Skill UI

A comprehensive guide for academic authors on designing high-quality, ethically sound, and publication-ready exhibits for anthropology manuscripts. It covers technical requirements (file formats, figure caps), ethical obligations (consent, heritage rights, dignity), and scholarly presentation principles, ensuring exhibits strengthen the argument and withstand peer scrutiny.

This guide helps transform rich but purely descriptive fieldwork, archaeological data, or linguistic observations into bold, transferable, and debatable theoretical arguments. It teaches how to explicitly define concepts, establish interpretive mechanisms, set scope conditions, and incorporate positionality, ensuring the work is robust enough to withstand academic critique.

This comprehensive skill guides researchers through the critical ethical framework for anthropological and social science manuscripts. It emphasizes 'do no harm,' continuous informed consent, community accountability, and proper handling of sensitive data, cultural heritage, and human remains. It ensures that all data sharing and knowledge reuse plans prioritize the rights, privacy, and well-being of interlocutors above academic publication needs.

A comprehensive guide for drafting and polishing academic manuscripts intended for Current Anthropology (CA) publication. It ensures the article is highly readable by a global, non-specialist audience, minimizes technical jargon, and adheres strictly to CA's house style (Chicago author-date, strict word caps for Major Articles/Reports, and abstract length). Ideal for final revisions, structuring arguments, and achieving maximum scholarly impact.

A comprehensive guide for authors aiming to submit manuscripts to Current Biology. This skill helps assess the manuscript's scope, novelty, and completeness against the journal's high standard. It covers journal positioning, structure requirements (STAR Methods, figure quality), and disciplinary fit across fields like ecology, neuroscience, and evolution, guiding authors on how to frame their work for a broad scientific readership.

A comprehensive guide for authors submitting research to CVPR, detailing best practices for packaging code, models, and datasets. It covers requirements for review-time reproducibility (sealed-box rules) and long-term public release (licensing, provenance, and version pinning), ensuring that the work is runnable by any skeptical third party.

A comprehensive guide for designing, auditing, and executing rigorous experimental programs for top-tier computer vision papers (e.g., CVPR). It covers essential scientific components, including establishing fair benchmarks, structured ablation studies, documenting failure modes, quantifying computational efficiency, and demonstrating cross-dataset generalization, ensuring the research is reproducible and robust.

A comprehensive guide on structuring and writing the 'Related Work' section for elite computer vision conferences like CVPR. It addresses the high velocity of academic literature, teaching advanced methods like defining explicit technical deltas, structuring arguments by research question, maintaining citation hygiene, and handling complex reviewer feedback on originality and concurrency.

A comprehensive guide for hardening deep learning papers, particularly in Computer Vision (CVPR). It details best practices for achieving rigorous reproducibility by utilizing the Compute Reporting Form (CRF), maintaining a detailed recipe ledger, ensuring benchmark split hygiene, and meticulously documenting all training hyperparameters, seeds, and variance analyses for academic publication.

A comprehensive model simulating the entire academic paper review cycle, specifically tailored to large-scale top-tier conferences like CVPR. It details the timeline, roles of reviewers and Area Chairs, the impact of mechanisms (like the LLM ban), and strategic advice for authors navigating the submission, rebuttal, and decision-making phases.

A comprehensive audit checklist for submitting papers to CVPR. This guide covers crucial deadlines (abstract, full paper, supplement), strict page limits (8 pages including figures), adherence to anonymity rules, and mandatory compliance with the Compute Reporting Form (CRF). Essential reading for authors preparing research submissions for major computer vision conferences to avoid desk rejection.

A comprehensive guide for authors submitting to top-tier computer vision venues like CVPR. It details how to write highly concise, visually impactful, and rigorous academic papers that pass the 'two-pass reader' test. Focus areas include effective figure design, managing strict page budgets (including figures/tables), calibrating claims to avoid overstatement, and adopting professional technical writing norms.