AgentSkillsCN

historical-stock-data-collector

当用户请求获取特定股票代码在指定日期范围内的历史股价数据时，此技能会从金融API中获取每日交易数据（开盘价、最高价、最低价、收盘价、成交量）。该技能可同时处理多个股票代码，自动排除非交易日，并可选择使用调整后价格或未调整价格。触发条件包括“股票数据”、“历史价格”、“每日交易数据”，或像AAPL、TSLA等特定股票代码的请求。

SKILL.md
--- frontmatter
name: historical-stock-data-collector
description: When the user requests historical stock price data for specific tickers over a date range, this skill retrieves daily trading data (open, high, low, close, volume) from financial APIs. It handles multiple tickers simultaneously, excludes non-trading days automatically, and can specify whether to use adjusted or unadjusted prices. Triggers include requests for 'stock data', 'historical prices', 'daily trading data', or specific ticker symbols like AAPL, TSLA, etc.

Instructions

1. Understand the Request

  • •Identify the target tickers (e.g., AAPL, TSLA, NVDA, META).
  • •Identify the date range (e.g., June-July 2025).
  • •Clarify if the user wants adjusted (auto_adjust: true) or unadjusted (auto_adjust: false) prices. Default to unadjusted prices unless specified.
  • •Identify any specific output format or destination (e.g., Google Sheet, CSV file, Notion page).

2. Retrieve Historical Data

For each identified ticker:

  • •Use the yahoo-finance-get_historical_stock_prices tool.
  • •Set interval to "1d".
  • •Set auto_adjust based on user preference.
  • •The API automatically excludes non-trading days.

3. Process and Format Data

  • •The required output columns are: Ticker, Date, Open, High, Low, Close, Volume.
  • •The Date should be formatted as YYYY-MM-DD.
  • •Combine data from all tickers into a single list, sorted by Date and then by Ticker.
  • •Use the provided scripts/process_stock_data.py for reliable, repeatable data transformation.

4. Output to Specified Destination

If the destination is a Google Sheet:

  1. •Create a new spreadsheet or identify an existing one using google_sheet-create_spreadsheet or google_sheet-get_spreadsheet.
  2. •Ensure the target worksheet exists and is correctly named. Use google_sheet-rename_sheet if needed.
  3. •Write the formatted data to the sheet starting from cell A1. Use google_sheet-write_to_cells.
  4. •Note the final spreadsheet URL.

If the destination is a Notion Page:

  1. •Locate the target Notion page using notion-API-post-search.
  2. •Append a new line with the text Google Sheet : {url} or the relevant data summary using notion-API-append-block.
  3. •If requested, add a comment/note at the top of the page using notion-API-append-block with the exact specified text.

5. Error Handling & Validation

  • •If a ticker returns no data for the date range, log a warning and continue with other tickers.
  • •Verify the final dataset is not empty before writing to the output destination.
  • •If writing to Google Sheets, check the operation was successful.

Key Principles

  • •Conciseness: Use scripts for deterministic data processing.
  • •Reliability: The financial API handles date filtering; do not manually calculate trading days.
  • •Clarity: Confirm data parameters (like auto_adjust) with the user if ambiguous.