide for researchers and reviewers designing or auditing Automated Software Engineering (ASE) evaluations. It outlines the necessary rigor, including the use of real subject systems, establishing fair and runnable tool baselines, performing targeted ablations (especially for LLMs), defining explicit oracles, and ensuring full data and model provenance to guarantee reproducible and scientifically sound results.

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.

A comprehensive guide for authors aiming to submit machine learning manuscripts to the Asian Conference on Machine Learning (ACML). This skill helps evaluate manuscript fit, re-frame research from journal or arXiv styles, understand required contribution types (e.g., algorithms, theorems, benchmarks), and align the paper's evidence bar with the expectations of a top-tier, regional AI/ML venue. Use it for venue selection and submission strategy development.

This guide outlines the rigorous, multi-stage workflow required to convert a conference acceptance or major revision into a final, publishable ACM-formatted paper. It covers critical steps including de-anonymization (restoring author details and citations), completing ACM rights forms, integrating archived artifacts, and managing the strict six-week timeline. Essential reading for authors ensuring their work meets high-quality publishing standards.