Agent Skills: Stock Comparison

Compare sentiment and blogger opinions between two stocks. