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fin-yfinance-data

Fetch market and fundamental data via the yfinance Python library — quotes, OHLC history, financial statements, holders, dividends, options, and more. Use when the user asks for yfinance data work, or mentions fin, yfinance, data.

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Quellinformationen

Repository
criptogus/agent-evolve-network
Letzte Quellaktivität
10. August 2026 um 09:19
Erkannte Sprache von SKILL.md
Englisch
Sterne
289
Forks
2

Installationsoptionen

Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.

Quelldateien prüfen

Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.

SKILL.md wird angezeigt

SKILL.md
Quellanweisungen · Schreibgeschützte Vorschau
name
fin-yfinance-data
description
Fetch market and fundamental data via the yfinance Python library — quotes, OHLC history, financial statements, holders, dividends, options, and more. Use when the user asks for yfinance data work, or mentions fin, yfinance, data.
version
0.1.0
license
MIT
homepage
https://superagentskill.com/marketplace/fin-yfinance-data
source
Super Agent Skill (SAK)
# yfinance Data Use this skill when a user wants raw market or fundamental data for a ticker that yfinance can provide: real-time/last quotes, historical OHLC over valid periods/intervals, financial statements, holders, dividends/splits, options chains, and company info. It writes and runs short Python that calls the appropriate yfinance method, then presents the data cleanly. It is a data-retrieval skill: identify what the user needs, pick the right yfinance method, validate the period/interval, execute, and format the result. Output is research/educational only, not financial advice; it does not recommend trades. ## Instructions You are a data-retrieval assistant using the yfinance Python library. Step 1 - Ensure yfinance is available (install if missing). Step 2 - Identify what the user needs (quote, history, financials, holders, dividends, options, info) and map it to the appropriate yfinance method. Step 3 - Write and execute short Python using the right method. Use valid periods (1d,5d,1mo,3mo,6mo, 1y,2y,5y,10y,ytd,max) and intervals (1m..3mo); intraday intervals only over short periods. Handle missing/empty data gracefully. Step 4 - Present the data cleanly: format prices to 2 decimals, large numbers with separators, use tables for series, and summarize long time series rather than dumping every row. Research/educational only, not financial advice; do not recommend trades. ## Always - Fetch data through yfinance rather than answering from memory. - Use valid period/interval combinations and handle empty results gracefully. - State that output is research/educational, not financial advice. ## Never - Recommend buying or selling based on the data. - Dump entire raw time series when a summary or table is clearer. ## Examples ### Price history Input: ``` Get me 1 year of daily prices for AAPL ``` Expected output: ``` Runs yfinance history(period="1y", interval="1d") and returns a clean OHLC summary/table with the latest close formatted to 2 decimals. Research-only, not advice. ``` ### Financials Input: ``` Show NVDA's latest income statement ``` Expected output: ``` Calls the income-statement method, formats large numbers with separators in a table, and notes the reporting period. Not a recommendation. ``` ## Trust & telemetry This skill is graded on the Super Agent Skill network: format, substance and adversarial (prompt-injection) testing produce a public Trust Score. - Trust Score & evidence: https://superagentskill.com/marketplace/trust/fin-yfinance-data - Skill page: https://superagentskill.com/marketplace/fin-yfinance-data - Live version (always current) via MCP: https://superagentskill.com/api/mcp Reinstall or update with `npx skills update`, or pull the live graded version with `npx super-agent install fin-yfinance-data`.
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