Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add thevibeworks/claude-code-docs --skill edgartoolsgit clone --depth 1 https://github.com/thevibeworks/claude-code-docsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/thevibeworks/claude-code-docs/edgartools)<a href="https://agentmods.dev/skills/thevibeworks/claude-code-docs/edgartools"><img src="https://agentmods.dev/badge/skills/thevibeworks/claude-code-docs/edgartools.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00064 | $0.01046 |
| Opus 5 | $0.00032 | $0.00523 |
| Sonnet 5 | $0.00013 | $0.00209 |
| Haiku 4.5 | $0.00006 | $0.00105 |
Grade A, and why
edgartools-sec-data scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to edgartools-sec-data — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
edgartools: 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.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 96 lines · 64 tokens per session scan A 4da2fb7e5c45
edgartools-sec-data is a skill published in the GitHub repository thevibeworks/claude-code-docs (40 stars, last pushed today), licensed MIT. It adds 64 tokens to every session and 1,046 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to edgartools-sec-data, differing in 0 lines, and is treated as a copy.
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