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README.md

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🔬 Riksdagsmonitor — Analysis Directory

📊 Political Intelligence Analysis Artifacts for Agentic Workflows
🎯 AI-Driven · Evidence-Based · Methodology-Guided · Never Scripted

Owner Version Effective Date Classification

📋 Document Owner: CEO | 📄 Version: 4.0 | 📅 Last Updated: 2026-03-31 (UTC)
🔄 Review Cycle: Quarterly | ⏰ Next Review: 2026-06-30
🏢 Owner: Hack23 AB (Org.nr 5595347807) | 🏷️ Classification: Public

📚 Architecture Documentation Map

Document Focus Description Documentation Link
Architecture 🏛️ Architecture C4 model showing current system structure View Source
Security Architecture 🛡️ Security Security controls and compliance mapping View Source
Threat Model 🎯 Security Political Threat Landscape analysis View Source
SWOT Analysis 💼 Business Strategic assessment (formatting exemplar) View Source
Data Model 📊 Data Current data structures and relationships View Source
Flowcharts 🔄 Process Current data processing workflows View Source
Workflows ⚙️ DevOps CI/CD and agentic workflow documentation View Source
Analysis Methodologies 📐 Methodology 6 political intelligence analysis frameworks View Source
Analysis Templates 📋 Templates 23 structured analysis output templates (8 core single-type + 15 extended / Tier-C) View Source

🔐 ISMS Policy Alignment

ISMS Policy Analysis Implementation
🛠️ Secure Development Policy Quality gates enforce evidence-based analysis; anti-pattern rejection prevents low-quality output
🤖 AI Policy AI agents MUST read methodology docs before analysis; per-file protocol ensures reproducibility
📝 Classification Policy 7-dimension classification adapted from ISMS for Swedish political events (see reference/)
🔍 Vulnerability Management Political threat analysis uses 6 purpose-built dimensions, NOT software-centric models
🔓 Open Source Policy All methodology documents published under project license for transparency

🚨 CRITICAL RULES — Read Before Any Analysis Work

Rule 1: Folder Isolation — Every Workflow Gets Its Own Folder

analysis/daily/YYYY-MM-DD/{articleType}/

Every agentic workflow MUST write ONLY to its own article-type subfolder. Workflows MUST NEVER write to another workflow's folder. This prevents overwriting.

Workflow Output Folder Example
news-committee-reports analysis/daily/YYYY-MM-DD/committeeReports/ analysis/daily/2026-03-30/committeeReports/
news-propositions analysis/daily/YYYY-MM-DD/propositions/ analysis/daily/2026-03-30/propositions/
news-motions analysis/daily/YYYY-MM-DD/motions/ analysis/daily/2026-03-30/motions/
news-interpellations analysis/daily/YYYY-MM-DD/interpellations/ analysis/daily/2026-03-30/interpellations/
news-evening-analysis analysis/daily/YYYY-MM-DD/evening/ analysis/daily/2026-03-30/evening/
news-realtime-monitor analysis/daily/YYYY-MM-DD/realtime-HHMM/ analysis/daily/2026-03-30/realtime-1400/
news-weekly-review analysis/weekly/YYYY-WNN/ analysis/weekly/2026-W13/
news-monthly-review analysis/monthly/YYYY-MM/ analysis/monthly/2026-03/

Rule 2: Never Overwrite Existing Analysis

An agentic workflow MUST NEVER overwrite analysis produced by another workflow. Each workflow run creates new files in its own scope. If a file already exists, the workflow MUST skip it or create an addendum, never replace.

flowchart LR
    A["Workflow starts"] --> B{"Does target folder<br/>already contain<br/>analysis files?"}
    B -->|"No"| C["✅ Create analysis<br/>in own folder"]
    B -->|"Yes — from SAME workflow"| D["✅ Update/append<br/>to own files only"]
    B -->|"Yes — from DIFFERENT workflow"| E["🚫 NEVER touch<br/>other workflow's files"]

    style C fill:#4CAF50,color:#FFFFFF
    style D fill:#FFC107,color:#000000
    style E fill:#D32F2F,color:#FFFFFF
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Rule 3: AI Performs ALL Analysis — Never Scripted Content

Scripts download data. AI performs ALL analysis. This is a fundamental architectural principle.

✅ Scripts MAY 🚫 Scripts MUST NEVER
Download MCP data to analysis/data/ Generate analysis prose, tables, or conclusions
Catalog pending files Create SWOT entries, risk scores, or threat assessments
Validate output format (quality gate) Fill template sections with generated content
Move/rename files Produce "placeholder" analysis that looks real

The AI agent reads the 6 core methodology guides (of 11 total) and the 8 core output templates (of 23 total), reads the actual data, and produces genuine analytical content based on evidence found in the documents. The remaining 5 methodology files (electoral-domain, per-document, strategic-extensions, structural-metadata, synthesis) provide domain-specific and meta-methodology context; the remaining 15 templates cover Tier-C aggregation (scenario-analysis, executive-brief, coalition-mathematics, election-2026-analysis, historical-parallels, comparative-international, devils-advocate, forward-indicators, implementation-feasibility, intelligence-assessment, media-framing-analysis, methodology-reflection, voter-segmentation, data-download-manifest, cross-reference-map).

