Classify medical devices for FDA, EU MDR, and MFDS regulatory classes.
npx skills add https://github.com/henry-1981/Cowork-RA --skill aria-classification
Please help me install this Agent Skill. Skill: aria-classification Source: https://github.com/henry-1981/Cowork-RA/tree/main/aria/skills/classification Command: npx skills add https://github.com/henry-1981/Cowork-RA --skill aria-classification
This Skill automates the complex and time-consuming process of determining the regulatory classification for medical devices across multiple key global jurisdictions, reducing manual effort and potential errors.
Determine the regulatory classification for a new implantable cardiac defibrillator.
High-intent search queries and answers about installing and using this skill.
Medical device classification is automated by analyzing invasiveness, duration, active or passive status, body system interaction, and anatomical location to determine regulatory class and risk levels across FDA, EU MDR, and MFDS. It generates a structured classification matrix and detailed rationale.
Regulatory class is determined by evaluating device invasiveness, duration of contact, active or passive status, body system interaction, and anatomical location. This assessment assigns the appropriate risk level and produces a structured classification matrix explaining the rationale.
Yes, you can assess global regulatory risk levels for an implantable cardiac defibrillator by inputting its specific characteristics. The system evaluates invasiveness and duration to generate a multi-region classification matrix covering FDA, EU MDR, and MFDS requirements.
Yes, this medical device classification tool fully supports MFDS South Korea regulations alongside FDA and EU MDR. It analyzes device characteristics to assign the correct regulatory class and risk level specific to South Korean requirements.
The best way to automate multi-region medical device classification is to evaluate device invasiveness, duration, and active status against FDA, EU MDR, and MFDS rules. This approach delivers a structured classification matrix and rationale, eliminating manual cross-referencing errors.
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