Skill UI

This guide details best practices for submitting research artifacts to academic conferences, following the ACM Artifact Review and Badging scheme. It covers everything from required packaging plans (Docker, README, data provenance) to achieving various levels of validation (Functional, Reusable, Reproduced). Learn how to structure your submission to ensure maximum reproducibility and pass rigorous evaluator checks on a clean machine.

Provides comprehensive guidelines for packaging supplementary materials, including code, data, model weights, and benchmarks, for major machine learning conferences like ICML. It details the requirements for both the double-blind review stage (focusing on anonymization and minimal replication commands) and the final public release package (ensuring permanent public links and clear licensing).

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.

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 guide provides rigorous best practices for developing and presenting theoretical economic models for top-tier journals. It emphasizes framing results with a clear economic payoff, maintaining minimal model setups, and ensuring that complex, full proofs are relegated to the online appendix. The goal is to ensure the primary economic insight remains highly visible and actionable, rather than being buried in algebraic machinery.

A comprehensive guide for packaging and evaluating research artifacts for academic conferences like SIGMETRICS. It details the requirements for achieving ACM Badges (Functional, Reusable, Reproduced) by ensuring the packaged simulation can regenerate key figures and results on a clean machine, moving beyond mere tool execution.

This skill provides a comprehensive framework for ensuring the mathematical verifiability of advanced algorithms papers (like those submitted to SODA). It guides authors to establish a complete, checkable argument, documenting every claim, explicit model assumption, constant status, and providing machine-checkable steps. It moves beyond simple claims of accuracy to mandate full, reproducible proof structures.

This guide outlines the rigorous process for submitting and evaluating research artifacts for academic conferences (TACAS/ETAPS). It details the mandatory two-round evaluation system, covering tool-demonstration papers and general research case studies. Authors must package their work to achieve badges like Available, Functional, and Reusable, ensuring maximum reproducibility and environmental compatibility on a clean virtual machine.

This guide details the methodology for packaging scientific artifacts (code, solvers, data, logs) for machine learning submissions. It focuses on creating double-blind, self-contained ZIP archives that allow reviewers to independently and quickly verify complex probabilistic claims, even under strict time constraints. It covers best practices for anonymity, structured README generation, and handling data/licensing.

A comprehensive guide for authors submitting to UAI (Uncertainty in Artificial Intelligence). It details how to position research by ensuring coverage across diverse academic communities, including Machine Learning, Statistics, Causal Inference, and foundational mathematics. Focuses on establishing the technical 'delta' rather than merely listing citations, helping authors avoid common review failures.

Scans your local machine's Git history to locate abandoned or unfinished side projects. It performs a deep 'autopsy' on these repositories, determining their causes of death (e.g., deployment fear, killed by newer ideas) and identifying personal developmental patterns. The tool then surfaces the most promising candidate for resurrection, helping you finally ship it.

Qiskit is a leading open-source quantum computing framework designed for building, optimizing, and executing quantum circuits. It provides comprehensive tools to run on local simulators or major cloud quantum backends, including IBM Quantum, IonQ, and Amazon Braket. Use it to develop algorithms in chemistry, machine learning, and optimization.