Fallback mechanism: If AI analysis fails or produces unusable output (detected by the quality gate bash check in .github/prompts/ — see the README for the module catalogue), the workflow should:

  1. Commit a minimal data-download-manifest.md documenting what was downloaded
  2. Flag the analysis as pending for the next workflow run
  3. Never commit placeholder or stub content that masquerades as genuine analysis

Rule 4: Deep Analysis — Not Shallow Summaries

Every analysis file must demonstrate genuine political intelligence depth. The quality standard is SWOT.md (965 lines of strategic analysis) and THREAT_MODEL.md (2,938 lines of multi-framework threat modeling).

Minimum depth indicators:

  • ≥ 3 evidence-backed claims per SWOT quadrant (with dok_id citations)
  • ≥ 1 color-coded Mermaid diagram per analysis file (with real data, not placeholders)
  • Multi-perspective analysis (government, opposition, citizen, media, international)
  • Explicit confidence labels on every analytical claim
  • Forward-looking indicators (what to watch next, with specific triggers)
  • Cross-document pattern identification (how this document relates to other recent activity)

Rule 5: Mandatory Data Download — ALWAYS Before Analysis

Every agentic workflow MUST download Riksdag data before deciding whether to produce an article. Data collection is NEVER optional:

flowchart TD
    Start(["🚀 Workflow Triggered"]) --> Health["🏥 MCP Health Gate\nget_sync_status"]
    Health -->|"✅ Healthy"| Feeds["📡 Download ALL\nFeed Endpoints"]
    Health -->|"❌ Failed 3x"| Abort(["⛔ Abort Run"])
    Feeds --> Advisory["📋 Download Advisory\nFeeds MANDATORY"]
    Advisory --> Analytics["🔬 Run Analytical\nContext Tools"]
    Analytics --> Gate{"📰 Newsworthy\nItems Found?"}
    Gate -->|"✅ Yes"| Analyze["🤖 Per-File Analysis\n+ Article Generation"]
    Gate -->|"❌ No"| Noop["📝 noop\nwith analysis summary"]

    style Start fill:#1565C0,stroke:#0D47A1,color:#FFFFFF
    style Health fill:#9E9E9E,stroke:#616161,color:#FFFFFF
    style Feeds fill:#2E7D32,stroke:#2E7D32,color:#FFFFFF
    style Advisory fill:#4CAF50,stroke:#2E7D32,color:#FFFFFF
    style Analytics fill:#7B1FA2,stroke:#4A148C,color:#FFFFFF
    style Gate fill:#FFC107,stroke:#FFA000,color:#000000
    style Analyze fill:#FF9800,stroke:#F57C00,color:#FFFFFF
    style Noop fill:#BDBDBD,stroke:#9E9E9E,color:#000000
    style Abort fill:#D32F2F,stroke:#B71C1C,color:#FFFFFF
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Key rules:

  • timeframe: "today" first, fallback to "one-week" for empty/error/timeout feeds
  • Riksdag API can take 30–90+ seconds per call — NEVER abort slow responses
  • Partial data is better than no data — continue with other feeds on individual failures
  • Even on noop, all data collection and analysis MUST complete first

Rule 6: Evidence-Based Only

Every factual claim must have a source citation. Every non-factual assessment must have a confidence level (HIGH/MEDIUM/LOW). Opinion-only entries are REJECTED.

🎯 Purpose

The analysis/ directory stores political intelligence analysis artifacts produced by Riksdagsmonitor's agentic workflows. These artifacts bridge raw Swedish parliamentary data (sourced via the riksdag-regering-mcp server) and the final published political intelligence articles, news summaries, and dashboards across 14 languages.

flowchart LR
    subgraph "📡 Data Collection"
        RD["🏛️ Riksdag MCP\nServer"]
    end

    subgraph "🔬 Analysis Pipeline"
        DL["📥 Download\nFeed Data"]
        AI["🤖 Per-File\nAI Analysis"]
        QG["✅ Analysis Gate\n(05-analysis-gate.md)"]
    end

    subgraph "📰 Article Generation (aggregate → render)"
        AGG["🧩 aggregate-analysis.ts\n→ article.md"]
        REN["🎨 render-articles.ts + render-lib/\n→ $DATE-$SUB-en/sv.html"]
        TR["🌐 news-translate\n→ 12 languages"]
    end

    RD --> DL --> AI --> QG --> AGG --> REN --> TR

    style RD fill:#0D47A1,stroke:#0D47A1,color:#FFFFFF
    style DL fill:#1565C0,stroke:#0D47A1,color:#FFFFFF
    style AI fill:#7B1FA2,stroke:#4A148C,color:#FFFFFF
    style QG fill:#2E7D32,stroke:#2E7D32,color:#FFFFFF
    style AGG fill:#FF9800,stroke:#F57C00,color:#FFFFFF
    style REN fill:#FF9800,stroke:#F57C00,color:#FFFFFF
    style TR fill:#D32F2F,stroke:#B71C1C,color:#FFFFFF
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Analysis artifacts are genuine intelligence products — not summaries or reformatted data — that enable:

  • 🔄 Workflow composition: Upstream agents deposit analysis; downstream agents consume it
  • 📐 Consistent methodology: 6 core frameworks (of 11 methodology files) + 8 core templates (of 23 total output templates) enforce analytical rigor
  • 📊 Full data analysis: Every downloaded MCP file receives per-file deep analysis
  • 🧠 Reusable intelligence: Cross-workflow pattern sharing and knowledge accumulation
  • 🎯 Quality assurance: Minimum 7.0/10 quality gate before article generation
  • 🔀 Collision-free design: Per-workflow directories prevent merge conflicts
  • 📅 Temporal aggregation: Daily → Weekly → Monthly intelligence roll-ups

