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 Geeksfino/finskills --skill findata-toolkitgit clone --depth 1 https://github.com/Geeksfino/finskillsWrote 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/geeksfino/finskills/findata-toolkit)<a href="https://agentmods.dev/skills/geeksfino/finskills/findata-toolkit"><img src="https://agentmods.dev/badge/skills/geeksfino/finskills/findata-toolkit/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/geeksfino/finskills/findata-toolkit"><img src="https://agentmods.dev/badge/skills/geeksfino/finskills/findata-toolkit.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 | $0.00101 | $0.01247 |
| Opus 5 | $0.00051 | $0.00624 |
| Sonnet 5 | $0.00020 | $0.00249 |
| Haiku 4.5 | $0.00010 | $0.00125 |
Grade A, and why
findata-toolkit-us 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 11d 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 — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
FinData Toolkit — US Market
A self-contained data toolkit providing live financial data and quantitative calculations for US market analysis. All data sources are free and require no API keys.
Setup
Install dependencies (one-time):
pip install -r requirements.txt
Available Tools
All scripts are in the scripts/ directory. Run from the skill root directory.
1. Stock Data (scripts/stock_data.py)
Fetch stock fundamentals, price history, and financial metrics via yfinance.
| Command | Purpose |
|---|---|
python scripts/stock_data.py AAPL |
Basic company info |
python scripts/stock_data.py AAPL --metrics |
Full financial metrics (valuation, profitability, leverage, growth, analyst consensus) |
python scripts/stock_data.py AAPL --history --period 1y |
OHLCV price history |
python scripts/stock_data.py AAPL --financials |
Income statement, balance sheet, cash flow |
python scripts/stock_data.py AAPL MSFT GOOGL --screen |
Screen stocks against value filters |
2. SEC EDGAR (scripts/sec_edgar.py)
Fetch insider trading data (Form 4), company filings, and CIK lookups.
| Command | Purpose |
|---|---|
python scripts/sec_edgar.py insider AAPL |
Recent insider trades |
python scripts/sec_edgar.py insider AAPL --days 90 |
Insider trades in last 90 days |
python scripts/sec_edgar.py filings AAPL --form-type 10-K |
Recent 10-K filings |
python scripts/sec_edgar.py cik AAPL |
Look up CIK number |
3. Financial Calculators (scripts/financial_calc.py)
DuPont decomposition, Altman Z-Score, Beneish M-Score, Piotroski F-Score, earnings quality, and working capital analysis.
| Command | Purpose |
|---|---|
python scripts/financial_calc.py AAPL --all |
All calculations |
python scripts/financial_calc.py AAPL --dupont |
5-factor DuPont decomposition |
python scripts/financial_calc.py AAPL --zscore |
Altman Z-Score (bankruptcy risk) |
python scripts/financial_calc.py AAPL --mscore |
Beneish M-Score (manipulation detection) |
python scripts/financial_calc.py AAPL --fscore |
Piotroski F-Score (financial strength) |
python scripts/financial_calc.py AAPL --quality |
Earnings quality assessment |
python scripts/financial_calc.py AAPL --working-capital |
Working capital & CCC analysis |
What ships with it
12 files 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.
- config/data_sources.yaml 760 B
- LICENSE.txt 565 B
- requirements.txt 383 B
- scripts/common/__init__.py 39 B runs code
- scripts/common/config.py 2.0 KB runs code
- scripts/common/utils.py 4.0 KB runs code
- scripts/factor_screener.py 12 KB runs code
- scripts/financial_calc.py 27 KB runs code
- scripts/macro_data.py 14 KB runs code
- scripts/portfolio_analytics.py 20 KB runs code
- scripts/sec_edgar.py 13 KB runs code
- scripts/stock_data.py 13 KB runs code
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.
- 11d ago First seen · 110 lines · 101 tokens per session scan A abc134f64256
findata-toolkit-us is a skill published in the GitHub repository Geeksfino/finskills (279 stars, last pushed 6mo ago), licensed Apache-2.0. It adds 101 tokens to every session and 1,247 once invoked, about $0.0005 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 2026-08-30.
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