Skill UI

A comprehensive guide on structuring research papers for top-tier AI conferences (like NeurIPS). It teaches authors how to strategically allocate contributions, proofs, data details, and code artifacts across the main body, text appendices, and separate ZIP files. Use this skill to ensure reviewers can easily identify the core contribution and assess the work's validity without missing crucial supporting details.

Search and read documents, repositories, URLs, and notes stored in OpenViking. Guides tool choice among find, grep, glob, and list, and drilling from directory summaries to exact files for efficient reference retrieval.

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.

Mandatory package structure for submitting accepted empirical papers to the Quarterly Journal of Economics (QJE). It ensures complete research reproducibility by organizing raw data, derived files, and analysis code. This package guides authors in creating a comprehensive, self-contained deposit that allows any investigator to regenerate every figure, table, and result published in the manuscript, adhering to strict academic data-availability policies.

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.

A comprehensive guide for packaging and submitting artifacts for Information Retrieval (IR) conferences (like SIGIR). It details the strict requirements for reproducibility, including mandatory components like TREC-format run files, qrels, and index recipes. The process covers repository structure, anonymization for review, handling data provenance, and ensuring that results can be audited by any reviewer using standard community tooling.

This comprehensive checklist guides authors through the final pre-submission process for manuscripts targeting the Strategic Management Journal (SMJ). It covers critical steps such as implementing double-blind anonymization, structuring the mandatory dual abstracts (research and managerial), verifying APA formatting, and ensuring all required files (cover letter, supplements) are prepared before submission.

Astropy is the essential Python package for astronomical research and data analysis. It provides a comprehensive suite of tools for handling celestial coordinate transformations (e.g., ICRS, Galactic), managing physical units and quantities, processing FITS files, performing complex cosmological calculations, and ensuring precise time handling. It is indispensable for any project involving scientific data in the field of astronomy and astrophysics.

This skill serves as a deterministic internal downloader for image URLs extracted from comprehensive web search results. It receives normalized media slots and search output via stdin, utilizing Python HTTP APIs to download direct image links. The process includes validating MIME types and saving files accurately into the configured local project directory, ensuring reliable asset retrieval without relying on sandboxed shell tools.

Build a local multimedia webpage project from a user topic, audience, language, style, and media preferences by framing requirements, researching, planning, acquiring media, generating files, packaging, validating, repairing, and delivering usage guidance.

This specialized skill traces the complete lifecycle of an uploaded or imported file through complex backend systems. It maps the sequence from ingress to final consumer, tracking every derived artifact, archive extraction, format conversion, parser invocation, and deserialization step. It is crucial for security analysis, vulnerability research, and understanding how a file reaches a decisive backend effect after initial sandbox assumptions are made.

This skill allows users to select and implement various compliance-aligned privacy policies (such as HIPAA Safe Harbor, GDPR pseudonymization, and PIPEDA) for robust de-identification. It manages complex policy profiles, ensuring data is masked, redacted, or pseudonymized according to specific legal requirements, while also supporting custom surrogate generators for advanced research needs.