Skill UI

This guide outlines the crucial, multi-stage process of converting a conditionally accepted academic paper into the final, publishable camera-ready version for top-tier conferences. It covers fulfilling rebuttal promises, meeting strict ACM accessibility standards (e.g., alt text for every figure and table), managing the ACM TAPS workflow, and finalizing all technical and logistical assets.

This guide covers the mandatory Open Science appendix for high-level security research. It instructs authors on enumerating all artifacts (code, datasets, configurations), documenting data withholding decisions, and implementing engineering controls (like environment pinning and version tracking) to ensure that published results are independently and reliably reproducible.

A comprehensive guide to vcpkg, a native C/C++ package manager. It covers project setup, dependency management, and cross-compiling for robust builds. The guide details best practices for Manifest mode, CMake integration, and Visual Studio setup, ensuring reproducible and isolated build environments.

This guide provides a comprehensive overview of the VLDB submission and review pipeline. It details the rolling monthly structure, the decision outcomes (Accept, Revise, Reject), and the specific expectations for authors at each stage. Use this to set realistic expectations regarding review timelines, the nature of expert feedback, and optimizing your revision strategy, contrasting it with traditional batch venues.

A comprehensive guide detailing when and how to structure theoretical models in development economics research, particularly for World Bank Economic Review submissions. It emphasizes that models must serve a functional purpose—such as interpreting empirical coefficients, running policy counterfactuals, or aggregating to welfare statements—rather than being decorative. Key principles cover parameter identification, matching structural frictions to the local setting, and handling counterfactual assumptions (Lucas critique).

Provides comprehensive guidelines for packaging and submitting research artifacts (datasets, models, code) for major conferences. It details the difference between mere 'existence' (badge verification) and true academic 'reproducibility,' covering archival strategies, web data licensing, takedown resilience, and minimal viable package structure for long-term scholarly impact.

A comprehensive guide for authors submitting papers to web conferences. It details how to structure empirical claims, ensuring evidence matches the claim's scope (scale, freshness, generalizability). The guide covers avoiding methodological flaws like temporal or popularity leakage, selecting datasets with proper provenance, and performing rigorous ablations and statistical analysis for maximum academic rigor.

This skill establishes the root directory for a collaborative workshop, serving as a centralized project hub. It manages two distinct paths: using an existing local directory (Path A), or initializing a new private GitHub repository (Path B). It scaffolds the necessary structure—including `desks/`, `bench/`, and `CAIRN.md`—while strictly preserving existing work and preventing repository nesting. Ideal for standardizing project setup and maintaining collaboration standards.

This comprehensive guide outlines best practices for packaging research artifacts, including code, proprietary data logs, and models, for submission to top-tier academic venues. It addresses the crucial need for verifiability and reproducibility, providing strategies for handling limited space, proprietary datasets, and the absence of formal artifact grading systems, ensuring high academic credibility.

This skill generates high-quality images, textures, icons, and visual assets from text prompts using advanced AI models. It supports multiple providers, including OpenAI (DALL-E) and Google Gemini, offering flexibility in quality and speed. Ideal for rapid prototyping, game asset creation, artwork, and visual mockups across various projects. Requires configuration with an API key.

Polars is a lightning-fast DataFrame library designed for efficient, memory-efficient data processing in Python and Rust. Built upon Apache Arrow, it supports lazy evaluation and parallel execution, making it ideal for ETL pipelines, large-scale data transformations, and scenarios where standard Pandas performance is insufficient. It excels at handling datasets up to 100GB that fit within available RAM.

Scikit-learn is the industry-standard Python library for classical machine learning. It provides comprehensive tools for supervised and unsupervised learning tasks, including classification, regression, clustering, and dimensionality reduction. Use it to build robust, production-ready ML pipelines, perform rigorous model evaluation, and preprocess complex datasets for various data science applications.