{"categories":[{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis"},{"label":"Financial","url":"https://skillfed.io/packages/category/office-business-financial"},{"label":"Investment","url":"https://skillfed.io/packages/category/office-business-financial-investment"}],"enrichment":{"capability":"EdgarTools parses SEC EDGAR filings into typed Python objects and pandas DataFrames, extracting financial statements, insider trades, fund holdings, and other filing types with a consistent API.","skillfed_tags":["sec-filings","financial-data","xbrl-parsing"],"use_cases":["Extract standardized income statements, balance sheets, and cash flow from 10-K and 10-Q filings for cross-company financial analysis.","Query insider trading transactions from Form 4 filings as a DataFrame to track executive buy/sell activity.","Retrieve 13F institutional holdings to analyze hedge fund and mutual fund portfolios over time.","Parse 8-K current reports to identify corporate events programmatically.","Build time-series datasets of financial metrics across companies using the Company Facts API.","Feed SEC filing text and structured data into RAG pipelines or AI systems via the built-in MCP server."],"what_it_does":"EdgarTools is a Python library that fetches SEC EDGAR filings and converts them into typed Python objects and pandas DataFrames, eliminating the need to parse raw XBRL or HTML yourself. It wraps the free SEC EDGAR API and handles the messy work of standardizing financial statements, insider trades, fund holdings, proxy statements, and other forms into a consistent, queryable format. You identify yourself with an email, then call methods to get clean, structured data in a few lines.\n\nThe library is built for production use: it includes configurable rate limiting, smart caching, type hints throughout, and an MCP server for AI integration. It has 21 runtime dependencies (httpx, pandas, pydantic, lxml, beautifulsoup4, and others) that handle HTTP requests, data manipulation, parsing, and text processing. The dependency footprint is moderate but standard for a data-extraction tool of this scope. It supports Python 3.10 through 3.14 and is actively maintained.","worth_installing":"Yes. EdgarTools is actively maintained, has low install friction, carries no security vulnerabilities, and solves a real problem\u2014turning free but messy SEC data into usable Python objects. The MIT license is permissive. The 21 dependencies are all standard and well-maintained. Use it if you need to work with SEC filings programmatically. The main gotcha is that you must provide an email to the SEC with every request, but that is a documented requirement."},"id":"edgartools","links":{"html":"https://skillfed.io/packages/edgartools","md":"https://skillfed.io/packages/edgartools.md","pypi":"https://pypi.org/project/edgartools/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-12","license_spdx":"MIT","license_treatment":"permissive","name":"edgartools","python_support":"supports_current","summary":"Python library to access and analyze SEC Edgar filings, XBRL financial statements, 10-K, 10-Q, and 8-K reports"},"popularity":{"monthly_downloads":941567,"position":4677,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"5.48.0"}