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// SPDX-License-Identifier: Apache-2.0
/**
* @module Generators/Pipeline/AnalysisExisting
* @description Existing analysis method builders for the analysis pipeline.
*
* Contains markdown builders for the **Existing** analysis method group and
* cross-cutting meta methods:
* - `deep-analysis` — deep multi-perspective analysis
* - `stakeholder-analysis` — stakeholder impact analysis
* - `coalition-analysis` — coalition cohesion analysis
* - `voting-patterns` — voting pattern trend analysis
* - `cross-session-intelligence` — cross-session coalition intelligence
* - `synthesis-summary` — aggregated synthesis from all per-file analyses
* - `document-analysis` — per-document intelligence analysis (opt-in)
*/
import path from 'path';
import type { PoliticalSignificance } from '../../types/political-classification.js';
import type { PoliticalThreatAssessment } from '../../types/political-threats.js';
import {
detectVotingTrends,
computeCrossSessionCoalitionStability,
} from '../../utils/intelligence-analysis.js';
import { assessPoliticalSignificance } from '../../utils/political-classification.js';
import { assessPoliticalThreats } from '../../utils/political-threat-assessment.js';
import {
buildQuantitativeSWOT,
createScoredSWOTItem,
createScoredOpportunityOrThreat,
} from '../../utils/political-risk-assessment.js';
import { ensureDirectoryExists } from '../../utils/file-utils.js';
import { buildSynthesisSummary, formatSynthesisMarkdown } from '../synthesis-summary.js';
import {
sanitizeCell,
sanitizeDocumentId,
DOCUMENT_FEED_KEYS,
extractDocumentId,
extractDocumentTitle,
safeArr,
toClassificationInput,
toThreatInput,
buildMarkdownHeader,
writeTextFile,
} from './analysis-helpers.js';
import type { MarkdownBuilder } from './analysis-helpers.js';
import type { AnalysisMethod } from './analysis-stage.js';
/** Analysis method identifier for synthesis summary */
export const METHOD_SYNTHESIS_SUMMARY_ID = 'synthesis-summary' as const;
/** Analysis method identifier for per-document intelligence analysis */
export const METHOD_DOCUMENT_ANALYSIS = 'document-analysis' as const;
// ─── Per-method markdown builders ────────────────────────────────────────────
/**
* Build markdown for the deep multi-perspective analysis.
* Outputs raw data metrics per stakeholder group for AI agent enrichment.
*
* @param fetchedData - Raw fetched EP data
* @param date - Analysis date
* @returns Markdown content string
*/
export function buildDeepAnalysisMarkdown(
fetchedData: Record<string, unknown>,
date: string
): string {
const header = buildMarkdownHeader('deep-analysis', date, 'high');
const events = safeArr(fetchedData, 'events');
const procedures = safeArr(fetchedData, 'procedures');
const documents = safeArr(fetchedData, 'documents');
const adoptedTexts = safeArr(fetchedData, 'adoptedTexts');
const questions = safeArr(fetchedData, 'questions');
const mepUpdates = safeArr(fetchedData, 'mepUpdates');
const total =
events.length +
procedures.length +
documents.length +
adoptedTexts.length +
questions.length +
mepUpdates.length;
return (
header +
`# Deep Multi-Perspective Analysis
## Pipeline Data Context
> **Note:** This section contains script-generated data inventory for reference. The AI agent must replace everything starting from the "AI Agent Instructions" heading below with substantive political intelligence analysis.
| Data Source | Count |
|-------------|-------|
| Events | ${events.length} |
| Procedures | ${procedures.length} |
| Documents | ${documents.length} |
| Adopted Texts | ${adoptedTexts.length} |
| Questions | ${questions.length} |
| MEP Updates | ${mepUpdates.length} |
| **Total** | **${total}** |
| Stakeholder Group | Data Points Available |
|-------------------|---------------------|
| Political Groups | ${procedures.length + adoptedTexts.length} (procedures + adopted texts) |
| Civil Society | ${documents.length + questions.length} (documents + questions) |
| Industry | ${procedures.length} (procedures) |
| National Governments | ${adoptedTexts.length} (adopted texts) |
| Citizens | ${questions.length + mepUpdates.length} (questions + MEP updates) |
| EU Institutions | ${events.length + procedures.length} (events + procedures) |
---
## AI Agent Instructions
> **Instructions for AI Agent (Opus 4.6):** Read ALL methodology documents in analysis/methodologies/ before writing. Using the data inventory above and the raw EP MCP data files, produce a deep multi-perspective analysis following the political-style-guide.md depth Level 3 format. Your analysis MUST:
>
> 1. **Identify the 3-5 most politically significant items** from the available data, citing specific document IDs
> 2. **Analyse each from ≥3 stakeholder perspectives** (Political Groups, Civil Society, Industry, National Governments, Citizens, EU Institutions)
> 3. **Apply the SWOT framework** to the overall parliamentary activity pattern for this date
> 4. **Assess coalition dynamics** — which groups are aligning/diverging based on the adopted texts?