🏗️ Analysis System Architecture

graph TB
    subgraph "🌐 Data Sources"
        RD["🏛️ Riksdag API<br/><i>Parliamentary data</i>"]
        REG["🏢 Regeringen.se<br/><i>Government data</i>"]
        CIA["📊 CIA Platform<br/><i>Hack23 intelligence</i>"]
    end

    subgraph "📥 Data Ingestion Layer"
        MCP["🔌 riksdag-regering-mcp<br/><i>MCP Server</i>"]
        PRE["📥 download-parliamentary-data.ts<br/><i>Data download + catalog</i>"]
    end

    subgraph "📚 Methodology Framework (v3.0)"
        direction TB
        GUIDE["🤖 AI-Driven Guide<br/><i>Master Protocol</i>"]
        M1["🏷️ Classification"]
        M2["⚠️ Risk"]
        M3["💼 SWOT"]
        M4["🎭 Threat"]
        M5["✍️ Style"]
    end

    subgraph "📋 Template Library (8 Core Single-Type Templates + 15 Extended for Tier-C)"
        T1["🔍 Per-File Intel"]
        T2["🏷️ Classification"]
        T3["⚠️ Risk"]
        T4["💼 SWOT"]
        T5["🎭 Threat"]
        T6["📈 Significance"]
        T7["👥 Stakeholder"]
        T8["🧩 Synthesis"]
    end

    subgraph "🤖 AI Analysis Engine"
        AI["🧠 GitHub Copilot<br/>Coding Agent<br/><i>All analysis performed here</i>"]
    end

    subgraph "✅ Quality Assurance"
        QG["✅ Analysis Gate<br/><i>05-analysis-gate.md validation</i>"]
    end

    subgraph "🧩 Article Pipeline (aggregate → render)"
        AGG["🧩 aggregate-analysis.ts<br/><i>analysis/daily/$DATE/$SUB/*.md → article.md</i>"]
        REN["🎨 render-articles.ts + render-lib/<br/><i>unified → remark → rehype → sanitise → HTML chrome</i>"]
    end

    subgraph "📰 Output"
        ART["📰 News Articles<br/><i>en/sv master + 12 translations</i>"]
        DASH["📊 Dashboards<br/><i>Political intelligence</i>"]
    end

    RD & REG & CIA --> MCP
    MCP --> PRE
    PRE -->|"raw data only"| AI
    GUIDE & M1 & M2 & M3 & M4 & M5 -->|"frameworks"| AI
    T1 & T2 & T3 & T4 & T5 & T6 & T7 & T8 -->|"templates"| AI
    AI -->|"analysis artifacts"| QG
    QG -->|"approved"| AGG
    AGG --> REN
    REN --> ART
    AI -->|"data products"| DASH

    style RD fill:#1565C0,color:#FFFFFF,stroke:#0D47A1,stroke-width:2px
    style REG fill:#1565C0,color:#FFFFFF,stroke:#0D47A1,stroke-width:2px
    style CIA fill:#1565C0,color:#FFFFFF,stroke:#0D47A1,stroke-width:2px
    style MCP fill:#7B1FA2,color:#FFFFFF,stroke:#4A148C,stroke-width:2px
    style PRE fill:#7B1FA2,color:#FFFFFF,stroke:#4A148C,stroke-width:2px
    style GUIDE fill:#D32F2F,color:#FFFFFF,stroke:#B71C1C,stroke-width:2px
    style AI fill:#2E7D32,color:#FFFFFF,stroke:#2E7D32,stroke-width:3px
    style QG fill:#FF9800,color:#FFFFFF,stroke:#F57C00,stroke-width:2px
    style AGG fill:#FF9800,color:#FFFFFF,stroke:#F57C00,stroke-width:2px
    style REN fill:#FF9800,color:#FFFFFF,stroke:#F57C00,stroke-width:2px
    style ART fill:#FFC107,color:#000000,stroke:#FFA000,stroke-width:2px
    style DASH fill:#FFC107,color:#000000,stroke:#FFA000,stroke-width:2px
    style M1 fill:#EEEEEE,color:#212121,stroke:#BDBDBD
    style M2 fill:#EEEEEE,color:#212121,stroke:#BDBDBD
    style M3 fill:#EEEEEE,color:#212121,stroke:#BDBDBD
    style M4 fill:#EEEEEE,color:#212121,stroke:#BDBDBD
    style M5 fill:#EEEEEE,color:#212121,stroke:#BDBDBD
    style T1 fill:#BBDEFB,color:#0D47A1,stroke:#90CAF9
    style T2 fill:#BBDEFB,color:#0D47A1,stroke:#90CAF9
    style T3 fill:#BBDEFB,color:#0D47A1,stroke:#90CAF9
    style T4 fill:#BBDEFB,color:#0D47A1,stroke:#90CAF9
    style T5 fill:#BBDEFB,color:#0D47A1,stroke:#90CAF9
    style T6 fill:#BBDEFB,color:#0D47A1,stroke:#90CAF9
    style T7 fill:#BBDEFB,color:#0D47A1,stroke:#90CAF9
    style T8 fill:#BBDEFB,color:#0D47A1,stroke:#90CAF9
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🧩 Article Pipeline — analysis/ is the article

Articles are not authored in HTML. Every rendered news/$DATE-$SUB-$LANG.html is a pure projection of the markdown artifacts in this directory, produced by two deterministic CLI scripts:

