Skill UI

This guide helps authors determine if their research is web-native and which specific research track (e.g., Search, Security, Economics) is the most appropriate fit for submission to the Web Conference (WWW). It outlines critical decision points, distinguishing between general ML methods and web-specific contributions, and advises on optimal submission strategy.

This guide provides a comprehensive framework for designing rigorous and defensible evaluation sections for machine learning and information retrieval papers, particularly for top-tier conferences like WSDM. It covers the four critical evidence quadrants—Effectiveness, Validity, Efficiency, and Robustness—and details necessary protocols, such as temporal splits, proper baseline selection (including heuristics), ablation studies, and hardware-aware performance reporting. Use this when auditing or writing the experimental section of a research paper.

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.

A guide for scholars on structuring academic essays to stake a clear, significant contribution within a broad critical conversation. It teaches how to move beyond simple citation dumps by identifying the core scholarly problem, engaging diverse critical traditions, and pre-empting counter-arguments, ensuring the work resonates with both specialists and generalists in the field.

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).

NetworkX is a powerful Python package designed for creating, manipulating, and analyzing complex networks and graphs. It supports various graph types (Directed, Undirected, MultiGraphs) and provides extensive functionality for graph algorithms, including shortest path calculation, centrality measures (like PageRank), community detection, and connectivity checks. Users can generate synthetic networks, read/write data from multiple formats (GraphML, CSV, Pandas), and visualize results using Matplotlib. Ideal for social network analysis, biological research, and system modeling.

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.

This module provides a lazy adapter registry to seamlessly integrate OpenMed's clinical PII detection with major NLP frameworks like Microsoft Presidio, spaCy, and LangChain. It allows users to use OpenMed for enhanced PII recall, merge spans, or implement on-device PHI redaction guardrails without rewriting entire pipelines.

This skill constructs a comprehensive, chronologically sorted patient timeline from raw clinical entities extracted from medical notes. It specializes in temporal normalization, resolving both absolute and relative dates (e.g., 'two days after admission') against a defined anchor date. The resulting sorted sequence of diagnoses, medications, and procedures is ready for immediate visualization or seamless export to FHIR resources.

This local developer tool inspects dependency manifests (requirements.txt, pyproject.toml, package.json) for surface-level issues like standard-library shadowing, unpinned dependencies, and conflicts. It provides plain-language explanations and suggested fixes, helping developers debug dependency rot before deeper installation attempts.

The Diagram Generator converts complex, unstructured inputs—such as natural language, code snippets, schemas, and notes—into professional, editable visual diagrams. It supports diverse standards including Mermaid, PlantUML, and Graphviz, enabling the creation of flowcharts, sequence diagrams, UML models, ER diagrams, architecture maps, and more. Outputs are designed to be reviewable, version-controllable, and highly precise.

Automates the creation of isolated Git worktrees for safe and efficient feature development. This process handles branch validation, optimizes dependency installation (e.g., symlinking node_modules), runs background verification tests, and provides guidance for database isolation, ensuring developers always work in a clean, separated environment.