Skill UI

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.

This tool analyzes a given codebase to automatically identify high-value, actionable automation routines. It surfaces use cases across three primary trigger types: scheduled cron jobs, external API calls, and GitHub repository events. By mapping manual pain points to autonomous workflows, it helps teams implement robust CI/CD and DevOps practices.

This technique segments unstructured clinical documents (such as HPI, PMH, and A&P sections) into contextually rich, canonical chunks. This is crucial for improving the precision of downstream NLP tasks like Named Entity Recognition (NER) and de-identification. By identifying and normalizing headers and mapping them to LOINC codes, the system ensures that the source context (e.g., historical vs. active) is maintained, leading to significantly more accurate results.

A comprehensive methodology for diagnosing complex systems where problems are emergent across multiple components. It guides the user to map system boundaries, stocks and flows, feedback loops, and underlying structural drivers. By identifying the highest feasible leverage point, it recommends structural interventions that address root causes rather than superficial symptoms.

Expert advisor for CIS Controls v8: implementation group scoping, gap assessments, safeguard guidance, and mapping to NIST CSF, ISO 27001, SOC 2, and CMMC. Supports prioritized cyber hygiene for any organization size.

Maps a litigation team's disclosure strategy from a case summary, pleading, chronology, or early case theory. Identifies relevant document categories, likely custodians, potentially adverse materials, evidential gaps, and search themes. Supports early case assessment and proactive risk planning in civil or commercial disputes.