Extract structured XBRL data from SEC 10-K, 10-Q, and 8-K filings.
npx skills add https://github.com/dgunning/edgartools --skill edgartools
Please help me install this Agent Skill. Skill: EdgarTools Source: https://github.com/dgunning/edgartools/tree/main/edgar/ai/skills/core Command: npx skills add https://github.com/dgunning/edgartools --skill edgartools
Analysts spend hours sifting through SEC filings and extracting key numbers. EdgarTools provides a unified, AI-friendly way to query filings, pull structured data, and perform multi-company analysis, reducing manual research time and complexity.
.to_context() outputs to feed AI agents with compact metadata for fast decisions.Get started by loading EdgarTools, set identity, and fetch Apple's latest 10-K summary:
set_identity("Your Name [email protected]")to_context() for a concise profilefiling.xbrl() and inspect xbrl.statements for quick numbersHigh-intent search queries and answers about installing and using this skill.
EdgarTools extracts structured financial data from SEC filings by querying XBRL statements and company facts. Load a Company object, call `filing.xbrl()` to access XBRL data, then inspect `xbrl.statements` to retrieve key numbers from 10-K, 10-Q, and 8-K forms.
Yes, EdgarTools supports multi-company analysis through built-in helper functions and filtering methods. Create multiple Company objects, fetch filings for each, and use `.to_context()` outputs to feed AI agents with compact metadata for fast cross-company comparisons.
XBRL is a standardized format for tagging financial data in SEC filings, enabling machine-readable extraction of structured numbers. EdgarTools parses XBRL to unlock automated access to balance sheets, income statements, and cash flows without manual copying.
EdgarTools automates due diligence by providing token-efficient access to 10-K, 10-Q, and 8-K filings with built-in search and filtering. Set your SEC identity, load target companies, extract XBRL statements, and use helper functions to screen and trend financial metrics across filings.
EdgarTools requires basic Python knowledge to call methods like `Company()`, `filing.xbrl()`, and `.to_context()`, but its concise, AI-friendly API and centralized documentation are designed to minimize setup complexity for financial automation tasks.
EdgarTools supports annual reports (10-K), quarterly filings (10-Q), and current reports (8-K). It accesses structured XBRL data across these forms to enable trend analysis, financial modeling, and cross-company screening workflows.
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