Skill UI

This tool guides the transformation of a coded, screened corpus into a robust analytical spine for academic synthesis. It moves beyond simple annotated bibliography summaries by requiring the construction of a conceptual framework (e.g., taxonomy, theoretical model, tensions). This structure ensures the review advances theory or policy, providing a coherent, critical argument for the field.

A comprehensive guide for ensuring robust scientific reproducibility in robotics research (RSS). It structures evidence into distinct 'Replication Tiers' (e.g., Rerunnable, Auditable), distinguishing between the computational half (code, seeds) and the physical half (robot, environment, human protocol). It mandates the use of platform ledgers and raw trial logs to convert claims of trust into verifiable, auditable evidence.

A comprehensive guide for authors preparing methodological papers for submission to SMR. It outlines strict standards for creating a usable, reproducible package, including structured code, master scripts with fixed seeds, and formal data/code availability statements, ensuring the method can be run by others.

A comprehensive guide for authors submitting to SCI journals, detailing best practices for making research code and data available. It addresses how to proactively provide reproducible materials, structured documentation, and honest availability statements, even when formal 'Artifact Evaluation' badges are not mandatory. It helps authors improve the robustness and impact of their scientific findings.

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 methodology for preparing research artifacts for rigorous academic reproducibility evaluations, such as those run by VLDB/SIGMOD. It details the required components—including prototype code, input data, workloads, and analysis scripts—to structure a comprehensive package. The goal is to ensure that any external evaluator can independently and flawlessly rebuild and validate all claimed results, maximizing the chances of winning reproducibility awards.

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.

This workflow performs a comprehensive review against the Azure Well-Architected Framework (WAF) using your existing Infrastructure as Code (IaC) and deployed resources. It systematically checks the five pillars—Reliability, Security, Cost Optimization, Operational Excellence, and Performance Efficiency—identifying architectural risks, drift, and compliance gaps. Findings are automatically logged and converted into actionable GitHub issues for streamlined remediation.

This skill converts unstructured documents (PDF, EPUB, DOCX, etc.) into actionable, structured agent skills. Instead of providing simple summaries, it deep-dives to extract core frameworks, mental models, key principles, techniques, and anti-patterns from educational material. Use this when you need to transform academic knowledge into a reusable, machine-readable toolkit for agents like Copilot CLI, Amp, or Claude Code.

This skill specializes in analyzing built frontend artifacts, going beyond static source code. It reconstructs the dynamic JavaScript structure, tracks chunk loading paths, and interprets source maps and build manifests to recover hidden routes, APIs, and runtime behaviors. Essential for deep security analysis and understanding execution flow from deployed bundles.

This skill guides the creation of computable phenotypes and patient cohorts using the OHDSI/OMOP CDM standards. It allows users to define precise, executable criteria by combining standard concept sets (like PheKB) with advanced Natural Language Processing (NLP) features extracted from free clinical text. Use this when structured codes alone are insufficient to capture critical health information (e.g., symptoms, social context).

A specialized tool designed to detect, suggest, and evaluate Gang of Four (GoF) design patterns within TypeScript and JavaScript codebases. It analyzes code structure, identifies code smells, and provides stack-aware refactoring recommendations, supporting frameworks like React, Angular, NestJS, and Vue.