Step Script Input Output
1. Aggregate scripts/aggregate-analysis.ts analysis/daily/$DATE/$SUB/*.md (+ documents/*-analysis.md) analysis/daily/$DATE/$SUB/article.md (canonical, front-mattered)
2. Render scripts/render-articles.ts + scripts/render-lib/ article.md (or article.<lang>.md) news/$DATE-$SUB-$LANG.html (sanitised, chrome-wrapped)
3. Translate (EN+SV → 12 extra) news-translate rendered *-en.html / *-sv.html sibling *-$LANG.html files

The aggregator enforces a canonical narrative order (executive-brief → synthesis → significance → stakeholder → SWOT → risk → threat → per-document intelligence → scenario/forward/comparative/historical → methodology-reflection → data-download-manifest). The renderer uses the unified → remark-parse → remark-gfm → remark-rehype → rehype-raw → rehype-slug → rehype-autolink-headings → rehype-sanitize → rehype-stringify pipeline; Mermaid fences survive as <pre class="mermaid"> and are upgraded to SVG client-side.

Implications for every analysis author (human or AI):

  • ✅ Every .md you write under analysis/daily/$DATE/$SUB/ ships verbatim (modulo sanitisation) to the published article.
  • ✅ Fix quality by fixing the .md, not by editing the rendered HTML (HTML is regenerated on every run).
  • ✅ Relative links in analysis files are auto-rewritten to absolute github.com/Hack23/riksdagsmonitor/blob/main/… URLs so every citation stays auditable.
  • 🚫 There is no scaffold, no AI_MUST_REPLACE marker, no HTML template to fill — see .github/prompts/06-article-generation.md for the full contract.

📚 Documentation Map

Document Type Focus Link
Methodologies README 📚 Index Complete methodology catalog with architecture diagrams View
Templates README 📋 Index Template catalog with usage flow and quality standards View
AI-Driven Guide 🤖 Protocol Master protocol for all AI analysis View
Classification Guide 🏷️ Method 7-dimension political taxonomy View
Risk Methodology ⚠️ Method Cascading risk assessment model View
SWOT Framework 💼 Method TOWS + Cross-SWOT analysis View
Threat Framework 🎭 Method 4-framework threat modeling (v3.0) View
Style Guide ✍️ Standards Evidence citation and writing standards View
ISMS Classification 📖 Reference ISO 27001 → Political classification mapping View
ISMS Risk 📖 Reference ISO 27001 → Political risk mapping View
ISMS Style 📖 Reference ISO 27001 → Political writing mapping View
ISMS Threat 📖 Reference ISO 27001 → Political threat mapping View

🎯 Article-Type-Specific Analytics Matrix

Every agentic workflow produces unique political intelligence tailored to its document type. This is not generic analysis — each workflow downloads type-specific data via MCP and produces analytics that only that document type can provide.

graph TB
    subgraph "📋 Document-Type Workflows"
        CR["📋 Committee Reports<br/><code>get_betankanden</code>"]
        PR["📜 Propositions<br/><code>get_propositioner</code>"]
        MO["✊ Motions<br/><code>get_motioner</code>"]
        IP["❓ Interpellations<br/><code>get_interpellationer</code>"]
    end

    subgraph "📰 Synthesis Workflows"
        EV["🌙 Evening Analysis<br/><code>search_voteringar + search_anforanden</code>"]
        RT["⚡ Realtime Monitor<br/><code>search_dokument + get_calendar_events</code>"]
    end

    subgraph "📊 Aggregation Workflows"
        WR["📅 Weekly Review<br/><code>search_dokument (7-day)</code>"]
        WA["🔮 Week Ahead<br/><code>get_calendar_events (7-day forward)</code>"]
        MR["📊 Monthly Review<br/><code>search_dokument (30-day)</code>"]
        MA["🔮 Month Ahead<br/><code>get_calendar_events (30-day forward)</code>"]
    end

    CR -->|"vote records"| EV
    PR -->|"legislative pipeline"| EV
    MO -->|"opposition patterns"| EV
    IP -->|"accountability gaps"| EV
    EV -->|"daily synthesis"| WR
    RT -->|"breaking events"| WR
    WR -->|"weekly patterns"| MR
    WA -->|"calendar preview"| MA

    style CR fill:#2E7D32,color:#FFFFFF,stroke:#2E7D32,stroke-width:2px
    style PR fill:#1565C0,color:#FFFFFF,stroke:#0D47A1,stroke-width:2px
    style MO fill:#FF9800,color:#FFFFFF,stroke:#F57C00,stroke-width:2px
    style IP fill:#D32F2F,color:#FFFFFF,stroke:#B71C1C,stroke-width:2px
    style EV fill:#7B1FA2,color:#FFFFFF,stroke:#4A148C,stroke-width:2px
    style RT fill:#C2185B,color:#FFFFFF,stroke:#880E4F,stroke-width:2px
    style WR fill:#4CAF50,color:#000000,stroke:#2E7D32,stroke-width:2px
    style WA fill:#2196F3,color:#000000,stroke:#0097A7,stroke-width:2px
    style MR fill:#E91E63,color:#FFFFFF,stroke:#C2185B,stroke-width:2px
    style MA fill:#FF5722,color:#FFFFFF,stroke:#e64a19,stroke-width:2px
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Per-Workflow Unique Analytics

