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Initial commit: Claude Skills Factory with 8 refined custom skills

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Custom Skills (ourdigital-custom-skills/):
- 00-ourdigital-visual-storytelling: Blog featured image prompt generator
- 01-ourdigital-research-publisher: Research-to-publication workflow
- 02-notion-organizer: Notion workspace management
- 03-research-to-presentation: Notion research to PPT/Figma
- 04-seo-gateway-strategist: SEO gateway page strategy planning
- 05-gateway-page-content-builder: Gateway page content generation
- 20-jamie-brand-editor: Jamie Clinic branded content GENERATION
- 21-jamie-brand-guardian: Jamie Clinic content REVIEW & evaluation

Refinements applied:
- All skills converted to SKILL.md format with YAML frontmatter
- Added version fields to all skills
- Flattened nested folder structures
- Removed packaging artifacts (.zip, .skill files)
- Reorganized file structures (scripts/, references/, etc.)
- Differentiated Jamie skills with clear roles

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
Andrew Yim
2025-12-10 17:56:04 +09:00
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ourdigital-custom-skills/20-jamie-brand-editor/scripts/compliance_checker.py Normal file
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"""
Jamie Marketing Brand Editor - Compliance Checker
==================================================
This script automatically scans marketing content for Korean medical advertising
law violations (의료법 제56조) and flags problematic content.
Usage:
python compliance_checker.py --input content.txt --output report.json
Or import as module:
from compliance_checker import ComplianceChecker
checker = ComplianceChecker()
results = checker.check_content(content_text)
"""
import re
import json
from typing import Dict, List, Tuple
from dataclasses import dataclass, asdict
@dataclass
class ComplianceViolation:
"""Represents a single compliance violation found in content"""
violation_type: str
severity: str # 'critical', 'high', 'medium', 'low'
location: Tuple[int, int] # (start_pos, end_pos)
matched_text: str
explanation_korean: str
suggestion: str
legal_reference: str
class ComplianceChecker:
"""
Checks marketing content for violations of Korean medical advertising law.
"""
def __init__(self):
self.prohibited_patterns = self._load_prohibited_patterns()
self.required_disclaimers = self._load_required_disclaimers()
def _load_prohibited_patterns(self) -> Dict[str, List[str]]:
"""
Load regex patterns for prohibited content types.
"""
return {
'effect_guarantee': [
r'100[%％]\s*(?:만족|효과|성공)',
r'반드시\s+(?:효과|개선|만족)',
r'완벽한?\s+결과',
r'보장합니다',
r'확실한?\s+효과',
],
'comparative_superiority': [
r'최고의?',
r'1위',
r'(?:압구정|강남|서울|국내)\s*(?:최고|1위)',
r'타\s*병원보다',
r'다른\s*(?:병원|의원)보다\s*우수',
],
'safety_guarantee': [
r'부작용\s*(?:없|無)',
r'(?:100[%％]|완전히?|절대)\s*안전',
r'위험\s*(?:없|無)',
],
'patient_testimonial': [
r'(?:환자|고객)\s*[A-Z가-힣]+\s*(?:씨|님)의?\s*(?:후기|경험)',
r'실제\s*(?:환자|고객)\s*(?:후기|리뷰|경험담)',
r'[""]\s*(?:정말|너무|진짜)\s+(?:만족|좋아요|감사)', # Quoted testimonials
r'수술\s*후\s*[0-9]+\s*(?:개월|주일|년)\s*(?:만족|경과)',
],
'exaggeration': [
r'(?:놀라운|대박|극적인)\s*(?:변화|효과|결과)',
r'마법같은?',
r'기적적인?',
],
}
def _load_required_disclaimers(self) -> Dict[str, str]:
"""
Load templates for required disclaimers.
"""
return {
'general_surgery': '※ 모든 수술 및 시술은 개인에 따라 붓기, 멍, 염증 등의 부작용이 발생할 수 있습니다.',
'individual_variation': '※ 수술 결과는 개인의 특성에 따라 차이가 있을 수 있습니다.',
'consultation_required': '※ 수술 전 반드시 전문의와 충분한 상담을 통해 결정하시기 바랍니다.',
}
def check_content(self, content: str) -> Dict:
"""
Main method to check content for compliance violations.
Args:
content: Korean text content to check
Returns:
Dictionary containing violations and recommendations
"""
violations = []
# Check for prohibited patterns
for violation_type, patterns in self.prohibited_patterns.items():
for pattern in patterns:
for match in re.finditer(pattern, content, re.IGNORECASE):
violation = self._create_violation(
violation_type=violation_type,
match=match,
content=content
)
violations.append(violation)
# Check for missing required disclaimers
disclaimer_issues = self._check_disclaimers(content)
violations.extend(disclaimer_issues)
# Generate compliance report
report = {
'is_compliant': len(violations) == 0,
'total_violations': len(violations),
'violations_by_severity': self._count_by_severity(violations),
'violations': [asdict(v) for v in violations],
'recommendations': self._generate_recommendations(violations),
'required_disclaimers': list(self.required_disclaimers.values())
}
return report
def _create_violation(self, violation_type: str, match, content: str) -> ComplianceViolation:
"""
Create a ComplianceViolation object from a regex match.
"""
explanations = {
'effect_guarantee': '효과를 보장하는 표현은 의료법 제56조 제2항 제2호 위반입니다.',
'comparative_superiority': '타 의료기관과의 비교 우위 주장은 의료법 제56조 제2항 제4호 위반입니다.',
