Skill UI

This comprehensive reference architecture details a multi-stage pipeline for web scraping and content ingestion using Firecrawl. It seamlessly integrates single-page scraping, site-wide crawling, URL mapping, and structured data extraction. The subsequent processing layer handles cleaning, deduplication, and chunking, making this blueprint ideal for designing robust knowledge bases, RAG systems, and complex AI data pipelines.

This module provides minimal working examples for the OpenEvidence API, designed for clinical intelligence applications. Users can perform complex clinical queries to get treatment recommendations, check potential drug interactions based on patient context, and search for authoritative medical guidelines from major health organizations (e.g., ACC/AHA). Ideal for developing evidence-based healthcare decision support systems.

SimPy is a powerful, process-based discrete-event simulation framework written in Python. It is designed for modeling complex systems where entities interact with shared resources and time-based events occur, such as manufacturing lines, service queues, network traffic, or logistics networks. Use this skill to analyze resource contention, simulate process optimization, conduct capacity planning, and model dynamic system behaviors.

This tool applies advanced role mining techniques (including clustering and formal concept analysis) to analyze existing user-permission assignments. It discovers optimal, minimal role sets for Role-Based Access Control (RBAC) systems, helping organizations mitigate 'role explosion' and enforce the principle of least privilege. Ideal for identity governance and access control architecture redesign.

A comprehensive guide and toolkit for extracting, parsing, and analyzing Windows Event Logs (EVTX). This method utilizes industry-leading tools like Chainsaw and Hayabusa, along with Sigma rules, to perform deep digital forensics. It is specifically designed to detect critical security indicators such as lateral movement, persistence mechanisms, privilege escalation, and unauthorized access attempts on Windows systems.

Performs constraint-based metabolic modeling with COBRApy: FBA, pFBA, FVA, gene knockouts, flux sampling, medium design, production envelopes, gap filling, and SBML model validation for systems biology and metabolic engineering.

This comprehensive checklist guides users through critical operational readiness steps for a ClickHouse production deployment. It covers server tuning (config.xml), schema design best practices, robust backup strategies (S3), detailed monitoring setup (Prometheus/Grafana), security hardening, and application integration verification. Use this document before go-live, during major upgrades, or when auditing production systems to ensure maximum stability and performance.

KAPE (Kroll Artifact Parser and Extractor) is a specialized, rapid triage tool for incident response. It collects specific, volatile forensic artifacts (like registry hives, event logs, and browser data) from compromised Windows systems. It is designed for remote, at-scale artifact collection when full disk imaging is impractical, enabling analysts to quickly build a defensible, parseable dataset for initial investigation and containment.

This guide provides rigorous guidelines for creating high-quality figures and tables for complex systems manuscripts, especially in agricultural science. It emphasizes shifting focus from single-factor bar charts to systemic visualizations that reveal interactions, trade-offs (e.g., Pareto fronts), model dynamics, resource flows (Sankey diagrams), and model performance (observed-vs-simulated fit). Exhibits must be self-contained, reproducible, and convey uncertainty.

A comprehensive guide for planning and auditing highly rigorous biological experimental protocols, particularly for cancer mechanism studies. It emphasizes orthogonal validation across independent systems (in vitro, in vivo, human), detailing requirements for controls, replicates, sample size justification, randomization, and blinding to ensure scientific rigor.

A comprehensive audit checklist designed for authors submitting data-engineering systems papers to major conferences like ICDE. It covers critical areas including IEEE formatting standards, round selection strategy, Microsoft CMT conflict declarations, and the requirement for supplemental material, ensuring authors mitigate common desk-reject risks.

This framework provides a comprehensive methodology for designing, auditing, and reporting empirical measurements about large-scale systems like the Internet. It guides users to ensure measurements are scientifically rigorous, by matching claims to evidence, quantifying coverage bias, designing for temporal changes, and adhering to strict ethical guidelines and data provenance standards.