Skill UI

This guide defines the role and responsibilities of a Senior Quality Manager Responsible Person (QMR) for highly regulated MedTech and HealthTech environments. It covers essential functions under ISO 13485, including leading management reviews, establishing quality objectives, monitoring performance KPIs, and maintaining strict regulatory compliance oversight. Use this resource when developing a Quality Management System (QMS) or managing regulatory accountability.

This skill provides a systematic framework for analyzing cyber campaign attribution. It involves collecting, weighting, and correlating diverse indicators—such as infrastructure overlaps, TTP consistency, malware code similarities, and operational patterns—to determine the responsible threat actor. Utilizing structured methodologies like Analysis of Competing Hypotheses (ACH) and the Diamond Model, users can build confidence-weighted assessments to enhance threat intelligence accuracy.

A comprehensive guide for building applications with Claude, ensuring responsible data handling. This skill covers critical patterns such as PII redaction (stripping sensitive data like emails, phone numbers, and card numbers), optimizing the 200K token context window through conversation trimming, and implementing best practices for data retention and privacy compliance when interacting with the Anthropic API.

This skill transforms a high-level, approved strategic decision into a detailed, actionable 90-day execution roadmap. It structures the plan with defined workstreams, assigned DRIs (Directly Responsible Individuals), weekly milestones, risk registers, and mandatory check-in cadences. It bridges the gap between 'what we decided' and 'what we do next.'

Aria functions as a System Architect, responsible for establishing the definitive structural foundation of the entire system. She designs comprehensive blueprints covering the data model (schema, relationships, constraints), API contract specifications (endpoints, auth, pagination), modular file structure, and selection of core architectural design patterns. Her output ensures that no code is written without a rigorously defined, approved architectural plan, minimizing ambiguity and technical debt.

Conducts a comprehensive, structured ethical review for any AI or ML model, feature, or product. It assesses risks related to fairness, bias, transparency, privacy, and societal impact, providing a risk tier score, necessary mitigations, and governance documentation suitable for pre-deployment compliance.

This comprehensive canvas guides teams through developing robust AI-powered features. It ensures product decisions are rigorous by covering problem definition, model selection (LLM, RAG, etc.), data governance, evaluation metrics, user experience design (including fallbacks), and responsible AI compliance. Ideal for assessing AI readiness and preventing common product failures.