Skill UI

This guide provides comprehensive best practices for packaging and documenting NLP research artifacts (datasets, models, prompts, etc.) for academic submission (e.g., NAACL/ARR). It emphasizes rigorous provenance tracing, ensuring reproducibility, and ethically handling sensitive data, particularly community-owned or indigenous language resources.

This guide provides an expert methodology for designing and auditing Natural Language Processing (NLP) experiments for top-tier academic submissions. It instructs researchers on how to map meta-review claims to measurable evidence, ensure budget-fair comparisons, and systematically address potential reviewer probes (scope, fairness, contamination, etc.) to maximize publication rigor.

This guide outlines the stringent standards for ensuring the reproducibility of Natural Language Processing (NLP) research. It teaches authors how to treat the Responsible NLP checklist as a binding contract by meticulously 'pinning' every variable—including model identifiers, API access dates, prompt strings, random seeds, and evaluation metrics. It details creating a comprehensive reproducibility manifest to make research results verifiable and defensible.

This skill serves as a detailed guide for authors submitting theoretical articles in syntax or morphology to Natural Language & Linguistic Theory (NLLT). It outlines the journal's specific expectations, emphasizing that formal analyses must be deeply grounded and rigorously tested using rich, representative, and often cross-linguistic empirical data. Use this tool to assess the methodological rigor, scope fit, and structural requirements of your manuscript against NLLT's high academic standards.

This guide provides a framework for designing and auditing the experimental rigor of academic papers, particularly in systems and programming languages. It emphasizes the crucial link between a claim type (e.g., 'Faster') and the required evidence type (e.g., benchmarks, user studies, formal proofs). Learn how to establish robust baselines, define clear workload rules, size experiments for conference cycles, and ensure statistical compliance for maximum impact.

A comprehensive guide for authors preparing the 'Related Work' section for top-tier academic conferences focused on programming languages, such as OOPSLA. It details rigorous citation standards (PACMPL journal format), mandates comprehensive venue coverage (POPL, PLDI, ICFP, etc.), and emphasizes identifying precise technical differences ('deltas') between the proposed work and existing literature, moving beyond mere inventory listing.

This guide provides comprehensive best practices for achieving scientific reproducibility in systems and programming language papers, following stringent SIGPLAN guidelines. It details the necessary rigor for documenting hardware, managing complex runtime measurements (like JIT warmup and variance), and maintaining a detailed, machine-readable provenance ledger and data-availability statement.

This comprehensive guide assists reviewers and authors in determining the most appropriate venue for programming language research within the SIGPLAN community. It provides a detailed 'center-of-mass' test, helping categorize contributions based on whether they emphasize metatheory (POPL), performance engineering (PLDI), functional techniques (ICFCP), or the broad design scope characteristic of OOPSLA. It guides the decision-making process for academic submissions.

This guide provides best practices for designing and auditing academic evaluations of programming language features and compilers, following standards used in venues like PLDI. It covers selecting defensible benchmark suites, establishing strong baselines, performing targeted ablations, and measuring multiple performance metrics (runtime, compile time, memory) to ensure research claims are reproducible and scientifically sound.

A comprehensive guide detailing the entire academic review lifecycle for top-tier programming language conferences like PLDI. It covers every critical stage, from initial submission and double-blind review through author responses, final acceptance criteria, and post-publication artifact evaluation. Learn what Program Chairs look for, how to address soundness doubts, and how to optimize your paper for maximum impact.

This guide provides a detailed framework for researchers to determine the most suitable venue (e.g., PLDI, POPL, OOPSLA) for their programming language or systems research paper. It guides authors on whether their core evidence lies in theoretical proofs, working implementations with benchmarks, or empirical usage studies, ensuring optimal submission routing and maximizing acceptance chances.

This skill guides authors on assessing the suitability of their general biology manuscript for PLOS Biology. It covers the journal's high standards for scientific rigor, data availability, mandatory reporting guidelines (e.g., ARRIVE, CONSORT), and the structure of a plain-language Author Summary. Use this tool for venue selection and optimizing your submission to maximize publication chances.