Skill UI

A comprehensive guide for authors preparing accepted academic papers for artifact evaluation at major conferences (e.g., NSDI). It details how to package code, traces, and testbed recipes to ensure permanent public availability, achieve recognition badges (Functionality, Availability, Reproducibility), and meet community award criteria. Learn best practices for reproducibility and structured submission.

This guide provides authors with comprehensive best practices for submitting research artifacts (e.g., compilers, benchmarks, proofs) for post-acceptance evaluation at PLDI. It details how to structure the submission, ensuring reproducibility, containerization, and clear documentation to achieve Functional, Reusable, or Available badges.

This comprehensive guide outlines the requirements for submitting computational artifacts for post-acceptance evaluation at PPoPP. It details PPoPP's unique badge policy (Functional, Reusable, Results Reproduced), addresses the challenges of ensuring results reproduce across different hardware architectures, and provides a detailed packaging plan, including containerization, README structure, and provenance documentation for maximizing reproducibility.

This guide outlines the comprehensive policy and architecture required for submitting a fully reproducible replication package to The Review of Economics and Statistics (REStat). It details how to structure the repository, including data, master scripts, and the README file, ensuring that every result can be traced and regenerated by other researchers.

This guide details how to assemble a submission-ready replication package for top academic journals, such as The Review of Economic Studies (REStud). It covers the necessary repository structure, mandatory README sections, and coding best practices (e.g., master scripts, relative paths, version pinning) to ensure that the data and code can be fully reproduced and verified by journal editors before publication.

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.

This guide outlines the mandatory requirements for authors publishing quantitative or mixed-method research in academic journals. It details the process of creating a complete replication package—including raw data, master scripts, seeds, and extensive README documentation—and depositing it in a trusted data repository (like Dataverse). It ensures scholarly rigor by guaranteeing that published findings are fully reproducible and traceable.

Creates task-oriented technical documentation with progressive disclosure for READMEs, API docs, architecture docs, and markdown. Also generates formal security reports (reverse engineering, penetration testing, CTF, malware/APT analysis) in the user's project directory with Evidence-Finding-Path chain.

This skill generates complete, deploy-ready static landing pages by analyzing a source code repository. It reads key files (README.md, CHANGELOG.md, etc.), extracts structured content (features, installation guides, FAQ), and compiles it into a professional, responsive HTML/CSS/JS site. Ideal for standardizing open-source project showcases, converting technical documentation into marketing pages, or quickly building a project homepage.

Builds data-and-code replication packages for Management World (《管理世界》) after conditional acceptance. Generates Chinese READMEs, audits deposits, handles restricted commercial databases (CSMAR/Wind) and government micro-data, ensuring one-pass editorial reproducibility checks.

This skill guides the creation of technical documentation (such as RFCs, READMEs, PR descriptions, and architecture guides) that minimizes the mental effort required for the reader. It emphasizes structural patterns like 'Lead with the answer,' 'Chunking,' and 'Progressive disclosure' to ensure content is scannable, actionable, and easy to review, significantly reducing cognitive load for maintainers and contributors.

Documents manifest locations for an example skill, guiding users to use the manifest index in README.md to locate related documentation.