Workflow Primary MCP Tool Unique Analytics Only This Workflow Produces Cross-Reference Tools
Committee Reports get_betankanden Committee voting splits, reservation analysis (dissenting opinions), committee-to-policy-domain mapping, fiscal/defence/healthcare committee specialisation metrics search_voteringar (by beteckning), search_anforanden, get_propositioner
Propositions get_propositioner Legislative pipeline tracking (committee referral → vote timeline), government legislative ambition scoring, policy domain impact analysis, budget allocation implications search_dokument, analyze_g0v_by_department
Motions get_motioner Opposition party strategy analysis (which parties push which issues), motion clustering by theme, cross-party alignment detection, signalverdi (signal value) for upcoming legislative battles search_dokument_fulltext, search_anforanden
Interpellations get_interpellationer Ministerial accountability scoring (response rate, timeliness, evasion detection), question-to-response quality analysis, party oversight strategy mapping search_anforanden (minister speeches), get_calendar_events
Evening Analysis search_voteringar + search_anforanden Daily parliamentary pulse (vote results + debate intensity), party discipline metrics, coalition cohesion scoring, MP-level voting deviation from party line get_betankanden, get_propositioner, get_calendar_events
Realtime Monitor search_dokument + get_calendar_events Breaking event detection, urgency classification, real-time political temperature index All MCP tools as needed
Weekly Review search_dokument (7-day window) Week-over-week trend detection, cross-document-type pattern identification, political narrative arc tracking search_voteringar, search_anforanden
Week Ahead get_calendar_events (forward 7 days) Prospective calendar analysis, scheduled debate preview, expected voting outcomes based on committee composition search_dokument, get_fragor, get_interpellationer
Monthly Review search_dokument (30-day window) Monthly legislative throughput metrics, party productivity rankings, government vs opposition scorecard All MCP tools for comprehensive retrospective
Month Ahead get_calendar_events (forward 30 days) Strategic political calendar, legislative pipeline forecast, major policy decision timeline search_dokument, get_betankanden, get_propositioner

Per-Document Unique Intelligence (Examples)

For high-significance documents (significance score ≥ 70), workflows produce document-specific deep dives:

Document Type Example dok_id Unique Deep-Dive Analysis
Committee Report H901JuU15 Voting split heatmap by party, reservation text analysis, comparison with original proposition intent, dissent pattern vs committee norms
Proposition H9032025/26:227 Policy impact chain (which laws change → who is affected), budget envelope impact, comparison with previous government bills in same domain
Motion H902mot1234 Opposition signalling analysis (is this a positioning motion or serious legislative bid?), cross-party co-sponsorship network, historical motion success rate for this topic
Interpellation H902ip456 Question framing analysis (adversarial vs inquiry), minister response timeliness, evasion score (did the minister actually answer?), policy commitment extraction

📁 Directory Structure

analysis/
├── README.md                          ← This file (CRITICAL RULES — read first)
├── data/                              ← Persistent MCP data repository (collision-free)
│   ├── README.md                      ← Data repository documentation
│   ├── documents/                     ← Parliamentary documents by type
│   │   ├── propositions/              ← Government propositions ({dok_id}.json + .meta.json)
│   │   ├── motions/                   ← Parliamentary motions
│   │   ├── committeeReports/          ← Committee reports
│   │   ├── votes/                     ← Voting records
│   │   ├── speeches/                  ← Parliamentary speeches
│   │   ├── questions/                 ← Written questions
│   │   └── interpellations/           ← Interpellations
│   ├── votes/                         ← Date-stamped vote ballots (YYYY-MM-DD/)
│   ├── events/                        ← Date-stamped calendar events (YYYY-MM-DD/)
│   ├── mps/                           ← MP profiles (intressent_id.json)
│   ├── worldbank/                     ← World Bank economic indicators
│   ├── scb/                           ← Statistics Sweden (SCB) table data
│   └── mcp-responses/                 ← Generic MCP tool response archive
├── templates/                         ← Analysis templates (AI fills these — NEVER scripts)
│   ├── political-classification.md    ← Event classification template
│   ├── risk-assessment.md             ← Political risk template (5×5 matrix + cascading risk)
│   ├── threat-analysis.md             ← Multi-framework threat template (Attack Trees + Kill Chain)
│   ├── swot-analysis.md               ← SWOT quadrant template (evidence-based, intersection analysis)
│   ├── stakeholder-impact.md          ← Stakeholder impact template (6 analytical lenses)
│   ├── significance-scoring.md        ← Significance scoring template (5-dimension rubric)
│   ├── synthesis-summary.md           ← Daily synthesis template (aggregates all above)
│   └── per-file-political-intelligence.md ← Per-file AI analysis template
├── methodologies/                     ← Detailed methodology guides (AI MUST read ALL before analyzing)
│   ├── ai-driven-analysis-guide.md    ← Master protocol: folder isolation, AI-only analysis, quality gates
│   ├── political-classification-guide.md ← Multi-dimensional classification, political temperature
│   ├── political-risk-methodology.md  ← 5×5 matrix, cascading risk, Bayesian updating
│   ├── political-threat-framework.md  ← Attack Trees, Kill Chain, Diamond Model, Political Threat Taxonomy
│   ├── political-swot-framework.md    ← Evidence hierarchy, cross-SWOT interference, scenario generation
│   └── political-style-guide.md       ← Intelligence writing standards, evidence density, attribution
├── reference/                         ← ISMS adaptation mappings
│   ├── isms-classification-adaptation.md
│   ├── isms-risk-assessment-adaptation.md
│   ├── isms-threat-modeling-adaptation.md
│   └── isms-style-guide-adaptation.md
├── daily/                             ← Per-day analysis (YYYY-MM-DD/{articleType}/ — ISOLATED per workflow)
│   └── README.md
├── weekly/                            ← Per-week aggregations (YYYY-WNN/)
│   └── README.md
└── monthly/                           ← Per-month strategic briefs (YYYY-MM/)
    └── README.md

