Skill UI

A comprehensive guide detailing the entire academic review lifecycle for top-tier programming language conferences like PLDI. It covers every critical stage, from initial submission and double-blind review through author responses, final acceptance criteria, and post-publication artifact evaluation. Learn what Program Chairs look for, how to address soundness doubts, and how to optimize your paper for maximum impact.

This guide provides a detailed framework for researchers to determine the most suitable venue (e.g., PLDI, POPL, OOPSLA) for their programming language or systems research paper. It guides authors on whether their core evidence lies in theoretical proofs, working implementations with benchmarks, or empirical usage studies, ensuring optimal submission routing and maximizing acceptance chances.

This guide provides strict guidelines for authors writing the "Related Work" section for top-tier programming language theory conferences like POPL. It emphasizes moving beyond general literature reviews to pinpoint precise, technical "delta sentences" that prove novelty against neighboring formal systems. It covers citation standards, venue mapping, and attribution verification for maximum academic impact.

This guide helps researchers determine the optimal venue (like POPL, PLDI, OOPSLA) for their academic papers on programming languages. It advises authors on whether their core contribution is theoretical (proofs, semantics), implementation-focused (compilers, benchmarks), or empirical (user studies), ensuring their work reaches the most appropriate specialized reviewer community.

Covers best practices for writing highly technical academic papers, particularly in programming language theory. It guides authors on structuring their work to build a strong 'ramp' from initial concrete programs and informal motivation to rigorous formal proofs and theorems, ensuring clarity and avoiding common pitfalls like unmotivated formalism or ambiguous notation.

A comprehensive deep dive into the PPoPP conference review pipeline. This guide models the single-round, double-blind review process, detailing how submissions are evaluated by the Program Committee and External Review Committee. It covers critical aspects such as the author response rebuttal, automatic poster consideration, and the specific technical axes reviewers prioritize (e.g., scalability, concurrency correctness, baseline honesty) for parallel programming research.

A comprehensive guide detailing the rigorous standards required for submitting parallel programming research to top-tier academic venues. It covers crucial topics such as proving concurrency correctness, demonstrating measurable scalability using speedup curves, adhering to strict formatting (10 pages, two-column layout), and structuring the technical contribution to satisfy expert reviewers.

This comprehensive guide outlines the rigorous standards for creating publication-grade tables and figures for top-tier academic journals, such as The Review of Economic Studies. It emphasizes the principles of elegance, economy, and self-containment. Authors must ensure that every exhibit carries one single, clear result, presents interpretable magnitudes (not just stars), and is generated programmatically from code to guarantee reproducibility and consistency.

Cirq is a powerful, open-source Python framework developed by Google Quantum AI. It allows users to design, simulate, and execute complex quantum circuits. It supports advanced features including parameterized circuits, noise modeling, circuit transformation, and seamless integration with multiple quantum hardware backends (e.g., IonQ, Google Quantum, Pasqal).

This SDK allows asynchronous Python programming to control a live web browser, simulating user interactions for web automation, testing, and data extraction. Users can navigate pages, locate elements, fill forms, click buttons, and capture snapshots of the current page's content. It enforces a 'perceive → act → verify' cycle, making it ideal for building robust web scrapers and end-to-end testing tools.

This skill facilitates cross-version symbol migration and binary diffing. When existing symbols or reverse engineering results from an old version need to be quickly mapped to a new binary version, this tool is used. It leverages LLMs to perform structured structural comparisons between corresponding functions in different versions, enabling the programmatic transfer of function names and offsets even when original PDB files are lost. Ideal for kernel debugging, application updates, and binary analysis.

This skill helps developers programmatically discover, filter, and inspect the entire OpenMed model registry. It allows users to find the correct model key for specific clinical tasks (e.g., diseases, oncology, PII detection), filter by language, size, or task, and review model metadata before loading. It pairs with `loading-openmed-models`.