findata-toolkit-us

Name: findata-toolkit-us
Author: geeksfino

github.com/geeksfinoData & databases28586 installsUpdated 34w ago

About findata-toolkit-us

FinData Toolkit — US Market is a financial data and quantitative analysis toolkit for US market research and portfolio analysis. Its command-line scripts cover stocks, SEC filings, macro data, factor screening, and portfolio risk, with JSON output and data sources that require no API keys.

Key features

01Fetch stock fundamentals, price history, and financial metrics
02Retrieve SEC filings and insider trades
03Calculate financial quality and risk measures
04Analyze portfolio concentration, correlations, and risk
05Screen stocks with multiple factors
06View US macroeconomic indicators

Use cases

01Review company financials, valuation, and price history
02Inspect SEC disclosures and insider trades
03Assess portfolio risk and run historical stress tests
04Screen stocks or review macroeconomic conditions

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/geeksfino/finskills --skill findata-toolkit-us

After installing, type/findata-toolkit-usin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

LICENSE.txt

requirements.txt

config/

data_sources.yaml

scripts/

factor_screener.py

financial_calc.py

macro_data.py

portfolio_analytics.py

sec_edgar.py

stock_data.py

scripts/common/

__init__.py

config.py

utils.py

Details

From the project README

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

4. Portfolio Analytics (`scripts/portfolio_analytics.py`)

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

Command	Purpose
`python scripts/portfolio_analytics.py --holdings "AAPL:30,MSFT:25,GOOGL:20,AMZN:15,META:10"`	Full health score (0–100)
`... --concentration`	Concentration analysis (HHI, sector)
`... --correlation`	Correlation clusters & EDR
`... --risk`	VaR/CVaR, Sharpe, Sortino, beta
`... --stress`	Historical stress testing (5 scenarios)

5. Factor Screener (`scripts/factor_screener.py`)

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

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

6. Macro Data (`scripts/macro_data.py`)

US macroeconomic indicators from FRED.

Command	Purpose
`python scripts/macro_data.py --dashboard`	Full macro dashboard
`python scripts/macro_data.py --rates`	Interest rates & yield curve
`python scripts/macro_data.py --inflation`	CPI, PCE, breakevens
`python scripts/macro_data.py --gdp`	GDP & leading indicators
`python scripts/macro_data.py --employment`	Unemployment, payrolls, JOLTS
`python scripts/macro_data.py --cycle`	Business cycle phase assessment

Data Sources

Source	Data	API Key
Yahoo Finance (yfinance)	Stock quotes, financials, history	Not required
SEC EDGAR	Filings, insider trades (Form 4)	Not required
FRED	Macro indicators	Not 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.

Comments

Markdown supported

Details

Author: geeksfino
Repository: geeksfino/finskills
Category: Data & databases
Installs: 86
GitHub stars: 285

Report

More skills

All skills

hyperframes-core

github.com/heygen-com

930.6k

The HyperFrames composition contract — build one renderable project. Use for composition structure, the `data-*` timing attributes, `class="clip"`, tracks, sub-compositions, variables, framework-owned media playback, deterministic-render rules, and validation. Also covers Tailwind projects and the STORYBOARD.md / SCRIPT.md plan formats. Read before writing composition HTML.

lark-base

open.feishu.cn

823.2k

Operate on Feishu Base: create tables, fields, records, and views; handle statistics, formulas/lookups, forms, dashboards, app mode (BaseApp/AppMode pages and components), Workspace directories, workflows, role permissions, and the template center (Base template categories, lists, and search). Use when encountering Base/multidimensional tables/bitable, BaseApp/AppMode, or /base/ or /app/ links. Do not use lark-apps for BaseApp; route file import/export to lark-drive and authentication/authorization to lark-shared.

lark-sheets

open.feishu.cn

821.8k

Feishu Spreadsheets: create and operate spreadsheets. Supports worksheets and row/column structures (add/delete/merge/size/hide/freeze/group), cell read/write (values/formulas/styles/comments/cell images), range copy/move/sort/fill, find and replace, bulk updates, and creating and maintaining objects such as charts, pivot tables, conditional formatting, filters and filter views, dropdown lists, sparklines, and floating images. Also supports formula validation, restoring historical versions, and importing/exporting local Excel/CSV files and Feishu spreadsheets. Use when users need to create or edit spreadsheets, summarize and visualize data, format spreadsheets, calculate formulas (including migrating Excel formulas), or build financial models (DCF, three statements, budgets, Sensitivity, etc.). For multidimensional tables (Base/bitable), use lark-base. If users want to search for spreadsheet files in cloud storage (Drive/cloud storage) by name or keyword, use lark-drive's drive +search to locate resources first. When users provide a doubao.com /sheets/ URL/token, use this skill directly; do not fall back to WebFetch because the domain is not Feishu. Routing is based on the URL path pattern and token, not the domain.

remotion-best-practices

github.com/remotion-dev

701.4k

Best practices and domain knowledge for building videos programmatically with Remotion (videos in React/TypeScript, rendered to MP4). Use whenever writing or editing Remotion code — scaffolding a video project, animating with interpolate/spring over useCurrentFrame(), sequencing scenes and transitions, captions/subtitles, audio, GIFs/Lottie/3D, charts, visual effects, dynamic duration via calculateMetadata, or rendering. Consult before reaching for a Remotion API, since its components and props evolve (e.g. @remotion/media).

findata-toolkit-us

About findata-toolkit-us

Key features

Use cases

Install

Bundled files

Details

FinData Toolkit — US Market

Setup

Available Tools

1. Stock Data (scripts/stock_data.py)

2. SEC EDGAR (scripts/sec_edgar.py)

3. Financial Calculators (scripts/financial_calc.py)

4. Portfolio Analytics (scripts/portfolio_analytics.py)

5. Factor Screener (scripts/factor_screener.py)

6. Macro Data (scripts/macro_data.py)

Data Sources

Output Format

Configuration

Comments

1. Stock Data (`scripts/stock_data.py`)

2. SEC EDGAR (`scripts/sec_edgar.py`)

3. Financial Calculators (`scripts/financial_calc.py`)

4. Portfolio Analytics (`scripts/portfolio_analytics.py`)

5. Factor Screener (`scripts/factor_screener.py`)

6. Macro Data (`scripts/macro_data.py`)