Skill UI

A comprehensive guide for constructing non-empirical, theoretical figures for sociological theory manuscripts. It details how to design conceptual exhibits—including mechanism diagrams, process models, typologies, and concept maps—that carry theoretical work. Crucially, it emphasizes that these figures must illustrate mechanisms or classifications, and must never include raw data, regression tables, or empirical results.

This skill provides a comprehensive framework for ensuring the mathematical verifiability of advanced algorithms papers (like those submitted to SODA). It guides authors to establish a complete, checkable argument, documenting every claim, explicit model assumption, constant status, and providing machine-checkable steps. It moves beyond simple claims of accuracy to mandate full, reproducible proof structures.

This comprehensive guide details how to architect a lengthy, complex academic paper into a single, highly navigable PDF. It establishes a two-tier document model, separating self-contained 'Front Matter' from verifiable 'Back Matter.' Learn best practices for structuring proofs, ensuring dependency order, and implementing critical navigation aids (like claim indexes and TOCs) so reviewers can follow the argument flow seamlessly, even without traditional supplementary files.

This resource models the complex, multi-stage review process for top-tier academic conferences, such as SOSP. It details the journey of a paper from initial submission through staged reviews, author rebuttal, and the final live committee decision. It helps users understand the structural dynamics of scholarly evaluation and how to strategically respond to reviewers' critiques.

A comprehensive guide for executing and reporting complex social psychological analyses required for expert peer review. This skill covers norms for reporting uncertainty, assessing measurement quality, ensuring robustness checks, selecting appropriate statistical inference (e.g., multilevel models, clustering), and linking statistical findings back to the theoretical structure. It emphasizes methodological rigor and reproducibility.

A comprehensive model detailing the rigorous evaluation process for submissions to STOC (Symposium on Theory of Computing). It covers the committee structure, the roles of program committee members and external subreviewers, the key evaluation axes (Correctness, Significance, Breadth, Novelty), the typical timeline, and strategic advice for authors on maximizing their leverage throughout the double-blind review process.

A comprehensive guide detailing the complex submission and evaluation pipeline for TACAS (Tools and Algorithms for Scientific Applications). It models key features such as category-specific blind review, the mandatory parallel artifact evaluation for tool papers, and the single-round conference decision-making process, helping authors strategically prepare their submissions.

This comprehensive guide details the rigorous review process used by top academic journals. It explains the tiered editor structure, the critical double-blind review model, and how to interpret various decision letters (desk reject, reject, revise and resubmit). It helps authors set realistic expectations, understand professional academic integrity rules, and prepare best practices for manuscript submission.

This skill processes unstructured, rambling, stream-of-consciousness input (like dictation or brainstorming) into a highly structured 'echo' audit. It captures the user's core mission, locked decisions, open questions, and all model inferences, ensuring the user verifies the model's belief, not just the stated doubts. Ideal for refining fuzzy ideas before committing to work.

A comprehensive guide for designing, auditing, and reporting advanced statistical and causal inference experiments. It emphasizes moving beyond simple accuracy metrics to quantify uncertainty, model robustness, and causal mechanisms. Covers metrics like coverage, KL divergence, and regret, and outlines best practices for seeding, compute fairness, and creating standardized reporting blocks (e.g., UAI standards).

This guide details advanced best practices for achieving scientific reproducibility in probabilistic modeling and deep learning research. It mandates rigorous disclosure of seeds, hyperparameters, diagnostic traces (e.g., R-hat, ELBO), and compute environments to ensure that all claims made in academic papers are verifiably backed by traceable evidence. It elevates reproducibility from a simple code run to a comprehensive audit.

This guide models the entire lifecycle of academic conference submissions, detailing stages like bidding, review writing, author response, and decision-making. It helps researchers strategically plan their work to maximize impact, preemptively address potential weaknesses, and optimize their research communication strategy in a competitive academic environment.