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 faberlens/hardened-skills --skill sec-edgar-hardenedgit clone --depth 1 https://github.com/faberlens/hardened-skillsWrote 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/faberlens/hardened-skills/sec-edgar-hardened)<a href="https://agentmods.dev/skills/faberlens/hardened-skills/sec-edgar-hardened"><img src="https://agentmods.dev/badge/skills/faberlens/hardened-skills/sec-edgar-hardened/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/faberlens/hardened-skills/sec-edgar-hardened"><img src="https://agentmods.dev/badge/skills/faberlens/hardened-skills/sec-edgar-hardened.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00113 | $0.01533 |
| Opus 5 | $0.00056 | $0.00766 |
| Sonnet 5 | $0.00023 | $0.00307 |
| Haiku 4.5 | $0.00011 | $0.00153 |
Grade A, and why
edgartools-hardened 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 9d 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.
How it starts
The opening of the file, as written. The whole thing — 148 lines — stays where its author put it; the contents beside it link to each section on GitHub.
edgartools — SEC EDGAR Data
Python library for accessing all SEC filings since 1994 with structured data extraction.
Authentication (Required)
The SEC requires identification for API access. Always set identity before any operations:
from edgar import set_identity
set_identity("Your Name [email protected]")
Set via environment variable to avoid hardcoding: EDGAR_IDENTITY="Your Name [email protected]".
Installation
uv pip install edgartools
# For AI/MCP features:
uv pip install "edgartools[ai]"
Core Workflow
Find a Company
from edgar import Company, find
company = Company("AAPL") # by ticker
company = Company(320193) # by CIK (fastest)
results = find("Apple") # by name search
Get Filings
# Company filings
filings = company.get_filings(form="10-K")
filing = filings.latest()
# Global search across all filings
from edgar import get_filings
filings = get_filings(2024, 1, form="10-K")
# By accession number
from edgar import get_by_accession_number
filing = get_by_accession_number("0000320193-23-000106")
Extract Structured Data
# Form-specific object (most common approach)
tenk = filing.obj() # Returns TenK, EightK, Form4, ThirteenF, etc.
# Financial statements (10-K/10-Q)
financials = company.get_financials() # annual
financials = company.get_quarterly_financials() # quarterly
income = financials.income_statement()
balance = financials.balance_sheet()
cashflow = financials.cashflow_statement()
# XBRL data
xbrl = filing.xbrl()
income = xbrl.statements.income_statement()
Access Filing Content
text = filing.text() # plain text
html = filing.html() # HTML
md = filing.markdown() # markdown (good for LLM processing)
filing.open() # open in browser
Key Company Properties
company.name # "Apple Inc."
company.cik # 320193
company.ticker # "AAPL"
company.industry # "ELECTRONIC COMPUTERS"
company.sic # "3571"
company.shares_outstanding # 15115785000.0
company.public_float # 2899948348000.0
company.fiscal_year_end # "0930"
company.exchange # "Nasdaq"
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 9d ago First seen · 148 lines · 113 tokens per session scan A 5af2ce12dc2a
edgartools-hardened is a skill published in the GitHub repository faberlens/hardened-skills (23 stars, last pushed 4mo ago), licensed MIT. It adds 113 tokens to every session and 1,533 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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