Skill UI

A comprehensive guide for managing the highly critical stage of journal revision. This skill helps researchers decode editor feedback, strategically decide on necessary data collection (e.g., new countries, invariance tests), and structure a professional, point-by-point response letter that addresses every reviewer comment effectively while maintaining scholarly rigor.

This skill provides a rigorous framework for designing academic studies, particularly for high-impact psychological journals. It guides researchers to proactively address common methodological pitfalls such as common-method variance (CMV), unmodeled nesting, and construct validity gaps. Topics covered include temporal separation, multi-level modeling, advanced causal inference techniques (DiD, IV), and pre-registration best practices, strengthening the plan before data collection.

Provides comprehensive methodological guidelines for designing rigorous studies in public policy and marketing. The framework covers both prospective (e.g., realistic experiments) and retrospective (e.g., DiD, RDD) evaluation designs, emphasizing the necessity of establishing a strong counterfactual basis and ensuring policy relevance in data collection and analysis.

This guide provides methodological standards for defending the empirical design of a language manuscript. It covers various subfields—including fieldwork, corpus construction, phonetics, and typological comparison—ensuring that the evidence base is transparent, rigorous, and directly supports the stated theoretical claims. Use this when justifying data collection methods or addressing reviewer concerns about evidence validity.

Provides a comprehensive, milestone-based project management guide for submitting a paper to the IEEE PerCom conference. It outlines the entire workflow, from initial venue fit and securing human-subjects data collection to final camera-ready submission, emphasizing the single annual deadline and the critical importance of backward planning for successful academic publishing.

A structured framework designed for authors writing comprehensive psychological reviews or theoretical statements. It guides users to transform a collection of research papers into a single, cohesive, and provocative argument by imposing a unifying structure—such as a taxonomy, theoretical model, or question sequence—that defines the field's central claim.

A comprehensive guide and fit test designed to help researchers assess whether their empirical psychology project meets the high standards of top-tier journals like Psychological Science. It evaluates key criteria including broad impact, statistical robustness, open science readiness (preregistration/data sharing), and adherence to strict format limits. Use this skill to pressure-test your idea before committing resources to data collection or analysis, guiding you toward the optimal manuscript type (e.g., Research Article vs. Registered Report).

This comprehensive guide details the rigorous methodology for designing, executing, and reporting experimental results in Information Retrieval (IR). It covers best practices for selecting appropriate test collections, choosing defensible metrics, performing proper statistical significance testing (e.g., paired t-tests), ensuring baseline fairness, and proactively addressing modern pitfalls like LLM contamination. Essential for publishing high-quality, auditable research in IR/NLP.

A comprehensive guide for ensuring the reproducibility of Information Retrieval (IR) research, essential for SIGIR submissions. It details best practices for pinning data collection versions, index settings, neural model hyperparameters, and managing non-determinism. It helps researchers document artifacts—not just promises—to validate reported gains.

This skill guides authors on positioning information retrieval (IR) work for the Text REtrieval Conference (TREC). It functions as a venue-selection and manuscript re-framing tool, ensuring the submission aligns with TREC's focus on shared evaluation tracks, test collections, and NIST-style benchmarking. It helps authors structure evidence, understand reviewer expectations, and distinguish the work from general CS conference submissions.

A comprehensive, multi-phase methodology for debugging complex software issues. This guide mandates systematic root cause investigation, starting from symptom collection, code path tracing, hypothesis formulation, and deterministic reproduction. It teaches developers to fix the underlying cause, not just the visible symptoms, ensuring highly robust and minimal changes.

A comprehensive, multi-phase workflow guide for authorized reverse engineering of complex network protocols. Ideal for analyzing proprietary formats such as custom TCP/UDP binary protocols, Protobuf, gRPC, WebSocket frames, and reconstructing state machines from raw PCAP data. This guide covers data collection, field layout restoration, serialization recovery, and secure replaying.