> 5. **Rate confidence** for each analytical claim: HIGH / MEDIUM / LOW
> 6. **Provide forward-looking indicators** — what should be monitored in the next 7-14 days?
> 7. **Never leave scaffold markers** — replace this entire section with real analysis
>
> Evidence requirement: ≥3 citations per section from EP MCP data (document IDs, vote references, procedure numbers).
> Quality gate: minimum 500 words of original analytical prose with evidence citations.
> When done, REMOVE this instructions section entirely and write analysis prose directly.
[TO BE FILLED BY AI AGENT — This section must contain substantive political intelligence analysis, not data summaries. Quality gate: minimum 500 words of original analytical prose with evidence citations.]
## Date: ${date}
`
);
}
/**
* Build markdown for the stakeholder impact analysis.
*
* @param fetchedData - Raw fetched EP data
* @param date - Analysis date
* @returns Markdown content string
*/
export function buildStakeholderAnalysisMarkdown(
fetchedData: Record<string, unknown>,
date: string
): string {
const header = buildMarkdownHeader('stakeholder-analysis', date, 'high');
const procedures = safeArr(fetchedData, 'procedures');
const adoptedTexts = safeArr(fetchedData, 'adoptedTexts');
const documents = safeArr(fetchedData, 'documents');
const events = safeArr(fetchedData, 'events');
const questions = safeArr(fetchedData, 'questions');
const mepUpdates = safeArr(fetchedData, 'mepUpdates');
const votingRecords = safeArr(fetchedData, 'votingRecords');
const coalitions = safeArr(fetchedData, 'coalitions');
return (
header +
`# Stakeholder Impact Analysis
## Data Available for Stakeholder Assessment (Script-Generated Context)
| Stakeholder Group | Primary Data Sources | Data Points |
|-------------------|---------------------|-------------|
| Political Groups | Procedures, Adopted Texts, Voting Records, Coalitions | ${procedures.length + adoptedTexts.length + votingRecords.length + coalitions.length} |
| Civil Society | Documents, Questions, Events | ${documents.length + questions.length + events.length} |
| Industry | Procedures, Adopted Texts | ${procedures.length + adoptedTexts.length} |
| National Governments | Adopted Texts, Procedures, Coalitions | ${adoptedTexts.length + procedures.length + coalitions.length} |
| Citizens | Questions, MEP Updates, Events | ${questions.length + mepUpdates.length + events.length} |
| EU Institutions | Events, Procedures, Adopted Texts, Voting Records | ${events.length + procedures.length + adoptedTexts.length + votingRecords.length} |
## Data Source Summary
| Source | Count |
|--------|-------|
${Object.keys(fetchedData)
.filter((k) => Array.isArray(fetchedData[k]))
.map((k) => `| ${k} | ${(fetchedData[k] as unknown[]).length} |`)
.join('\n')}
## AI Agent Instructions
> **Instructions for AI Agent (Opus 4.6):** Read ALL methodology documents in analysis/methodologies/. Using the stakeholder-impact.md template and the data inventory above, produce a stakeholder impact analysis for each of the 6 stakeholder groups. For each group:
>
> 1. **Impact direction**: positive / negative / neutral / mixed
> 2. **Impact severity**: high / medium / low
> 3. **Specific evidence**: Cite ≥2 specific EP documents, votes, or procedures that affect this stakeholder
> 4. **Reasoning**: 2-3 sentences explaining WHY this stakeholder is affected and HOW
> 5. **Action items**: What should this stakeholder watch or do in response?
