Skill UI

This guide provides an expert methodology for designing and auditing Natural Language Processing (NLP) experiments for top-tier academic submissions. It instructs researchers on how to map meta-review claims to measurable evidence, ensure budget-fair comparisons, and systematically address potential reviewer probes (scope, fairness, contamination, etc.) to maximize publication rigor.

This guide outlines the stringent standards for ensuring the reproducibility of Natural Language Processing (NLP) research. It teaches authors how to treat the Responsible NLP checklist as a binding contract by meticulously 'pinning' every variable—including model identifiers, API access dates, prompt strings, random seeds, and evaluation metrics. It details creating a comprehensive reproducibility manifest to make research results verifiable and defensible.

This skill serves as a detailed guide for authors submitting theoretical articles in syntax or morphology to Natural Language & Linguistic Theory (NLLT). It outlines the journal's specific expectations, emphasizing that formal analyses must be deeply grounded and rigorously tested using rich, representative, and often cross-linguistic empirical data. Use this tool to assess the methodological rigor, scope fit, and structural requirements of your manuscript against NLLT's high academic standards.

This skill guides the creation of computable phenotypes and patient cohorts using the OHDSI/OMOP CDM standards. It allows users to define precise, executable criteria by combining standard concept sets (like PheKB) with advanced Natural Language Processing (NLP) features extracted from free clinical text. Use this when structured codes alone are insufficient to capture critical health information (e.g., symptoms, social context).

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.

Utilizes OpenMed's analyze_text to perform sophisticated Named-Entity Recognition (NER) on clinical and biomedical text. This tool extracts structured entities such as diseases, drugs, anatomy, genes, and proteins. It provides precise character spans and model confidence scores, making it essential for building robust, privacy-first clinical Natural Language Processing (NLP) pipelines.

This skill decodes complex pipe-delimited HL7 v2.x messages (including ADT, ORU, MDM) into structured segments and fields. It is essential for ingesting HL7 feeds from interface engines, allowing users to surface embedded clinical narratives (OBX-5 and NTE-3) for subsequent Natural Language Processing (NLP) analysis. It also provides segment-aware de-identification capabilities to redact PHI while preserving the native HL7 message structure.

Turns a natural-language intent into a deployed model-driven Power Apps app — tables, relationships, forms, views, charts, business rules and sitemap — through an interactive, reviewable app spec you can also download and edit.

Autonomously generate and create complete Power Automate flow definitions from a natural-language description. Discovers environments and connections, validates definitions first, and enforces OpenApiConnection best practices.

Full reference guide for Ouroboros commands and agents. Explains usage, natural language triggers, and available agents for workflow automation. Helps users execute seeds, evaluate results, and manage sessions effectively within the development environment.

Autonomous DevSecOps & FinOps guardrails that review Linux kernel patches for memory safety, analyze Terraform cost drifts to prevent bill spikes, and translate natural language security intent into hardened Kubernetes policies. Integrates advanced AI reasoning into the SDLC for high-stakes audits and CI/CD quality gating.

Generate, refine, validate, and render diagrams from natural language, notes, code, schemas, or existing diagram sources. Supports flowcharts, swimlanes, attack-path graphs, data-flow diagrams, architecture, and state machines using Mermaid, Graphviz, or PlantUML.