mdr-745-specialist
EU MDR 2017/745 regulation specialist and consultant for medical device requirement management. Provides comprehensive MDR compliance expertise, gap analysis, technical documentation guidance, clinical evidence requirements, and post-market surveillance implementation. Use for MDR compliance assessment, classification decisions, technical file preparation, and regulatory requirement interpretation.
Third-Party Agent Skill: Review the code before installing. Agent skills execute in your AI assistant's environment and can access your files. Learn more about security
Installation for Agentic Skill
View all platforms →skilz install alirezarezvani/claude-skills/mdr-745-specialist skilz install alirezarezvani/claude-skills/mdr-745-specialist --agent opencode skilz install alirezarezvani/claude-skills/mdr-745-specialist --agent codex skilz install alirezarezvani/claude-skills/mdr-745-specialist --agent gemini
First time? Install Skilz: pip install skilz
Works with 22+ AI coding agents
Cursor, Aider, Copilot, Windsurf, Qwen, Kimi, and more...
Extract and copy to ~/.claude/skills/ then restart Claude Desktop
git clone https://github.com/alirezarezvani/claude-skills cp -r claude-skills/ra-qm-team/mdr-745-specialist ~/.claude/skills/ Need detailed installation help? Check our platform-specific guides:
Related Agentic Skills
automating-mail
by SpillwaveSolutionsAutomates Apple Mail via JXA with AppleScript dictionary discovery. Use when asked to "automate email", "send mail via script", "JXA Mail automation",...
automating-reminders
by SpillwaveSolutionsAutomates Apple Reminders using JavaScript for Automation (JXA). Use when asked to "create reminders programmatically", "automate reminder lists", "JX...
mastering-postgresql
by SpillwaveSolutionsPostgreSQL development for Python with full-text search (tsvector, tsquery, BM25 via pg_search), vector similarity (pgvector with HNSW/IVFFlat), JSONB...
automating-contacts
by SpillwaveSolutionsAutomates macOS Contacts via JXA with AppleScript dictionary discovery. Use when asked to "automate contacts", "JXA contacts automation", "macOS addre...
Agentic Skill Details
- Owner
- alirezarezvani (GitHub)
- Repository
- claude-skills
- Type
- Other
- Meta-Domain
- N/A
- Primary Domain
- N/A
- Market Score
- 0.0
Agent Skill Grade
D
Score: 68/100
Click to see breakdown
Score Breakdown
Areas to Improve
- SKILL.md references detailed guides (mdr-classification-guide.md, clinical-evidence-requirements.md, technical-documentation-templates.md, notified-body-selection-criteria.md, mdcg-guidance-library.md) but only placeholder content exists
- No concrete input/output examples for classification decisions, gap analysis, or documentation preparation; templates mentioned but not provided
- Header uses subjective qualifiers ('Expert-level', 'comprehensive knowledge') instead of objective capability description
Recommendations
- Focus on improving Pda (currently 17/30)
- Address 3 high-severity issues first
- Add trigger phrases to description for discoverability
Graded: 1/23/2026
Developer Feedback
Found your mdr-745-specialist skill while reviewing the grading pipeline—the approach to specialist routing caught my eye, especially given the 68/100 score suggests some solid fundamentals but room to tighten the architecture. What's driving the focus on MDR-specific handling here?
Links:
The TL;DR
You're at 68/100, solid D-grade territory. The skill has strong fundamentals—your Spec Compliance (12/15) and metadata are legit—but you're losing major points on Progressive Disclosure Architecture (17/30) and Utility (13/20). The core issue: you're front-loading too much detail into SKILL.md when it should be spread across reference files, and your promised implementations (scripts, templates, detailed guides) don't actually exist yet.
What's Working Well
- Spec Compliance is locked in — Your frontmatter is valid YAML, naming follows conventions perfectly (hyphen-case), and you've got solid trigger phrases like "MDR compliance," "classification," and "technical documentation"
- Clear workflow structure — The numbered decision trees (Classification Determination, Gap Analysis Framework, Documentation Preparation) show you understand the problem space and have thought through the process
- Good navigational signals — Even at 196 lines, your headers make it scannable. The "when to use" section is practical
The Big One: Reference Files Are Placeholders
This is eating ~5 points right now. You reference detailed guides (mdr-classification-guide.md, clinical-evidence-requirements.md, technical-documentation-templates.md) throughout SKILL.md, but references/api_reference.md is just a stub:
# Reference Documentation for Mdr 745 Specialist
This is a placeholder for detailed reference documentation.
Here's the fix: Move the heavy detail out of SKILL.md and actually build those reference files. Take sections like "Technical Documentation Structure" (currently 20+ lines in SKILL.md) and move them to references/technical-documentation-templates.md. Then in SKILL.md, just say: "See references/technical-documentation-templates.md for the complete structure." This shrinks SKILL.md, improves token economy, and makes you searchable without sacrificing comprehensiveness.
Impact: +5 points if executed properly.
Other Things Worth Fixing
Scripts and assets don't exist — You mention
mdr-gap-analysis.pyandclinical-evidence-tracker.pybut only haveexample.py. Either implement them or remove the specific file references. Vague is better than broken promises.Zero concrete examples — No sample classification rationales, no sample gap analysis output, no actual documentation section templates. Add 2-3 worked examples showing input → classification logic → output. This alone bumps Utility up ~3 points.
Marketing language in the opener — "Expert-level...comprehensive knowledge" feels sales-y. Replace with objective capabilities: "EU MDR 2017/745 compliance specialist covering device classification, technical documentation, clinical evidence requirements, and post-market surveillance."
Missing validation steps — Your Gap Analysis Framework describes what to assess but doesn't define completion criteria or verification checkpoints. Add explicit "How do you know the analysis is complete?" checks.
Quick Wins
- Most impactful: Build out reference files with actual content from SKILL.md (30 min work, +5 points)
- Next: Add 2-3 concrete examples showing real device classifications and their rationale (+3 points)
- Then: Implement or remove promised scripts; don't reference tools that don't exist (+4 points)
- Polish: Trim marketing language, add validation steps (+2 points combined)
You're well-positioned at 68—another 15-20 points is totally achievable by moving from "promises detail" to "has detail available where it matters."
Checkout your skill here: SkillzWave.ai | SpillWave We have an agentic skill installer that install skills in 14+ coding agent platforms. Check out this guide on how to improve your agentic skills.
AI-Detected Topics
Extracted using NLP analysis
Report Security Issue
Found a security vulnerability in this agent skill?