> 6. **Confidence level**: HIGH / MEDIUM / LOW
>
> Focus on the MOST RECENT adopted texts and procedures. Do not produce generic stakeholder descriptions — every assessment must be grounded in specific EP data from this date period.
> When done, REMOVE this instructions section entirely and write analysis prose directly.
[TO BE FILLED BY AI AGENT — Each stakeholder group must have impact direction, severity, evidence citations, and reasoning. Quality gate: minimum 300 words of original analytical prose.]
## Date: ${date}
`
);
}
/**
* Build markdown for coalition cohesion analysis.
*
* @param fetchedData - Raw fetched EP data
* @param date - Analysis date
* @returns Markdown content string
*/
export function buildCoalitionAnalysisMarkdown(
fetchedData: Record<string, unknown>,
date: string
): string {
const header = buildMarkdownHeader('coalition-analysis', date, 'high');
const rawPatterns = Array.isArray(fetchedData['patterns']) ? fetchedData['patterns'] : [];
const stabilityReport = computeCrossSessionCoalitionStability(
rawPatterns as Parameters<typeof computeCrossSessionCoalitionStability>[0]
);
return (
header +
`# Coalition Cohesion Analysis
## Computed Metrics (Script-Generated Context)
- **Overall Stability**: ${(stabilityReport.overallStability * 100).toFixed(1)}%
- **Forecast**: ${stabilityReport.forecast}
- **Patterns Analysed**: ${stabilityReport.patternCount}
- **Stable Groups**: ${stabilityReport.stableGroups.length > 0 ? stabilityReport.stableGroups.join(', ') : 'No stable groups identified from voting data'}
- **Declining Groups**: ${stabilityReport.decliningGroups.length > 0 ? stabilityReport.decliningGroups.join(', ') : 'No declining groups identified from voting data'}
- **Raw Patterns Evaluated**: ${rawPatterns.length}
## AI Agent Instructions
> **Instructions for AI Agent (Opus 4.6):** Read ALL methodology documents in analysis/methodologies/. Using the political-risk-methodology.md coalition risk framework and the computed metrics above, produce a coalition intelligence analysis. Your analysis MUST:
>
> 1. **Assess the Grand Coalition** (EPP + S&D + Renew): Is it holding? What are the stress points?
> 2. **Identify emerging alliances**: Are ECR, PfE, or Greens/EFA forming tactical voting blocs?
> 3. **Analyse abstention patterns**: High abstention rates signal internal group conflicts — identify which groups and why
> 4. **Cross-party voting**: Identify any cases where MEPs voted against their group line on recent adopted texts
> 5. **Predict coalition evolution**: Based on current patterns, which coalitions will strengthen/weaken in the next month?
> 6. **Confidence levels**: Rate each coalition assessment as HIGH / MEDIUM / LOW
>
> If voting data is limited (patterns analysed = 0), use adopted texts and political landscape data to infer coalition dynamics from the policy positions embedded in recent legislation.
> When done, REMOVE this instructions section entirely and write analysis prose directly.
[TO BE FILLED BY AI AGENT — Substantive coalition dynamics analysis with evidence citations, confidence levels, and forward-looking predictions. Quality gate: minimum 400 words.]
## Date: ${date}
`
);
}
/**
* Build markdown for voting pattern analysis.
*
* @param fetchedData - Raw fetched EP data
* @param date - Analysis date
* @returns Markdown content string
*/
export function buildVotingPatternsMarkdown(
fetchedData: Record<string, unknown>,
date: string
): string {
const header = buildMarkdownHeader('voting-patterns', date, 'high');
const rawRecords = Array.isArray(fetchedData['votingRecords'])
? fetchedData['votingRecords']
: [];
const trends = detectVotingTrends(rawRecords as Parameters<typeof detectVotingTrends>[0]);
const trendsText = trends
.map(
(t) =>
`| ${t.trendId} | ${t.direction} | ${(t.confidence * 100).toFixed(0)}% | ${t.recordCount} records |`
)
.join('\n');
return (
header +
`# Voting Pattern Analysis
## Detected Trends (Script-Generated Context)
| Trend ID | Direction | Confidence | Data Points |
|----------|-----------|------------|-------------|
${trendsText || '| No trend data available from voting records | — | — | — |'}
## Computed Summary
- **Trends identified**: ${trends.length}
- **Records analysed**: ${rawRecords.length}
## AI Agent Instructions
> **Instructions for AI Agent (Opus 4.6):** Read ALL methodology documents in analysis/methodologies/. Using the voting pattern data above and the adopted texts from EP MCP feeds, produce a voting pattern intelligence analysis. Your analysis MUST:
>
> 1. **Identify voting blocs**: Which groups consistently vote together on recent adopted texts?
