Skill UI

A specialized deep-dive skill for analyzing container-to-host escape vulnerabilities. This tool is used after a sandbox orchestrator establishes initial assumptions, focusing on mapping isolation boundaries (namespaces, cgroups, seccomp, capabilities). It helps prove boundary crossings, identify minimal exploit primitives, and determine if an exploit can break out of the container context to the host kernel.

This specialized skill is designed to trace and prove the complete flow of OAuth or OIDC authentication, acting as a downstream specialization after an orchestrator has established sandbox assumptions. It meticulously tracks the entire journey—from initial redirect and parameter handling (state, nonce, PKCE) through token exchange, claim mapping, and ultimately to the point where an identity or privilege is accepted by the backend service. Use this when the critical task is proving the security and mechanics of the authentication chain.

A specialized downstream skill for deep analysis of complex web rendering pipelines. Use this to trace vulnerabilities or logical flaws residing in Server-Side Rendering (SSR), template data, route loaders, or client-server hydration boundaries. It compares template-level gating against backend handler enforcement to pinpoint the decisive weak point in the render chain.

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 skill automatically detects and extracts tabular laboratory panels from diverse sources, including scanned images, photos, and delimited files (CSV/TSV). It converts complex visual data into clean, structured rows, capturing the test name, measured value, unit, reference range, and abnormal flag. It is essential for preparing medical data for subsequent standardization using LOINC and UCUM, and for mapping into FHIR resources.

This tool performs a comprehensive analysis of AI configuration file evolution within a Git repository. It tracks when specific AI configs were first introduced, charts monthly commit velocity related to AI features, highlights key PRs that shaped the development, and synthesizes the project's AI maturity phases. Ideal for mapping AI adoption timelines and assessing technical maturity.

This skill maps free-text laboratory and clinical observation names to standardized LOINC codes. It resolves ambiguous terms by considering the full six-axis model: Component, Property, Time, System (specimen), Scale, and Method. It integrates with FHIR terminology services to ensure accuracy, supporting US Core Laboratory Results and UCUM units.

This skill maps clinical concept spans (disorders, anatomy, procedures, etc.) extracted by OpenMed to SNOMED CT concept IDs (SCTID). It performs complex, out-of-process terminology resolution using standard FHIR R4 endpoints ($lookup, $translate, ValueSet/$expand) against a user-supplied, licensed terminology server (e.g., Ontoserver, UTS). Ideal for generating rich, hierarchy-aware clinical codes, performing subsumption queries (ECL), or translating codes between clinical standards (like ICD-10 to SNOMED CT).

This skill parses standardized C-CDA/CCD XML clinical documents exported from EHRs. It extracts both human-readable section narrative text and coded entries, mapping them by LOINC codes and template IDs. It is essential for preparing clinical free-text data for subsequent NLP analysis and supports XML-aware de-identification while preserving document structure.

Implements GDPR-compliant pseudonymization for sensitive personal or clinical data from EU residents. Unlike full anonymization, this method creates a reversible mapping key, which must be stored separately and securely. This process allows for controlled re-linkage (e.g., for Subject Access Requests or longitudinal care) while maintaining GDPR Article 4(5) compliance by ensuring the linkage key is kept separate from the working data.

This skill facilitates reversible de-identification (pseudonymization) of clinical text, allowing sensitive Protected Health Information (PHI) to be masked for processing while retaining the ability to restore the original data later under strict authorization. It captures a mapping key and provides a function to re-identify the text, crucial for workflows like GDPR compliance, patient recontact, or auditing. Crucially, the re-identification mapping must be stored separately and securely.