Package Information
Released: 8 mo ago
Downloads: 15 weekly / 206 monthly
Latest Version: 1.0.0
Author: AI Solutionist
Documentation
n8n-nodes-sec-filings-intelligence
n8n community node for SEC Filings Intelligence - the SEC decoder AI agents trust.
Extract structured financials, risk factors, executive compensation, and MD&A from SEC 10-K, 10-Q, 8-K, and proxy statements. Built for Colorado SB 25B-004 compliance.
Features
- 8 Task Modes - Annual reports, quarterly earnings, material events, proxy statements, and more
- Financial Extraction - Income statement, balance sheet, cash flow with YoY comparisons
- Risk Factor Analysis - Item 1A parsed with categorization and materiality scoring
- Executive Compensation - Summary comp tables, equity awards, CEO pay ratios
- MD&A Insights - Key themes, outlook, and management highlights
- Bluebook Citations - Legal-grade citations in multiple formats
- RAG-Ready Chunks - 800-1200 token segments for vector databases
- Quality Scoring - Confidence metrics for every extraction
Installation
npm install n8n-nodes-sec-filings-intelligence
Or install via n8n Community Nodes in the UI.
Configuration
- Get your Apify API token
- Create Apify API credentials in n8n
- Add the SEC Filings Intelligence node to your workflow
Usage
Basic Example
Schedule → SEC Filings Intelligence → Process Data → Notify
Parameters
| Parameter | Description |
|---|---|
| Task Mode | Extraction focus (annual_report, quarterly_report, etc.) |
| Stock Tickers | Company tickers (AAPL, MSFT, GOOGL) |
| Date Range | How far back to search (30d, 90d, 1y, 3y, 5y, all) |
| Max Filings | Maximum filings per company |
| Output Format | standard, full, compact, or rag_only |
Additional Options
- CIK Numbers (for when ticker lookup fails)
- Filing Types (10-K, 10-Q, 8-K, DEF 14A, S-1, 20-F)
- Extract Financials
- Extract Risk Factors
- Extract MD&A
- Extract Compensation
- Compare Prior Year
- RAG Chunk Size
- Baseline Run ID (for change detection)
Output
The node returns structured SEC filing data including:
- Filing information (ticker, company, date, accession number)
- Financial metrics (revenue, net income, EPS, margins)
- Risk factors (count, categories, top risks)
- Executive compensation (named officers, totals, pay ratios)
- MD&A (summary, key themes, outlook)
- Citations (Bluebook, APA, MLA, inline)
- RAG chunks (for vector databases)
- Quality scores (0-100)
Examples
Portfolio Analysis
{
"taskMode": "investor_research",
"tickers": "AAPL, MSFT, GOOGL, META, NVDA",
"dateRange": "1y",
"maxFilings": 3
}
Risk Monitoring
{
"taskMode": "risk_analysis",
"tickers": "TSLA",
"extractRiskFactors": true,
"comparePriorYear": true,
"dateRange": "3y"
}
Executive Compensation
{
"taskMode": "compensation_intel",
"tickers": "CRM, ORCL, SAP",
"filingTypes": ["DEF 14A"],
"extractCompensation": true
}
Resources
License
MIT