Skill UI

This comprehensive guide teaches authors how to structure and write professional rebuttals to reviewers' comments (R&R). It covers addressing complex critiques across theory contribution, advanced methodologies (e.g., HLM, CMV), measurement validity, and conflicting reviewer opinions. It ensures responses are highly specific, traceable, and academically rigorous.

A comprehensive workflow router designed to guide researchers through the entire process of preparing manuscripts for top-tier business journals, such as the Nankai Business Review. This guide does not replace specialized skills but provides a structured, stage-gated approach covering theory gap identification, hypothesis development, rigorous measurement validation (e.g., SEM, validity), contextualization, and strategic revision (R&R). It ensures that the paper meets the necessary standards for theoretical contribution and empirical depth.

A comprehensive guide detailing the high standards required for presenting security evidence at major conferences. It covers methodologies for proving claims—including attacks, defenses, and detection systems—across live internet measurements, controlled testbeds, and emulation. Key requirements include adaptive adversary modeling, rigorous measurement hygiene, and adherence to ethical review protocols to ensure results are reproducible and relevant to real-world networks.

Provides comprehensive guidelines for packaging and presenting research artifacts (code, data, models, benchmarks) submitted to NeurIPS. It details requirements for anonymous review packages, public release, and ensures scientific claims are reproducible by providing necessary metadata, licenses, and execution details.

A comprehensive guide on structuring research papers for top-tier AI conferences (like NeurIPS). It teaches authors how to strategically allocate contributions, proofs, data details, and code artifacts across the main body, text appendices, and separate ZIP files. Use this skill to ensure reviewers can easily identify the core contribution and assess the work's validity without missing crucial supporting details.

A comprehensive guide for structuring and defending academic research designs, particularly for New Media and Society studies. It provides rigorous criteria for qualitative ethnography, content/discourse analysis, computational data validation, and mixed methods integration. The focus is on strengthening the theoretical argument by justifying choices, validating measures, and defeating leading alternative readings.

A comprehensive guide for authors preparing their research paper for the final camera-ready submission at a major academic conference. It details the entire workflow, covering transforming anonymous drafts into public, presented papers. Key steps include de-anonymizing content while adhering to strict page limits, managing public artifact releases (code/data), designating presenters, and meeting critical deadlines for open-access proceedings.

This guide outlines the rigorous standards for writing top-tier distributed systems conference papers. It details the mandatory narrative arc—moving from an operational pain point to a named design principle—and emphasizes that every claim must be supported by precise, measured evidence (e.g., percentiles and specific workloads) rather than relying on superlatives or vague adjectives. It also provides guidance on structuring the argument, budgeting page space, and designing figures as core evidence.

This skill provides robust functionality to resolve free-text scientific labels into standard ontology term IDs (CURIEs) and validate existing identifiers against authoritative databases. It is essential for metadata curation, ensuring that submitted biological data (e.g., tissue types, diseases, assays) uses current, correct, and non-obsolete identifiers, thereby maintaining high data quality for large-scale scientific datasets.

This guide provides a structured approach for writing high-stakes author responses to academic paper reviews (like those in OOPSLA). It teaches authors how to move beyond simple rebuttals by framing every concern as an argument supporting a desired outcome (Accept, Minor Revision, Major Revision, or Reject). Focus areas include quantifying feasibility, structuring the argument for committee skimming, and strategically shaping the scope of required revisions.

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.

A comprehensive guide for authors aiming to submit top-tier technical papers (like those for OOPSLA). It teaches how to transform descriptive drafts into highly verifiable, structured academic arguments. Focus areas include developing running examples, articulating falsifiable claims, optimizing article pacing, and anticipating reviewer challenges to maximize the chances of acceptance.