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Macrotrends

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gauss314
macrotrends

Scraper de Macrotrends: financial statements, ratios, employee count con 15+ años de data histórica. Sin API key.

Overview

Publishergauss314
Repositoryskills
Skill namemacrotrends
Stars
250
Forks
35
Bundled files
3
LicenseMIT
Links
  • 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.

  • 3 bundled files

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

  • Open source

    Published by gauss314 on GitHub. Read the source before you install it.

Installation

Install the Macrotrends 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/gauss314/skills/tree/main/skills/macrotrends
  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/gauss314/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/macrotrends .claude/skills/macrotrends
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Macrotrends 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 Macrotrends 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 Macrotrends 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.

Macrotrends — Financial Data Scraper

Scraper de Macrotrends que extrae datos financieros históricos: income statements, balance sheets, cash flow statements, financial ratios y employee count con hasta 15+ años de data para ~6,500 tickers de mercados US (NYSE, NASDAQ, AMEX), incluyendo ADRs internacionales de +30 países.

Los datos vienen en JSON embebido en la página (no requiere API key, solo requests + BeautifulSoup).


⚠️ Aviso Legal

  • Macrotrends no tiene API pública oficial. Este scraper accede a datos públicamente disponibles en la web.
  • Respetá los términos de servicio de Macrotrends.
  • Implementá rate limiting (mín 2 segundos entre requests, recomendado 3s).
  • El sitio puede cambiar su estructura HTML/JS, rompiendo el scraper.

Instalación

bash
pip install requests beautifulsoup4

Scripts

ScriptDescripción
fetch_financials.pyScript principal: statements, ratios, employees

Uso rápido

bash
# Income statement anual
python scripts/fetch_financials.py --ticker NVDA --income

# Todo lo disponible (income + balance + cashflow + ratios + employees)
python scripts/fetch_financials.py --ticker AAPL --all

# Statements trimestrales
python scripts/fetch_financials.py --ticker MSFT --income --quarterly
python scripts/fetch_financials.py --ticker MSFT --balance --quarterly

# Ratios anuales o trimestrales
python scripts/fetch_financials.py --ticker NVDA --ratios
python scripts/fetch_financials.py --ticker GGAL --ratios --quarterly

# Employee count
python scripts/fetch_financials.py --ticker NVDA --employees

# Output a archivo JSON
python scripts/fetch_financials.py --ticker AAPL --all --output data.json

# Silencioso (solo JSON)
python scripts/fetch_financials.py --ticker NVDA --all --quiet

Endpoints disponibles

FlagDataFrecuenciasPeríodos
--incomeIncome StatementAnual, Trimestral~15 años / ~60 quarters
--balanceBalance SheetAnual, Trimestral~15 años / ~60 quarters
--cashflowCash FlowSolo anual~15 años
--ratiosFinancial Ratios (20 ratios)Anual, Trimestral~15 años / ~60 quarters
--employeesEmployee CountAnual~15 años
--allTodos los anterioresSegún --quarterly-

Flags adicionales

FlagDescripción
--quarterlyUsa frecuencia trimestral (donde esté disponible)
--outputArchivo JSON de salida (default: stdout)
--slugSlug manual (si el search no funciona)
--delayDelay entre requests (default: 3.0s)
--quietModo silencioso

Resolución ticker → slug

Macrotrends usa URLs con el formato /stocks/charts/{TICKER}/{SLUG}/financial-statements.

El slug se resuelve automáticamente via el search endpoint interno. Ejemplos:

TickerSlug
AAPLapple
NVDAnvidia
MSFTmicrosoft
GGALgrupo-financiero-galicia-sa
TSLAtesla

Si el search falla, se puede pasar el slug manualmente:

bash
python scripts/fetch_financials.py --ticker GGAL --slug grupo-financiero-galicia-sa --all

Datos disponibles por statement

Income Statement (22 campos)

Revenue, COGS, Gross Profit, R&D, SG&A, Operating Income, Net Income, EBITDA, EBIT, EPS, Shares Outstanding, etc.

Balance Sheet (23 campos)

Cash, Receivables, Inventory, Total Assets, Total Liabilities, Shareholders Equity, Debt, Retained Earnings, etc.

Cash Flow (29 campos)

Net Income, Depreciation, Working Capital Changes, Capex, Free Cash Flow, Dividends, Stock Repurchases, etc.

Financial Ratios (20 ratios)

Current Ratio, Debt/Equity, Gross/Operating/Net Margins, ROE, ROA, ROI, Book Value/Share, FCF/Share, etc.

Employee Count

Año a año, empleados totales.

Ver referencia completa de campos en references/STATEMENTS_REFERENCE.md.


Cobertura de Mercados

Macrotrends cubre ~6,500 tickers que cotizan en NYSE, NASDAQ y AMEX (incluyendo ADRs).

TipoCantidadEjemplos
US Common Stocks~5,300AAPL, NVDA, MSFT
ADRs Internacionales~276+GGAL (Argentina), BABA (China), TM (Japón), PBR (Brasil), SAN (España), BP (UK)
ETFs & Fondos~91SPY, QQQ, VTI
Class A/B Shares~22BRK.A, BRK.B

Ver detalle completo de mercados, ADRs por país y cómo buscar tickers en references/MARKETS_REFERENCE.md.


Rate Limits

ComportamientoRecomendación
Requests seguidos sin delay429 Too Many Requests
Delay recomendado3 segundos entre requests
Tickers por ejecución~5-6 como máximo (para evitar bloqueo)
BackoffSi recibís 429, esperá 30-60s

Errores comunes

ErrorCausaSolución
429 Too Many RequestsRate limitAumentar --delay o esperar
slug not foundNo se pudo resolver el tickerUsar --slug manualmente
No data found for XEndpoint no disponibleVerificar que el ticker existe en Macrotrends
originalData not foundPágina cambió estructuraReportar issue

Estructura del skill

skills/macrotrends/
├── SKILL.md                              # Este archivo
├── references/
│   ├── STATEMENTS_REFERENCE.md           # Referencia de campos
│   └── MARKETS_REFERENCE.md             # Cobertura de mercados y ADRs
└── scripts/
    └── fetch_financials.py               # Script principal

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 Macrotrends AI skill do?

Scraper de Macrotrends: financial statements, ratios, employee count con 15+ años de data histórica. Sin API key.

Why use Macrotrends on TypingMind?

Because you install it once and use it with any model. Macrotrends 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 Macrotrends in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/gauss314/skills/tree/main/skills/macrotrends. 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 Macrotrends?

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 Macrotrends?

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

Is the Macrotrends AI skill free?

Yes. It is published on GitHub by gauss314 under the MIT 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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