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mdr-745-specialist

0.0
D

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.

Commands Agents Marketplace
#clinical evidence#claude-ai#MDR compliance#claudecode-subagents#claude-ai-skills#MDR requirements#requirements#technical documentation

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Installation for Agentic Skill

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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

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Download Agent Skill ZIP

Extract and copy to ~/.claude/skills/ then restart Claude Desktop

1. Clone the repository:
git clone https://github.com/alirezarezvani/claude-skills
2. Copy the agent skill directory:
cp -r claude-skills/ra-qm-team/mdr-745-specialist ~/.claude/skills/

Need detailed installation help? Check our platform-specific guides:

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Agentic Skill Details

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

Spec Compliance
12/15
PDA Architecture
17/30
Ease of Use
18/25
Writing Style
7/10
Utility
13/20
Modifiers: +1

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

  1. Scripts and assets don't exist — You mention mdr-gap-analysis.py and clinical-evidence-tracker.py but only have example.py. Either implement them or remove the specific file references. Vague is better than broken promises.

  2. 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.

  3. 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."

  4. 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."


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AI-Detected Topics

Extracted using NLP analysis

clinical evidence claude-ai MDR compliance claudecode-subagents claude-ai-skills MDR requirements requirements technical documentation claude-skills clinical claude-code MDR Annex MDR anthropic-claude Notified Body claude-code-skills agentic-ai agentic-coding assessment

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