🔒 Folder Isolation Model (Critical)

analysis/daily/2026-03-30/              ← Date folder
├── committeeReports/                   ← news-committee-reports ONLY writes here
│   ├── documents/                      ← Per-file analyses
│   │   ├── H901AU10-analysis.md
│   │   └── H901JuU25-analysis.md
│   ├── synthesis-summary.md
│   └── data-download-manifest.md
├── propositions/                       ← news-propositions ONLY writes here
│   ├── documents/
│   │   ├── H901prop227-analysis.md
│   │   └── H901prop213-analysis.md
│   ├── synthesis-summary.md
│   └── data-download-manifest.md
├── motions/                            ← news-motions ONLY writes here
│   └── ...
├── interpellations/                    ← news-interpellations ONLY writes here
│   └── ...
├── evening/                            ← news-evening-analysis ONLY writes here
│   └── ...
└── realtime-1400/                      ← news-realtime-monitor ONLY writes here (timestamped)
    └── ...

Enforcement: Each workflow's git add scope MUST be limited to its own subfolder:

# ✅ CORRECT — scoped to article type
git add "analysis/daily/${ARTICLE_DATE}/${DOC_TYPE}/"

# 🚫 WRONG — broad scope can overwrite other workflows
git add "analysis/daily/${ARTICLE_DATE}/"

📅 Naming Conventions

Scope Format Example Description
Daily YYYY-MM-DD 2026-03-26/ ISO 8601 calendar date
Weekly YYYY-WNN 2026-W13/ ISO 8601 week number (Mon–Sun)
Monthly YYYY-MM 2026-03/ ISO 8601 year-month
Ad-hoc descriptive coalition-risk/ Named topic directories when needed

Rules:

  • All directory names use zero-padded numbers (W03, not W3)
  • Weekly directories align with ISO 8601: weeks start Monday
  • Never use locale-specific date formats (no 26/3/2026 or Mar-26)

🤖 Workflow Integration

The following agentic workflows produce analysis artifacts. All workflows MUST:

  1. Write ONLY to their own article-type subfolder (folder isolation)
  2. Never overwrite analysis produced by another workflow
  3. Follow the AI-driven analysis protocol (read methodologies, then analyze)
  4. Never use scripts to generate analytical content

🌅 Daily Morning Workflows (scheduled Mon–Fri)

Workflow Schedule Output Folder Primary Output
news-committee-reports 04:00 UTC daily/YYYY-MM-DD/committeeReports/ Committee report analysis
news-propositions 05:00 UTC daily/YYYY-MM-DD/propositions/ Proposition analysis
news-motions 06:00 UTC daily/YYYY-MM-DD/motions/ Motion analysis
news-interpellations 07:00 UTC daily/YYYY-MM-DD/interpellations/ Interpellation analysis

🌆 news-evening-analysis (18:00 UTC Mon–Fri, 16:00 UTC Sat)

Output folder: daily/YYYY-MM-DD/evening/

The evening analysis workflow is the most comprehensive. It:

  1. Downloads data via populate-analysis-data.ts + download-parliamentary-data.ts (scripts for DATA only)
  2. AI reads ALL 6 methodology guides + ALL 8 templates
  3. AI performs per-file analysis on all pending files using genuine analytical reasoning
  4. AI composes daily synthesis from per-file analyses
  5. AI generates evening analysis articles — all content is AI-produced intelligence

📡 news-realtime-monitor (10:00+14:00 UTC Mon–Fri, 12:00 UTC weekends)

Output folder: daily/YYYY-MM-DD/realtime-HHMM/ (timestamped to prevent overwrites)

Real-time monitoring of parliamentary activity with per-file analysis on new data. Each run gets a unique timestamped folder so successive runs never overwrite each other.

📅 Weekly & Monthly Workflows

Workflow Schedule Output Folder Output
news-week-ahead Fridays 07:00 UTC weekly/YYYY-WNN/week-ahead/ Weekly forecast + aggregated SWOT
news-weekly-review Scheduled weekly/YYYY-WNN/review/ Weekly parliamentary wrap-up
news-month-ahead Scheduled monthly/YYYY-MM/month-ahead/ Monthly forecast
news-monthly-review Scheduled monthly/YYYY-MM/review/ Monthly strategic brief

📐 Template Usage Guide

Using a Template

  1. Copy the template from analysis/templates/ to the appropriate dated subdirectory
  2. Rename using scope/workflow conventions from the target directory README.md (e.g. daily: morning-risk-snapshot.md / evening-swot-update.md / realtime-HHMM-risk-delta.md, weekly: week-summary-swot.md, monthly: monthly-risk-register.md)
  3. Fill all required fields (marked [REQUIRED])
  4. Complete optional fields where evidence is available
  5. Validate against the methodology guide before consuming downstream

