Skill UI

This skill computes disproportionality metrics (PRR, ROR, EBGM, IC) using FAERS/OpenFDA drug-event data. It analyzes spontaneous reports to statistically screen for potential drug-reaction associations (Signal of Disproportionate Reporting, SDR). By constructing a 2x2 contingency table, it helps pharmacovigilance professionals identify over-reported adverse events, providing a statistical hypothesis for drug safety assessment.

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.

A comprehensive guide for authorized digital forensics investigation. It covers critical areas including memory dump analysis (using Volatility), disk timeline reconstruction, PCAP network traffic analysis, and system artifact triage (like Event Logs and Prefetch). Essential for incident response, evidence preservation, and threat hunting to maintain a robust chain of custody.

Generates high-quality, structured technical documentation for READMEs, APIs, and architecture guides, adhering to progressive disclosure principles. It also specializes in creating formal, evidence-backed security reports for penetration testing, CTF writeups, and reverse engineering tasks, ensuring a professional, reusable format.

This guide provides a systematic framework for authorized email security reviews. It covers advanced phishing analysis, including deep dives into email headers, assessing SPF/DKIM/DMARC compliance, identifying Business Email Compromise (BEC) patterns, and researching mailbox token abuse. It is essential for incident response, threat intelligence, and security architecture review.

This skill implements a comprehensive logging guard to prevent Protected Health Information (PHI) leakage into logs, traces, and error reports. It acts as a pre-emission filter, redacting structured identifiers (SSN, email, phone) via regex and handling free-text PHI using advanced models. Essential for maintaining strict data privacy and compliance when shipping logs to centralized stores or error trackers.

This skill performs the crucial ETL process of mapping OpenMed-extracted, terminology-coded clinical facts (conditions, drugs, measurements) into the OMOP Common Data Model (CDM) v5.4 tables. It is used downstream after entity extraction and source coding, transforming raw NLP findings into standardized, OMOP-conformant records for OHDSI analytics.

This tool systematically audits and scores defined AI agents to ensure they are correctly scoped, highly specific, and safe for programmatic orchestration. It checks for description ambiguity, over-permissive tool grants, and anti-patterns that break headless workflows. Use it when onboarding new agents, integrating agent fleets, or debugging inconsistent multi-agent selection.

This skill evaluates de-identification and clinical NER models against rigorous leakage-first release gates (G1a-G8). Instead of relying on aggregate F1 scores, it focuses on detecting residual Protected Health Information (PHI) leakage and enforcing per-label recall floors, which is crucial for HIPAA compliance. Determines if a model is RELEASABLE or QUARANTINED.

A comprehensive guide for designing and building large-scale, resilient platforms using the evolutionary modular monolith approach. It enforces strong logical boundaries using DDD principles (bounded contexts, flat-by-aggregate) and implements necessary patterns like Anti-Corruption Layers and transactional outboxes. Use this when defining a system's structure, choosing between monolith and microservices, or ensuring robust decoupling from external vendors (e.g., ERP, AI).

This skill provides a structured way to understand complex codebases, functions, or architectural concepts. It allows the user to specify the depth of explanation (Simple, Standard, Deep), ensuring the output covers purpose, step-by-step flow, key decisions, and trade-offs for maximum learning efficiency.

This skill facilitates the extraction of population-scale (cohort-level) clinical text from Electronic Health Records (EHR) or FHIR data warehouses using the FHIR Bulk Data Access ($export) operation. It manages the asynchronous job kickoff, polling, and downloading of NDJSON files. The resulting data stream is designed to be fed in batches into OpenMed for mandatory de-identification and Named Entity Recognition (NER) analysis.