Skill UI

A comprehensive guide for building robust causal identification strategies in empirical research, particularly for analyzing policy shocks. It provides best practices and checklist items for advanced econometrics methods like Difference-in-Differences (DID), Instrumental Variables (IV), Regression Discontinuity (RDD), and Double Machine Learning (DML), helping researchers avoid common methodological pitfalls in top-tier academic publications.

A comprehensive guide for authors targeting Nature Machine Intelligence. It outlines the high standard required for AI/ML research, emphasizing conceptual significance, real-world impact, interdisciplinary relevance, and rigorous ethical and methodological depth. Use this tool to assess fit and framework your submission for a Nature-family audience.

This skill provides a comprehensive framework for authors targeting top-tier AI conferences like NeurIPS. It guides researchers in refining their manuscript's narrative, ensuring the contribution meets the venue's standards for broad, high-novelty machine learning research. It covers topic fit, evidence bar requirements, structuring the argument, handling interdisciplinary claims, and navigating the specific submission cycle rules for maximizing acceptance chances.

This skill guides authors on how to strategically position their machine learning research against complex academic landscapes. It covers comparing work to neighboring conferences (ICML, ICLR), analyzing contemporaneous preprints, and defining the exact technical delta from prior methods to minimize novelty risk for top-tier AI venues.

A comprehensive guide for authors submitting to machine learning conferences like NeurIPS. It details the rigorous standards for data splitting, hyperparameter reporting, random-seed protocols, and compute transparency required for a main-track paper. It also helps authors strategically decide between the main track and the MLRC/TMLR routes based on their core contribution (replication vs. novelty).

This skill simulates the rigorous peer review process used in top-tier AI and machine learning conferences (like NeurIPS). It helps users diagnose potential weaknesses in a submission regarding quality, clarity, significance, ethical compliance, and reproducibility. It guides the identification of decision-relevant issues that ACs and reviewers are likely to raise, helping authors strategically plan responses for revision.

This skill acts as a comprehensive router for the NeurIPS academic workflow. It guides users through every stage of preparing a machine learning manuscript, from initial topic and track selection to writing, mandatory paper checklist completion, OpenReview submission, handling the double-blind review process, and finalizing the camera-ready version. It helps ensure the manuscript is robust, evidence-backed, and adheres to all conference policies.

A comprehensive guide for refining machine learning research papers tailored for NeurIPS submission. It teaches how to structure introductions, calibrate claims with evidence, explicitly state limitations, and address mandatory academic checklist items (e.g., reproducibility, broader impact). Use this skill to transform technical writing into a reviewer-proof, high-impact academic manuscript.

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 outlines the entire lifecycle of submitting and revising academic papers for major conferences like OOPSLA. It details strategic decisions, such as choosing between October or March submission rounds, managing the state machine (Accept, Minor, Major Revision, Reject), budgeting time for necessary revisions, and planning post-publication artifacts and talks. Essential for academic research teams.

This guide outlines the rigorous analytical methodology required for Operations Research (OR) manuscripts. It instructs users on selecting the appropriate mathematical machinery—such as duality, dynamic programming, convergence analysis, or concentration inequalities—to establish provable guarantees, approximation factors, and algorithmic complexity. It ensures that claims are backed by rigorous proofs, moving beyond mere heuristics.

Provides a rigorous framework for ensuring the reproducibility of systems research presented at conferences like OSDI. It mandates maintaining a comprehensive, machine-readable provenance ledger that records hardware specifications, software dependencies, workload traces, and measurement policies throughout the entire experimental lifecycle, preventing 'artifact drift' and ensuring verifiable results.