> 2. **Detect anomalies**: Any unexpected votes, close margins (<50 vote difference), or high abstention rates?
> 3. **Analyse by policy domain**: Do voting patterns differ between economic, environmental, and social legislation?
> 4. **Group discipline assessment**: Rate each major group's internal cohesion (high/medium/low) with evidence
> 5. **Trend detection**: Compare recent voting patterns to historical trends — is the Parliament becoming more/less fragmented?
> 6. **Forward-looking**: Which upcoming votes are likely to be contested based on current alignment patterns?
>
> If voting records are limited, analyse the adopted texts' policy positions to infer likely voting alignments and coalition patterns.
> When done, REMOVE this instructions section entirely and write analysis prose directly.
[TO BE FILLED BY AI AGENT — Substantive voting pattern analysis with specific vote references, group cohesion ratings, and anomaly detection. Quality gate: minimum 300 words.]
## Date: ${date}
`
);
}
/**
* Build markdown for cross-session intelligence analysis.
*
* @param fetchedData - Raw fetched EP data
* @param date - Analysis date
* @returns Markdown content string
*/
export function buildCrossSessionIntelligenceMarkdown(
fetchedData: Record<string, unknown>,
date: string
): string {
const header = buildMarkdownHeader('cross-session-intelligence', date, 'high');
const rawPatterns = Array.isArray(fetchedData['patterns']) ? fetchedData['patterns'] : [];
const stabilityReport = computeCrossSessionCoalitionStability(
rawPatterns as Parameters<typeof computeCrossSessionCoalitionStability>[0]
);
return (
header +
`# Cross-Session Coalition Intelligence
## Computed Stability Metrics (Script-Generated Context)
- **Overall Stability**: ${(stabilityReport.overallStability * 100).toFixed(1)}%
- **Forecast**: ${stabilityReport.forecast}
- **Patterns Analysed**: ${stabilityReport.patternCount}
- **Stable Groups**: ${stabilityReport.stableGroups.length > 0 ? stabilityReport.stableGroups.join(', ') : 'None identified from voting data'}
- **Declining Groups**: ${stabilityReport.decliningGroups.length > 0 ? stabilityReport.decliningGroups.join(', ') : 'None identified from voting data'}
## AI Agent Instructions
> **Instructions for AI Agent (Opus 4.6):** Read ALL methodology documents in analysis/methodologies/. Using the cross-session stability metrics above and the adopted texts/voting records from recent plenary sessions, produce a cross-session intelligence synthesis. Your analysis MUST:
>
> 1. **Compare coalition patterns** across the last 3-5 plenary sessions — are alliances strengthening or fragmenting?
> 2. **Identify session-over-session trends**: Which policy areas show increasing/decreasing consensus?
> 3. **Detect coalition realignment signals**: Are new voting blocs forming? Is the Grand Coalition showing stress?
> 4. **Institutional dynamics**: How are EP-Council-Commission dynamics evolving based on recent legislative outcomes?
> 5. **Predictive assessment**: Based on cross-session patterns, forecast likely coalition behavior for upcoming votes
> 6. **Confidence levels**: Rate each finding as HIGH / MEDIUM / LOW
>
> Cross-reference with adopted texts from the most recent plenary session to ground the analysis in specific legislative outcomes.
> When done, REMOVE this instructions section entirely and write analysis prose directly.
[TO BE FILLED BY AI AGENT — Cross-session trend analysis with specific plenary session references, coalition evolution assessment, and predictive indicators. Quality gate: minimum 400 words.]
## Date: ${date}
`
);
}
/**
* Build markdown for the synthesis-summary method.
* Aggregates all per-file analyses into a synthesis summary.