'safety_guarantee': '안전성을 보장하거나 부작용이 없다는 표현은 의료법 제56조 제2항 제7호 위반입니다.',
'patient_testimonial': '환자 치료경험담은 의료법 제56조 제2항 제2호 위반입니다.',
'exaggeration': '과장된 표현은 객관적 사실과 다른 내용으로 의료법 제56조 제2항 제3호 위반 가능성이 있습니다.',
}
suggestions = {
'effect_guarantee': '"개선에 도움을 줄 수 있습니다" 또는 "개인에 따라 결과가 다를 수 있습니다"와 같은 표현으로 변경하세요.',
'comparative_superiority': '"풍부한 경험을 보유한" 또는 "전문적인"과 같은 객관적 표현으로 변경하세요.',
'safety_guarantee': '부작용 가능성을 명시하고 "안전한 수술을 위해 최선을 다합니다"와 같은 표현으로 변경하세요.',
'patient_testimonial': '개인 환자 경험담 대신 통계적 데이터나 일반적인 수술 과정 설명으로 대체하세요.',
'exaggeration': '객관적이고 절제된 표현으로 변경하세요. 예: "자연스러운 개선", "점진적인 효과"',
}
severity_map = {
'effect_guarantee': 'critical',
'comparative_superiority': 'critical',
'safety_guarantee': 'critical',
'patient_testimonial': 'critical',
'exaggeration': 'high',
}
return ComplianceViolation(
violation_type=violation_type,
severity=severity_map.get(violation_type, 'medium'),
location=(match.start(), match.end()),
matched_text=match.group(),
explanation_korean=explanations.get(violation_type, ''),
suggestion=suggestions.get(violation_type, ''),
legal_reference='의료법 제56조'
)
def _check_disclaimers(self, content: str) -> List[ComplianceViolation]:
"""
Check if required disclaimers are present in content.
"""
violations = []
# Check if content discusses surgery/procedures
procedure_keywords = ['수술', '시술', '이마거상', '쌍꺼풀', '리프팅', '보톡스', '필러']
has_procedure_content = any(keyword in content for keyword in procedure_keywords)
if has_procedure_content:
# Check for required disclaimers
has_side_effect_notice = any(term in content for term in ['부작용', '합병증', '붓기', '멍'])
has_individual_variation = '개인' in content and any(term in content for term in ['차이', '다를 수'])
if not has_side_effect_notice:
violations.append(ComplianceViolation(
violation_type='missing_disclaimer',
severity='high',
location=(-1, -1),
matched_text='',
explanation_korean='부작용 가능성에 대한 고지가 누락되었습니다.',
suggestion='페이지 하단에 "※ 모든 수술 및 시술은 개인에 따라 붓기, 멍, 염증 등의 부작용이 발생할 수 있습니다." 문구를 추가하세요.',
legal_reference='의료법 제56조 제2항 제7호'
))
if not has_individual_variation:
violations.append(ComplianceViolation(
violation_type='missing_disclaimer',
severity='medium',
location=(-1, -1),
matched_text='',
explanation_korean='개인차에 대한 고지가 누락되었습니다.',
suggestion='"개인에 따라 결과가 다를 수 있습니다" 문구를 추가하세요.',
legal_reference='의료법 시행령 제23조'
))
return violations
def _count_by_severity(self, violations: List[ComplianceViolation]) -> Dict[str, int]:
"""Count violations by severity level."""
counts = {'critical': 0, 'high': 0, 'medium': 0, 'low': 0}
for v in violations:
counts[v.severity] += 1
return counts
def _generate_recommendations(self, violations: List[ComplianceViolation]) -> List[str]:
"""Generate actionable recommendations based on violations found."""
recommendations = []
if any(v.violation_type == 'patient_testimonial' for v in violations):
recommendations.append('환자 후기를 제거하고 통계적 만족도 데이터로 대체하세요.')
if any(v.violation_type in ['effect_guarantee', 'safety_guarantee'] for v in violations):
recommendations.append('절대적인 보장 표현을 가능성 표현으로 변경하세요. 예: "도움을 줄 수 있습니다"')
if any(v.violation_type == 'comparative_superiority' for v in violations):
recommendations.append('비교 우위 표현을 제거하고 객관적 사실(경력, 경험)로 대체하세요.')
if any(v.violation_type == 'missing_disclaimer' for v in violations):
recommendations.append('페이지 하단에 필수 고지사항을 추가하세요.')
return recommendations
def main():
"""Command-line interface for compliance checker."""
import argparse
parser = argparse.ArgumentParser(description='Check medical marketing content for compliance')
parser.add_argument('--input', '-i', required=True, help='Input content file')
parser.add_argument('--output', '-o', default='compliance_report.json', help='Output report file')
parser.add_argument('--verbose', '-v', action='store_true', help='Print detailed output')
args = parser.parse_args()
# Read input content
with open(args.input, 'r', encoding='utf-8') as f:
content = f.read()
# Run compliance check
checker = ComplianceChecker()
report = checker.check_content(content)
# Save report
with open(args.output, 'w', encoding='utf-8') as f:
json.dump(report, f, ensure_ascii=False, indent=2)
# Print summary
print(f"Compliance Check Complete")
print(f"==========================================")
print(f"Compliant: {'YES ✓' if report['is_compliant'] else 'NO ✗'}")
print(f"Total Violations: {report['total_violations']}")
print(f" - Critical: {report['violations_by_severity']['critical']}")
print(f" - High: {report['violations_by_severity']['high']}")
print(f" - Medium: {report['violations_by_severity']['medium']}")
print(f" - Low: {report['violations_by_severity']['low']}")
print(f"\nReport saved to: {args.output}")
if args.verbose and report['violations']:
print(f"\nViolations Found:")
for v in report['violations']:
print(f"\n Type: {v['violation_type']}")
print(f" Severity: {v['severity']}")
print(f" Text: '{v['matched_text']}'")
print(f" Explanation: {v['explanation_korean']}")
if __name__ == '__main__':
main()