Skill UI

This library provides comprehensive patterns for building robust Clinical Decision Support Systems (CDSS) integrated into Electronic Medical Records (EMR). It ensures patient safety by implementing critical checks such as drug interaction screening, weight/age/renal-adjusted dose validation, and standardized clinical scoring (e.g., NEWS2). The engine uses pure functions, guaranteeing high testability and reliability for critical medical insights.

This skill centralizes and manages Standard Operating Procedures (SOPs) for marketing agencies. Users can create SOPs from templates, assign them to specific brands, track compliance against recent campaign executions, and maintain detailed version control. It ensures consistent quality and accountability across all operational areas, from paid media to SEO.

This guide details the critical steps and changes required to transform a reviewed SIGIR submission into the final Version of Record published via ACM Digital Library. It covers technical requirements, metadata standardization (CCS, keywords), conflict resolution, and adhering to publishing timelines and copyright protocols (e-rights, TAPS).

This comprehensive guide outlines the mandatory steps for transforming an accepted, anonymized academic paper into the final ACM Digital Library Camera-Ready format. It covers de-anonymization, adhering to ACM metadata standards (DOI, ORCID, CCS concepts), integrating reviewer feedback, and ensuring permanent reproducibility of code and traces.

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.

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.

This skill guides users on writing Python code within n8n Code nodes, focusing on standard library usage. It details critical limitations regarding external packages, offering workarounds for HTTP requests and data analysis. Users learn when to prefer Python over JavaScript and how to handle specific syntax requirements safely within the n8n environment.

Generate production-ready Terraform modules for AWS, Azure, and GCP. Standardize cloud resource provisioning, implement infrastructure as code best practices, and ensure multi-cloud compatibility for scalable infrastructure management.