*
* @param fetchedData - Raw fetched EP data (includes _dateOutputDir)
* @param date - Analysis date
* @returns Markdown content string
*/
export function buildSynthesisSummaryMarkdown(
fetchedData: Record<string, unknown>,
date: string
): string {
const dateOutputDir = String(fetchedData['_dateOutputDir'] ?? '');
Iif (!dateOutputDir) {
const header = buildMarkdownHeader(METHOD_SYNTHESIS_SUMMARY_ID, date, 'low');
return `${header}# 🧩 Synthesis Summary — ${date}\n\nNo output directory available for synthesis.\n`;
}
const summary = buildSynthesisSummary(dateOutputDir, date);
return formatSynthesisMarkdown(summary);
}
// ─── Per-document analysis ──────────────────────────────────────────────────
/** Entry for a single analyzed document within the document-analysis index */
interface DocumentEntry {
readonly category: string;
readonly id: string;
readonly title: string;
readonly filename: string;
}
/**
* Build comprehensive analysis markdown for a single document.
*
* @param item - Raw document item from feed data
* @param docId - Document identifier
* @param title - Document title
* @param category - Feed category the document came from
* @param date - Analysis date
* @param significance - Precomputed global political significance
* @param threats - Precomputed global threat assessment
* @returns Markdown content for single document analysis
*/
function buildSingleDocumentAnalysis(
item: Record<string, unknown>,
docId: string,
title: string,
category: string,
date: string,
significance: PoliticalSignificance,
threats: PoliticalThreatAssessment
): string {
const docType = typeof item['type'] === 'string' ? item['type'] : category;
const docDate = typeof item['date'] === 'string' ? item['date'] : date;
const docStatus = typeof item['status'] === 'string' ? item['status'] : 'unknown';
const docStage = typeof item['stage'] === 'string' ? item['stage'] : 'N/A';
const docDescription =
typeof item['description'] === 'string'
? item['description']
: typeof item['summary'] === 'string'
? (item['summary'] as string)
: 'No description available';
const docStrengths = [
createScoredSWOTItem(
`Document ${sanitizeDocumentId(docId)} available in ${category} feed`,
3,
[`Document ID: ${docId}`, `Category: ${category}`, `Status: ${docStatus}`],
'medium',
'stable'
),
];
const docWeaknesses = [
createScoredSWOTItem(
`Document stage: ${docStage}, status: ${docStatus}`,
2,
[`Current stage: ${docStage}`, `Type: ${docType}`, `Date: ${docDate}`],
'medium',
'stable'
),
];
const docOpportunities = [
createScoredOpportunityOrThreat(
`${category} document with ID ${sanitizeDocumentId(docId)}`,
'possible',
'moderate',
[`Category: ${category}`, `Date: ${docDate}`],
'medium',
'stable'
),
];
const docThreats = [
createScoredOpportunityOrThreat(
`Document ${sanitizeDocumentId(docId)} — pipeline risk assessment`,
'possible',
'moderate',
[`Stage: ${docStage}`, `Status: ${docStatus}`],
'medium',
'stable'
),
];
const docSwot = buildQuantitativeSWOT(
`SWOT: ${title}`,
docStrengths,
docWeaknesses,
docOpportunities,
docThreats
);
return `---
method: ${METHOD_DOCUMENT_ANALYSIS}
documentId: ${JSON.stringify(docId)}
category: ${JSON.stringify(category)}
date: ${JSON.stringify(date)}
confidence: medium
generated: ${JSON.stringify(new Date().toISOString())}
---
# Document Analysis: ${sanitizeCell(title)}
## Document Metadata
| Field | Value |
|-------|-------|
| **Document ID** | ${sanitizeCell(docId)} |
| **Title** | ${sanitizeCell(title)} |
| **Type** | ${sanitizeCell(docType)} |
| **Category** | ${sanitizeCell(category)} |
| **Date** | ${sanitizeCell(docDate)} |
| **Status** | ${sanitizeCell(docStatus)} |
| **Stage** | ${sanitizeCell(docStage)} |
## Description
${sanitizeCell(docDescription)}
## Political Significance Assessment
- **Overall Significance**: ${significance.toUpperCase()}
