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Findata Toolkit Us

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Geeksfino
findata-toolkit-us

Financial data toolkit for US market analysis. Provides scripts to fetch real-time stock data (yfinance), SEC filings and insider trades (EDGAR), financial statement calculators (DuPont, Z-Score, M-Score, F-Score), portfolio analytics (VaR, stress testing, health scoring), multi-factor screening, and macro indicators (FRED). Use when you need live US market data to ground investment analysis. All data sources are free — no API keys required.

Overview

PublisherGeeksfino
Repositoryfinskills
Skill namefindata-toolkit-us
Stars
285
Forks
45
Bundled files
11
LicenseApache-2.0
Links
Skill sourceRepository
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 11 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Geeksfino on GitHub. Read the source before you install it.

Installation

Install the Findata Toolkit Us AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Geeksfino/finskills/tree/main/US-market/findata-toolkit
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Geeksfino/finskills.git /tmp/finskills
mkdir -p .claude/skills
cp -r /tmp/finskills/US-market/findata-toolkit .claude/skills/findata-toolkit-us
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Findata Toolkit Us in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Findata Toolkit Us on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Findata Toolkit Us is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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):

bash
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.

CommandPurpose
python scripts/stock_data.py AAPLBasic company info
python scripts/stock_data.py AAPL --metricsFull financial metrics (valuation, profitability, leverage, growth, analyst consensus)
python scripts/stock_data.py AAPL --history --period 1yOHLCV price history
python scripts/stock_data.py AAPL --financialsIncome statement, balance sheet, cash flow
python scripts/stock_data.py AAPL MSFT GOOGL --screenScreen stocks against value filters

2. SEC EDGAR (scripts/sec_edgar.py)

Fetch insider trading data (Form 4), company filings, and CIK lookups.

CommandPurpose
python scripts/sec_edgar.py insider AAPLRecent insider trades
python scripts/sec_edgar.py insider AAPL --days 90Insider trades in last 90 days
python scripts/sec_edgar.py filings AAPL --form-type 10-KRecent 10-K filings
python scripts/sec_edgar.py cik AAPLLook 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.

CommandPurpose
python scripts/financial_calc.py AAPL --allAll calculations
python scripts/financial_calc.py AAPL --dupont5-factor DuPont decomposition
python scripts/financial_calc.py AAPL --zscoreAltman Z-Score (bankruptcy risk)
python scripts/financial_calc.py AAPL --mscoreBeneish M-Score (manipulation detection)
python scripts/financial_calc.py AAPL --fscorePiotroski F-Score (financial strength)
python scripts/financial_calc.py AAPL --qualityEarnings quality assessment
python scripts/financial_calc.py AAPL --working-capitalWorking capital & CCC analysis

4. Portfolio Analytics (scripts/portfolio_analytics.py)

Portfolio risk analysis: concentration, correlation clusters, VaR/CVaR, stress testing, and health scoring.

CommandPurpose
python scripts/portfolio_analytics.py --holdings "AAPL:30,MSFT:25,GOOGL:20,AMZN:15,META:10"Full health score (0–100)
... --concentrationConcentration analysis (HHI, sector)
... --correlationCorrelation clusters & EDR
... --riskVaR/CVaR, Sharpe, Sortino, beta
... --stressHistorical stress testing (5 scenarios)

5. Factor Screener (scripts/factor_screener.py)

Multi-factor stock scoring: value, momentum, quality, low volatility, size, growth.

CommandPurpose
python scripts/factor_screener.py --universe "AAPL,MSFT,GOOGL,AMZN" --top 5Screen custom universe
python scripts/factor_screener.py --sp500-sample --top 10Screen S&P 500 sample
... --factors value,qualityUse specific factors only

6. Macro Data (scripts/macro_data.py)

US macroeconomic indicators from FRED.

CommandPurpose
python scripts/macro_data.py --dashboardFull macro dashboard
python scripts/macro_data.py --ratesInterest rates & yield curve
python scripts/macro_data.py --inflationCPI, PCE, breakevens
python scripts/macro_data.py --gdpGDP & leading indicators
python scripts/macro_data.py --employmentUnemployment, payrolls, JOLTS
python scripts/macro_data.py --cycleBusiness cycle phase assessment

Data Sources

SourceDataAPI Key
Yahoo Finance (yfinance)Stock quotes, financials, historyNot required
SEC EDGARFilings, insider trades (Form 4)Not required
FREDMacro indicatorsNot required

Output Format

All scripts output JSON to stdout for easy parsing. Errors go to stderr.

Configuration

Optional: Edit config/data_sources.yaml to customize rate limits or add API keys for premium data sources.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Findata Toolkit Us AI skill do?

Financial data toolkit for US market analysis. Provides scripts to fetch real-time stock data (yfinance), SEC filings and insider trades (EDGAR), financial statement calculators (DuPont, Z-Score, M-Score, F-Score), portfolio analytics (VaR, stress testing, health scoring), multi-factor screening, and macro indicators (FRED). Use when you need live US market data to ground investment analysis. All data sources are free — no API keys required.

Why use Findata Toolkit Us on TypingMind?

Because you install it once and use it with any model. Findata Toolkit Us is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Findata Toolkit Us in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Geeksfino/finskills/tree/main/US-market/findata-toolkit. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Findata Toolkit Us?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Findata Toolkit Us?

As many as you like. As long as a model supports skills, you can use Findata Toolkit Us with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Findata Toolkit Us AI skill free?

Yes. It is published on GitHub by Geeksfino under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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