Edgartools Sec Data Skill for Claude
How to pull SEC EDGAR data with the edgartools Python package — company lookup, filings, XBRL financial statements, and filing sections like Item 1A risk factors. Use whenever a task involves SEC filings, 10-K/10-Q data, or company financials.
Install
one command, takes just this skill from the repositorynpx skills add https://github.com/anthropics/cwc-workshops --skill edgartools-sec-dataThe instruction itself
8 sections, as written by the authoredgartools: SEC EDGAR data access
edgartools is the desk's standard way to read SEC data. It is preinstalled in your environment. Work in Python (a script or python -c), not by fetching sec.gov pages by hand.
Always set your identity first
The SEC requires a contact identity on automated requests. Do this before any other call, every session:
from edgar import set_identity
set_identity("Research Desk workshop [email protected]") # use the EDGAR_IDENTITY value you were given
(Equivalently, the EDGAR_IDENTITY environment variable, exported before running Python.)
Companies and filings
from edgar import Company
company = Company("NVDA") # by ticker (or CIK)
company.name, company.cik, company.industry
filings = company.get_filings(form="10-K") # also "10-Q", "8-K", "DEF 14A", ...
latest_10k = filings.latest() # most recent of that form
latest_10q = company.get_filings(form="10-Q").latest()
latest_10k.form, latest_10k.filing_date, latest_10k.accession_no
Pick whichever of the latest 10-K / 10-Q is more recent when asked for "the most recent filing". Foreign private issuers file 20-F instead of 10-K.
Reading the filing
filing = latest_10k
tenk = filing.obj() # rich object for 10-K/10-Q: sections, financials
# Sections (10-K item numbers; 10-Q uses Part/Item naming)
risk_factors = tenk["Item 1A"] # Risk Factors text
mda = tenk["Item 7"] # Management's Discussion & Analysis
business = tenk["Item 1"]
# Plain text of the whole filing if you need to search it
text = filing.text()
Sections are long — extract what you need rather than pasting whole sections into your reply.
Financial statements (XBRL)
financials = tenk.financials # also: company.get_financials() for the latest annual figures
income = financials.income_statement()
balance = financials.balance_sheet()
cashflow = financials.cashflow_statement()
These return tabular objects (pandas-friendly). Typical fields: total revenue, gross profit, operating income, net income, cash and equivalents, total debt, inventory, R&D expense. The same statement usually carries the prior period's column — use it for year-over-year comparisons instead of fetching another filing.
Segment and geographic revenue
Consolidated revenue hides the story; the segment note is where concentration and regional shifts show up. Segment data lives in the financial-statement notes (ASC 280), not the primary statements:
# The segment/geography breakdown is a note, not a primary statement.
# Search the filing text for the segment note and read the tables around it.
text = filing.text()
for marker in ["Segment Information", "revenue by geographic", "Disaggregation of Revenue"]:
idx = text.find(marker)
if idx != -1:
print(text[idx : idx + 3000]) # the note's tables follow the heading
break
Report segment/geography revenue alongside the consolidated figure and call out: any region or segment that moved more than ~20% year over year, and any customer-concentration disclosure (usually phrased "one customer accounted for X% of revenue").
Useful patterns
- Year-over-year risk-factor diff: pull Item 1A from this year's and last year's 10-K (
company.get_filings(form="10-K")is sorted; take the first two) and compare. - Insider activity:
company.get_filings(form="4")lists Form 4 insider transaction filings. - Be defensive: the API surface evolves. If an attribute or method isn't there, inspect it (
dir(obj),help(obj)) or fall back tofiling.text()and targeted searching, and note the fallback in your output. - Be polite to EDGAR: keep requests to what you need; everything is cached within the session by the library.
Units and reporting hygiene
- Statement values are typically raw USD; convert to millions when reporting (
value / 1e6) and label them. - Always state which filing (form, fiscal period, filing date) a number came from.
- If a figure is missing or ambiguous in XBRL, say so rather than estimating.
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Python library for accessing, analyzing, and extracting data from SEC EDGAR filings. Use when working with SEC filings, financial statements (income statement, balance sheet, cash flow), XBRL financial data, insider trading (Form 4), institutional holdings (13F), company financials, annual/quarterly reports (10-K, 10-Q), proxy statements (DEF 14A), 8-K current events, company screening by ticker/CIK/industry, multi-period financial analysis, or any SEC regulatory filings.
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How to use it
Take anthropics/edgartools-sec-data from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.