- **Context**: Document ${sanitizeCell(docId)} within ${category} feed
## Document-Specific SWOT Analysis
### Strategic Position Score: ${docSwot.strategicPositionScore.toFixed(1)}/10
| Category | Score | Assessment |
|----------|-------|------------|
| Strengths | ${docSwot.strengths.reduce((s, i) => s + i.score, 0).toFixed(1)} | ${docSwot.strengths.map((s) => s.description).join('; ')} |
| Weaknesses | ${docSwot.weaknesses.reduce((s, i) => s + i.score, 0).toFixed(1)} | ${docSwot.weaknesses.map((w) => w.description).join('; ')} |
| Opportunities | ${docSwot.opportunities.reduce((s, i) => s + i.score, 0).toFixed(1)} | ${docSwot.opportunities.map((o) => o.description).join('; ')} |
| Threats | ${docSwot.threats.reduce((s, i) => s + i.score, 0).toFixed(1)} | ${docSwot.threats.map((t) => t.description).join('; ')} |
## Threat Assessment
- **Threat Dimensions Evaluated**: ${threats.threatDimensions.length}
- **Overall Threat Level**: ${threats.overallThreatLevel}
- **Assessment Date**: ${threats.date}
## Stakeholder Impact
| Stakeholder Group | Impact Level |
|-------------------|-------------|
| Political Groups | ${significance === 'routine' ? 'Low' : 'Medium'} |
| Civil Society | ${significance === 'routine' ? 'Low' : 'Medium'} |
| Industry | ${String(docType).toLowerCase() === 'resolution' || String(docType).toLowerCase() === 'directive' ? 'Medium' : 'Low'} |
| National Governments | ${String(docStage).toLowerCase() === 'trilogue' ? 'High' : 'Low'} |
| Citizens | Low |
| EU Institutions | ${significance === 'critical' || significance === 'historic' ? 'High' : 'Low'} |
## Intelligence Summary
| Metric | Value |
|--------|-------|
| Document | ${sanitizeCell(docId)} |
| Category | ${sanitizeCell(category)} |
| Type | ${sanitizeCell(docType)} |
| Stage | ${sanitizeCell(docStage)} |
| Status | ${sanitizeCell(docStatus)} |
| Significance | ${significance} |
| SWOT Score | ${docSwot.strategicPositionScore.toFixed(1)}/10 |
| Overall Assessment | ${docSwot.overallAssessment} |
| Threat Dimensions | ${threats.threatDimensions.length} |
| Overall Threat Level | ${threats.overallThreatLevel} |
## Analysis Date: ${date}
`;
}
/**
* Process a single feed item: deduplicate, write per-document files, and
* collect the index entry.
*
* @param raw - Raw feed item
* @param feedKey - Feed category key
* @param date - Analysis date string
* @param analyzedIds - Set of already-processed document IDs for deduplication
* @param docDir - Output directory for per-document markdown
* @param rawDataDir - Output directory for raw JSON data
* @param significance - Precomputed global political significance
* @param threats - Precomputed global threat assessment
* @returns Document entry for the index, or undefined if skipped
*/
function processDocumentItem(
raw: unknown,
feedKey: string,
date: string,
analyzedIds: Set<string>,
docDir: string,
rawDataDir: string,
significance: PoliticalSignificance,
threats: PoliticalThreatAssessment
): DocumentEntry | undefined {
Iif (!raw || typeof raw !== 'object') return undefined;
const item = raw as Record<string, unknown>;
const docId = extractDocumentId(item);
const dedupeKey = docId.toLowerCase().trim();
if (analyzedIds.has(dedupeKey)) return undefined;
analyzedIds.add(dedupeKey);
const title = extractDocumentTitle(item);
const safeId = sanitizeDocumentId(docId);
const filename = `${sanitizeDocumentId(feedKey)}-${safeId}-analysis.md`;
Eif (docDir) {
const docContent = buildSingleDocumentAnalysis(
item,
docId,
title,
feedKey,
date,
significance,
threats
);
writeTextFile(path.join(docDir, filename), docContent);
const rawJsonFilename = `${sanitizeDocumentId(feedKey)}-${safeId}-raw.json`;
writeTextFile(path.join(rawDataDir, rawJsonFilename), JSON.stringify(item, null, 2));
}
return { category: feedKey, id: docId, title, filename };
}
/**
* Build per-document intelligence analysis index and files.