Template Quick Reference

Template When to Use Key Output
political-classification.md New political event arrives Sensitivity + urgency classification
risk-assessment.md Coalition/policy risk spike Risk scores + mitigation map
threat-analysis.md Political Threat Taxonomy review Threat inventory + actor mapping
swot-analysis.md Weekly/strategic SWOT pass Quadrant entries with evidence
stakeholder-impact.md Policy decision announced Impact by stakeholder group
significance-scoring.md Deciding what to publish Composite score → publish/skip
synthesis-summary.md Daily synthesis (aggregation) Combined intelligence dashboard
per-file-political-intelligence.md Per-file AI analysis Full deep analysis per document

📚 Related Scripts

Path Purpose
scripts/catalog-downloaded-data.ts Catalog downloaded files, list pending analysis
scripts/populate-analysis-data.ts Standalone MCP data fetcher (7 data types)
scripts/download-parliamentary-data.ts Orchestrates raw data download and persistence (manifest + JSON)
scripts/analysis-framework/ Core analysis pipeline (TypeScript)
scripts/analysis-framework/lenses/ Per-perspective classifiers (citizen, economic, government, international, media, opposition)
scripts/analysis-framework/significance-scorer.ts Significance score computation
scripts/analysis-framework/cross-reference.ts Cross-document reference linking
scripts/parliamentary-data/data-persistence.ts MCP data persistence to analysis/data/
scripts/parliamentary-data/data-downloader.ts Document download from riksdag-regering-mcp
scripts/ai-analysis/ AI-assisted analysis generation
scripts/ai-analysis/swot/ SWOT generation pipeline
scripts/analysis-reader.ts Read daily analysis files with fallback
scripts/prompts/v2/ LLM prompt templates for analysis (v2)

📊 Per-File AI Analysis Quality Gate

Every per-file analysis must score ≥ 7.0/10 across the 5 weighted dimensions defined in methodologies/ai-driven-analysis-guide.md:

Dimension Weight Minimum Description
Evidence 25% 6/10 Evidence density, citations per claim, source variety, dok_id references
Depth 25% 6/10 Multi-framework application, analytical depth, cross-reference density, non-trivial insights
Structural 20% 7/10 All template sections filled, required Mermaid diagrams present, coherent flow
Actionable 15% 6/10 Clear implications, decision support, Riksdag-specific relevance, stakeholder identification
Neutrality 15% 7/10 Balanced tone, writing quality, style guide compliance, clarity, no boilerplate or bias

🔒 ISMS Compliance Framework Mapping

ISO 27001:2022 Controls

Control Title Analysis Implementation
A.5.1 Policies for information security All analysis methodologies align with Hack23 ISMS policy framework
A.5.10 Acceptable use of information Classification guide defines sensitivity-based data handling
A.5.33 Protection of records Style guide enforces evidence citation and audit trail
A.8.3 Information access restriction Sensitivity levels (PUBLIC/SENSITIVE/RESTRICTED) gate access
A.8.10 Information deletion 180-day SWOT decay rule ensures stale data is removed
A.8.28 Secure coding AI analysis guide enforces structured, reviewable output with quality gates

NIST CSF 2.0 Functions

Function Analysis Relevance
Identify (ID) Classification guide identifies and categorizes Riksdag events by sensitivity and impact
Protect (PR) Style guide protects analytical quality through evidence requirements and anti-patterns
Detect (DE) Threat framework detects political threats across 6 dimensions using multiple analytical models
Respond (RS) Risk methodology provides quantified risk scores enabling proportionate response
Recover (RC) SWOT framework supports strategic recovery planning through forward-looking opportunity analysis

CIS Controls v8.1

Control Title Analysis Relevance
Control 1 Inventory and Control of Enterprise Assets Classification guide inventories and categorizes all Riksdag data assets
Control 3 Data Protection Sensitivity levels enforce appropriate handling for each data classification
Control 8 Audit Log Management AI analysis guide requires documented quality gate assessments (audit trail)
Control 14 Security Awareness and Skills Training Methodology documents serve as training material for AI agents and analysts
Control 16 Application Software Security Quality gates enforce structured, validated analytical output

🔒 ISMS Adaptation Reference

The reference/ directory maps ISMS security frameworks to political intelligence:

Reference Document Source ISMS Document Political Adaptation
isms-classification-adaptation.md ISO 27001 A.5.12–A.5.13, Classification Policy Confidentiality → Sensitivity, Integrity → Accuracy, Availability → Urgency
isms-risk-assessment-adaptation.md ISO 27001 A.8.8, Risk Assessment Methodology CIA Triad → Political Triad (Accountability, Policy Fidelity, Democratic Continuity)
isms-threat-modeling-adaptation.md ISO 27001 A.5.7, NIST CSF ID.RA-3 Political Threat Landscape + Attack Trees + Diamond Model
isms-style-guide-adaptation.md STYLE_GUIDE ISMS writing standards → Political intelligence writing standards

🔗 Related Documentation

🤖 Per-File AI Analysis (Primary Analysis Mode)

The primary analysis mode is per-file AI-driven analysis: for every downloaded MCP data file, the AI agent produces a deep analysis markdown file. The AI reads all methodology guides, reads the actual data, and produces genuine political intelligence — not script-generated summaries.

