Agent skill · Backend & API

marketscreener

Scraper de MarketScreener (S&P Capital IQ): earnings transcripts, cotizaciones, perfiles empresa, financials, valuation, consenso analistas, noticias, insider trading, ratings. Sin API key.

Juanpy170★ · +3/wk · 1 repos on radarProfile →
claude-codeships scriptsMIT
Install
npx skills add gauss314/skills --skill marketscreener --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 4
SKILL.md size: 4 KB
Bundled scripts: yes
Path: skills/marketscreener/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 172 · +2 this week
Language: Python
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# MarketScreener — Datos Financieros y Earnings Transcripts Globales Scraper de **MarketScreener** (plataforma de S&P Capital IQ) que accede a **datos gratuitos sin registro**: earnings transcripts, cotizaciones, perfiles, financials históricos, valuación, consenso de analistas, noticias, insider trading y ratings. **URL base:** `https://www.marketscreener.com` **País soportado:** Global (20,000+

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About this skill
What does the marketscreener skill do?

Scraper de MarketScreener (S&P Capital IQ): earnings transcripts, cotizaciones, perfiles empresa, financials, valuation, consenso analistas, noticias, insider trading, ratings. Sin API key.

How do I install it?

Run `npx skills add gauss314/skills --skill marketscreener --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From gauss314/skills, a repository with 172 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going