Skill UI

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.

An advanced orchestrator that manages the entire software development lifecycle (SDLC) from initial concept vetting to final code deployment. It guides the process through distinct, sequential stages: Product Direction Review (CEO), Technical Architecture Review (Engineering), Detailed Planning, Validation, and final Execution (PR). This tool ensures all critical gates are passed before writing code, minimizing risks and ensuring alignment.

Performs comprehensive, two-layer validation on generated plans. Layer 1 executes instant structural checks (format, dependencies, compliance) while Layer 2 triggers specialist agents (e.g., security, DB, performance) based on plan content. The system forces resolution of all identified issues before allowing execution, supporting auto-fixes and guided human input.

A robust template designed for creating complex, multi-stage commands or 'recipes.' It enforces a highly structured process by mandating 'Context Validation Checkpoints' before any step executes. This pattern ensures that preconditions are explicitly verified, significantly increasing the reliability and predictability of complex automated workflows.

This skill validates FHIR R4 resources and Bundles against mandatory US Core profiles (USCDI). It goes beyond mere syntactic checks to ensure that data prepared for submission to major Electronic Health Records (EHRs) like Epic or Cerner meets the required US-specific conformance standards. It detects common data gaps, checks 'must-support' elements, and converts the comprehensive validation report into a standard FHIR OperationOutcome for seamless pipeline integration.

This skill is designed to read data records from a specified source file, performing necessary transformations and cleaning to prepare the data for downstream use. It handles the core Extract, Transform, Load (ETL) process, making it ideal for data pipelines, data validation, and preparing raw data into a structured format.

This utility provides a straightforward mechanism for reading the contents of specified files and returning the data. It is ideal for quick file inspection, data validation, or integrating basic file content retrieval into automated scripts and workflows.