How It Works

flowchart LR
    A["📥 MCP Download<br/>(scripts — DATA only)"] --> B["📋 Catalog<br/>pending files"]
    B --> C["📖 AI reads ALL<br/>6 methodology guides<br/>+ 8 templates"]
    C --> D["🔍 AI performs<br/>genuine analysis<br/>per document"]
    D --> E["💾 {id}-analysis.md<br/>in workflow's folder"]
    E --> F["📊 AI composes<br/>daily synthesis"]

    style A fill:#1565C0,color:#FFFFFF
    style C fill:#7B1FA2,color:#FFFFFF
    style D fill:#4CAF50,color:#FFFFFF
    style F fill:#7B1FA2,color:#FFFFFF
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Step Action Responsible Tool / Reference
1. Download Scripts fetch MCP data to analysis/data/ Scripts scripts/populate-analysis-data.ts
2. Catalog List files needing analysis Scripts scripts/catalog-downloaded-data.ts --pending-only
3. Read methods AI reads ALL 6 methodology docs + 8 templates AI analysis/methodologies/*.md + analysis/templates/*.md
4. Analyze AI applies multi-framework analysis to each file AI Evidence-based reasoning, not scripts
5. Write Save {id}-analysis.md in workflow's isolated folder AI e.g. analysis/daily/YYYY-MM-DD/propositions/documents/H901prop227-analysis.md
6. Synthesize AI composes synthesis from per-file analyses AI analysis/daily/YYYY-MM-DD/{articleType}/synthesis-summary.md

What "Genuine AI Analysis" Means (vs. Scripted Content)

✅ Genuine AI Analysis 🚫 Scripted/Shallow Content
"Proposition 2025/26:227 strengthens criminal penalties for gang crime, extending minimum sentences from 4→6 years. Coalition partner L has historically resisted harsh sentencing (see L motion 2024/25:1234), creating potential friction. SD has publicly demanded even stricter measures (interpellation 2025/26:456). Risk: L could break ranks on floor vote, though KD mediation has historically bridged such gaps (vote record H901JuU15). [MEDIUM confidence]" "This proposition relates to justice policy. The government's position is strengthened. [MEDIUM confidence]"
Cross-references 3+ documents, names specific actors with party, identifies tension dynamics, provides forward risk assessment Generic summary with no specific data, no cross-references, no named actors

Methodology Documents (AI Must Read Before Analyzing)

Priority Document Key Analytical Frameworks
🔴 1 political-swot-framework.md Evidence hierarchy, confidence levels, temporal decay, cross-SWOT interference, strategic scenario generation
🔴 2 political-risk-methodology.md 5×5 Likelihood×Impact, cascading risk analysis, Bayesian updating, risk interconnection mapping
🔴 3 political-threat-framework.md Attack Trees, Political Kill Chain, Diamond Model, Political Threat Taxonomy, threat actor profiling
🟠 4 political-classification-guide.md Multi-dimensional classification, political temperature index, strategic significance
🟠 5 political-style-guide.md Intelligence writing standards, evidence density requirements, attribution rules
🟠 6 ai-driven-analysis-guide.md Master protocol: folder isolation, AI-only analysis, quality gates, time budget

Analysis Prompts (v2)

Prompt Purpose
per-file-intelligence-analysis.md Step-by-step per-file analysis protocol
political-analysis.md Core political analysis framework (6 lenses)
swot-generation.md SWOT generation with pre-computed data
political-risk-prompt.md Risk assessment prompt
political-threat-prompt.md Threat analysis prompt
quality-criteria.md Quality self-assessment rubric (≥7/10)

Conflict Resolution

Because each workflow writes to its own isolated folder:

  • Per-file analyses ({id}-analysis.md) are conflict-free — each file analyzed independently in its workflow's folder
  • Daily synthesis files are scoped to each workflow's folder — no cross-workflow conflicts
  • Weekly aggregations read from all daily folders (read-only) to compose weekly intelligence
  • Realtime monitor uses timestamped folders (realtime-HHMM/) so successive runs never overwrite

Quality Standard: Every per-file analysis must match SWOT.md and THREAT_MODEL.md formatting quality — Hack23 header badges, color-coded Mermaid diagrams, evidence tables with confidence labels, multi-framework analysis, and actionable intelligence.

Document Control:

📊 Temporal Aggregation Architecture

graph LR
    subgraph "📅 Daily Analysis"
        D1["📄 Per-Document<br/>Analyses"]
        D2["🧩 Daily<br/>Synthesis"]
    end

    subgraph "📆 Weekly Intelligence"
        W1["📰 Weekly<br/>Review"]
        W2["🔮 Week<br/>Ahead"]
    end

    subgraph "📅 Monthly Intelligence"
        M1["📊 Monthly<br/>Review"]
        M2["🔮 Month<br/>Ahead"]
    end

    D1 -->|"aggregate"| D2
    D2 -->|"7-day roll-up"| W1
    D2 -->|"prospective"| W2
    W1 -->|"4-week roll-up"| M1
    W2 -->|"monthly forecast"| M2

    style D1 fill:#1565C0,color:#FFFFFF,stroke:#0D47A1,stroke-width:2px
    style D2 fill:#1565C0,color:#FFFFFF,stroke:#0D47A1,stroke-width:2px
    style W1 fill:#FF9800,color:#FFFFFF,stroke:#F57C00,stroke-width:2px
    style W2 fill:#7B1FA2,color:#FFFFFF,stroke:#4A148C,stroke-width:2px
    style M1 fill:#D32F2F,color:#FFFFFF,stroke:#B71C1C,stroke-width:2px
    style M2 fill:#C2185B,color:#FFFFFF,stroke:#880E4F,stroke-width:2px
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📊 Hack23 AB — Political Intelligence Through Systematic Analysis