📋 Issue Type

Enhancement / Content Generation

🎯 Objective

Transform incomplete committee reports articles (dates 2026-02-16 through 2026-02-18) from simple link lists into comprehensive analytical articles with full data analysis, context, and commentary using riksdagen/regering MCP data.

📊 Current State

Articles Affected: ~42 articles (3 dates × 14 languages)

• Pattern: news/2026-02-{16,17,18}-committee-reports-{lang}.html
• Current Content: Link lists with document IDs, minimal descriptions like "Committee report on parliamentary matter"
• Missing: Real data analysis, committee context, political significance, cross-committee patterns
• Title Issue: Generic "Committee Reports: Parliamentary Priorities This Week" repeated across all dates
• Description Issue: Generic "Analysis of 10 committee reports revealing Riksdag priorities" - not content-specific

🚀 Desired State

Rich analytical articles with:

• ✅ Document-specific analysis: 150-300 words per report with policy details, political significance
• ✅ Committee context: Full committee names, jurisdiction, political composition
• ✅ Political significance: Coalition dynamics, party positions, legislative strategy
• ✅ Cross-committee patterns: Thematic analysis across multiple reports
• ✅ Forward-looking insights: Expected chamber debates, voting timeline, policy impact
• ✅ Content-based titles: Unique per date reflecting actual policy areas (e.g., "Healthcare Reform and Energy Transition Lead Committee Agenda")
• ✅ Specific descriptions: Reference key reports and policy domains
• ✅ 14-language completeness: Fully translated analysis using translation guides

🔧 Implementation Approach

MCP Data Collection

Use riksdag-regering MCP server tools:

// For each document in articles:
riksdag-regering-get_dokument({
  dok_id: "HD01SkU19", // Example document ID
  include_full_text: true
})

// Committee information:
riksdag-regering-get_utskott()

// Voting data (if available):
riksdag-regering-search_voteringar({
  bet: "SkU19",
  rm: "2025/26"
})

Content Enhancement Process

1. Parse document IDs from existing articles
2. Fetch full details via MCP (title, summary, proposer, status, committee)
3. Generate analysis (150-300 words):
   • Policy content and implications
   • Political context and party positions
   • Coalition dynamics and consensus/conflict
   • Legislative timeline and expected votes
4. Create cross-cutting section identifying themes across reports
5. Write "What to Watch" section with forward-looking insights
6. Generate unique titles/descriptions based on actual content
7. Translate to all 14 languages using translation guides

✅ Acceptance Criteria

Content Quality

• Each document has 150-300 word analysis (not generic text)
• Committee context explained (full name, role, composition)
• Political significance identified with party positions
• Cross-cutting analysis section present
• "What to Watch" section with forward-looking insights
• Unique titles per date reflecting actual policy areas
• Specific descriptions mentioning key reports

Multi-Language Support

• All 14 languages fully translated (EN, SV, DA, NO, FI, DE, FR, ES, NL, AR, HE, JA, KO, ZH)
• No English text in non-English versions
• RTL layout correct for Arabic and Hebrew
• Translation guides followed for terminology consistency

Technical Quality

• HTML validation passes
• Link checking passes
• BreadcrumbList structured data present
• Schema.org metadata updated with new titles/descriptions
• WCAG 2.1 AA accessibility verified

Date Coverage

• 2026-02-16-committee-reports-*.html (14 languages)
• 2026-02-17-committee-reports-*.html (14 languages)
• 2026-02-18-committee-reports-*.html (14 languages)

📚 References

• Complete article example: news/2026-02-13-evening-analysis-en.html
• Incomplete article example: news/2026-02-18-committee-reports-en.html
• MCP Configuration: .github/copilot-mcp.json
• Translation Guide: TRANSLATION_GUIDE.md
• ISMS Policy: https://github.com/Hack23/ISMS-PUBLIC/blob/main/Secure_Development_Policy.md

🤖 Recommended Agent

content-generator - Expert in data-to-narrative transformation, multi-language content, OSINT methodologies for political analysis

📝 Additional Context

Depends on: Issue #306 (provides implementation pattern and quality baseline)

Success Metrics:

• Average word count increases from ~800 to ~2,500 per article
• Unique titles and descriptions for each date (currently repeated)
• 14-language completeness with proper translation
• Enhanced user engagement (time on page, article completion)

Timeline: 6-10 days for 42 articles (3 dates × 14 languages)