Skill UI

A comprehensive guide for placing art historical arguments within the broader context of scholarly debate and historiography. It teaches users how to trace foundational scholarship, identify precise gaps in existing research, and clearly establish the intellectual novelty of their contribution for a generalist academic audience.

Use the Codebase Memory graph to explore unfamiliar code, map architecture, find symbols and relationships, trace callers and data flow, assess impact, and audit dead or complex code, with Scout, Verify, and Auditor evidence levels.

This guide outlines the stringent standards for academic paper reproducibility in theoretical machine learning (e.g., COLT). It provides a comprehensive pre-submission audit checklist covering formal proof completeness, assumption bookkeeping, constant tracking, and ensuring numerical illustrations are fully traceable. Use this methodology to strengthen your theoretical claims and withstand rigorous peer review.

This skill reconstructs the full narrative behind a piece of code by analyzing local Git history. Instead of just naming the last author (like git blame), it traces the file's origin, tracks subsequent changes, identifies related companion files, and extracts intent clues from commit messages. Use this when you need to know *why* code exists before refactoring, making risky edits, or understanding complex system behavior.

Provides rigorous guidelines for conducting robust, verifiable, and reproducible evaluations in networking research. It emphasizes moving beyond simple simulations by demanding the use of real target platforms, documenting full system provenance (traces and configs), establishing honestly tuned baselines, and reporting detailed measurement statistics and uncertainty. Essential for authors aiming for top-tier conference submissions.

A comprehensive guide for authors submitting networking research to CoNEXT. It outlines best practices for achieving technical reproducibility, focusing on pinning traces, configurations, environments, and pipelines. Learn the critical timeline for optional ACM badging and how to maintain an honest data-availability posture for robust academic review.

A comprehensive guide for designing and auditing empirical research in CSCW. It emphasizes methodological pluralism, defining distinct standards of rigor for various techniques like ethnography, trace analysis, surveys, and mixed methods. The guide mandates pre-design planning, explicitly addressing ethical concerns, unit of analysis, and aggregation logic before data collection.

This guide provides comprehensive standards for achieving auditability and transparency in Computer-Supported Cooperative Work (CSCW) research. It teaches researchers how to document non-shareable evidence, such as field interviews, log traces, and system deployments, by focusing on detailed analysis trails, decision logs, and rigorous process documentation, thus strengthening academic rigor.

This guide outlines rigorous standards for incorporating theoretical and quantitative models into economic policy manuscripts. It emphasizes that models must be minimal, transparent, and directly bear policy weight—converting estimates into statements about welfare, fiscal costs, or clear mechanisms. It teaches how to design models that pass 'the necessity test,' ensuring policymakers can trace headline numbers directly back to key, identifiable assumptions.

This guide outlines comprehensive standards for creating self-contained, reproducible, and highly professional tables and figures for top-tier academic journals. It covers best practices for econometric reporting, including Monte Carlo error presentation, consistent precision, adhering to booktabs style, and ensuring every piece of data is traceable back to a fixed script and seed.

A deep dive into the best practices for drafting the Related Work section for top-tier NLP conferences (like EMNLP). This guide instructs authors on how to trace task lineage, verify dataset ancestry, manage concurrent work citations (arXiv vs. Anthology), and ensure rigorous citation hygiene to withstand expert reviewer scrutiny.

A comprehensive guide for planning rigorous scientific studies in Earth and Planetary Sciences, covering sampling, analytical campaigns, experimental runs, and modeling. It ensures that research designs are robust, addressing critical elements like sample context, traceability to reference materials, replication levels, and falsifiability before data collection begins. Essential for high-impact scientific publishing.