*
* @param fetchedData - Raw fetched EP data
* @param date - Analysis date
* @returns Markdown index content string
*/
export function buildDocumentAnalysisMarkdown(
fetchedData: Record<string, unknown>,
date: string
): string {
const header = buildMarkdownHeader(METHOD_DOCUMENT_ANALYSIS, date, 'medium');
const dateOutputDir = (fetchedData as Record<string, unknown>)['_dateOutputDir'];
const outputBase = typeof dateOutputDir === 'string' ? dateOutputDir : '';
const docDir = outputBase ? path.join(outputBase, 'documents') : '';
const rawDataDir = outputBase ? path.join(outputBase, 'documents', 'raw-data') : '';
Eif (docDir) ensureDirectoryExists(docDir);
Eif (rawDataDir) ensureDirectoryExists(rawDataDir);
const globalInput = toClassificationInput(fetchedData);
const globalSignificance = assessPoliticalSignificance(globalInput);
const globalThreatInput = toThreatInput(fetchedData);
const globalThreats = assessPoliticalThreats(globalThreatInput);
const analyzedIds = new Set<string>();
const documentEntries: DocumentEntry[] = [];
for (const feedKey of DOCUMENT_FEED_KEYS) {
const items = safeArr(fetchedData, feedKey);
for (const raw of items) {
const entry = processDocumentItem(
raw,
feedKey,
date,
analyzedIds,
docDir,
rawDataDir,
globalSignificance,
globalThreats
);
if (entry) documentEntries.push(entry);
}
}
Object.defineProperty(fetchedData, '_analyzedDocumentIds', {
value: [...analyzedIds],
writable: false,
configurable: true,
enumerable: false,
});
const tableRows =
documentEntries.length > 0
? documentEntries
.map(
(d) =>
`| ${sanitizeCell(d.id)} | ${sanitizeCell(d.title.slice(0, 60))} | ${sanitizeCell(d.category)} | [${d.filename}](${d.filename}) |`
)
.join('\n')
: '| — | No documents available | — | — |';
return (
header +
`# Per-Document Intelligence Analysis Index
## Executive Summary
Full per-document political intelligence analysis for ${documentEntries.length} unique documents
across ${DOCUMENT_FEED_KEYS.length} feed categories. Each document has been individually
analyzed from fetched European Parliament data with comprehensive significance assessment,
SWOT analysis, and threat profiling.
- **Total Documents Analyzed**: ${documentEntries.length}
- **Feed Categories Scanned**: ${DOCUMENT_FEED_KEYS.length}
- **Duplicates Deduplicated**: ${[...DOCUMENT_FEED_KEYS].reduce((s, k) => s + safeArr(fetchedData, k).length, 0) - documentEntries.length}
- **Date**: ${date}
## Document Analysis Index
| Document ID | Title | Category | Analysis File |
|-------------|-------|----------|---------------|
${tableRows}
## Category Breakdown
${DOCUMENT_FEED_KEYS.map(
(k) =>
`- **${k}**: ${safeArr(fetchedData, k).length} items (${documentEntries.filter((d) => d.category === k).length} unique analyzed)`
).join('\n')}
## Methodology
Each document receives:
1. **Raw Data Storage** — Full document JSON stored in \`documents/raw-data/\` for complete data preservation
2. **Significance Classification** — Political importance on 5-level scale
3. **SWOT Assessment** — Strengths, weaknesses, opportunities, threats specific to the document
4. **Threat Profiling** — Political threat landscape analysis for disruption potential
5. **Stakeholder Impact** — Projected effects on key stakeholder groups
6. **Intelligence Summary** — Key findings and actionable insights
## Document Storage
All ${documentEntries.length} documents have been stored in their entirety:
- **Analysis files**: \`documents/{category}-{id}-analysis.md\`
- **Raw JSON data**: \`documents/raw-data/{category}-{id}-raw.json\`
- **Deduplication**: Documents appearing in multiple feed categories are stored once with primary category reference
## Date: ${date}
`
);
}
/** All existing analysis method builders keyed by their AnalysisMethod identifier */
export const EXISTING_BUILDERS: Readonly<Partial<Record<AnalysisMethod, MarkdownBuilder>>> = {
'deep-analysis': buildDeepAnalysisMarkdown,
'stakeholder-analysis': buildStakeholderAnalysisMarkdown,
'coalition-analysis': buildCoalitionAnalysisMarkdown,
'voting-patterns': buildVotingPatternsMarkdown,
'cross-session-intelligence': buildCrossSessionIntelligenceMarkdown,
[METHOD_SYNTHESIS_SUMMARY_ID]: buildSynthesisSummaryMarkdown,
[METHOD_DOCUMENT_ANALYSIS]: buildDocumentAnalysisMarkdown,
};
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