Skill UI

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.

A comprehensive framework for authorized security assessments of Operational Technology (OT) and Industrial Control Systems (ICS). It emphasizes a 'passive-first' approach, covering Purdue model zoning, analyzing industrial protocols (Modbus, DNP3, etc.), and identifying exposure points on SCADA/DCS environments. The methodology strictly avoids disruptive write operations on PLCs, prioritizing read-only validation and safe network analysis for physical safety.

Guides users to select a local, on-device Personally Identifiable Information (PII) detection model from a committed registry. It filters models based on language, required runtime environment (e.g., PyTorch, MLX), and size constraints. The process emphasizes building an offline shortlist and mandates rigorous pre-deployment recall validation and performance benchmarking to ensure secure, privacy-preserving deployment.

This skill manages complex, stateful proof projects as a local-first pipeline. It guides the user through drafting, rigorous verification, and source bookkeeping. It systematically handles proof continuation across runs, maintaining a full audit trail. When local attempts stall, it orchestrates handoffs to external AI models (like GPT Pro) or performs optional deep audits (DeepSeek), ensuring a structured, traceable, and verifiable output suitable for academic or technical submission.

This skill guides users on deploying OpenMed models completely on-device for maximum privacy and edge computing. It details running workflows using three backends: MLX (Apple Silicon), CoreML (iOS/macOS), and ONNX/WebGPU (cross-platform/browser). The process involves converting, optionally quantizing, and running models locally, ensuring PHI never leaves the host device.

Utilize zero-shot NER to extract custom entity types (e.g., Drug, Symptom, Device) from complex clinical or biomedical text without requiring any labeled training data or fine-tuning. OpenMed wraps GLiNER/GLiNER2, allowing users to define labels on the fly. The workflow involves building a model index and then running inference via CLI or Python API, making it ideal for rapid prototyping or unstable schemas.

Provides a hardened FastAPI endpoint for OpenMed's core functionalities: clinical Named Entity Recognition (NER), Personally Identifiable Information (PII) extraction, and de-identification. Designed for self-hosted microservices, it handles inference requests, model lifecycle management, and supports dynamic batching, ensuring secure, on-device processing of sensitive clinical data.

This skill provides comprehensive tools for creating and refining visual assets. It supports generating entirely new images, artwork, diagrams, or product mockups using advanced AI models. It also facilitates sophisticated image editing, allowing users to preserve, modify, or combine elements from existing visuals turn by turn. Ideal for designers, marketers, and content creators needing high-quality, context-aware visual content.

A structured framework based on the Cynefin model to classify problems by their underlying cause-effect domain (Clear, Complicated, Complex, Chaotic). It guides users on selecting the correct response mode and methodology before attempting solutions, preventing failure due to domain mismatch. Useful when unsure how to approach novel or mixed problems.

A systematic methodology for resolving discrepancies between conceptual models (documentation, tests, diagrams, metrics) and actual observed behavior (live code execution or real data). This process mandates that observed reality (the 'territory') always overrules the theoretical map, ensuring system integrity and accurate knowledge updates.

A systematic methodology for solving complex, multi-domain, or high-stakes problems where a single mental model creates blind spots. This process guides users to select 2-3 complementary models, assign distinct roles, define a conflict rule, and synthesize findings into a single, decision-ready recommendation.

This tool acts as a meta-skill guide, helping users systematically determine the most appropriate cognitive framework when faced with complex problem-solving tasks. It maps the problem's domain and type (e.g., diagnosis, strategy, architecture) to select the optimal primary thinking skill, preventing guesswork or arbitrary stacking of tools. It ensures the chosen method is justified by mechanism fit and relevance.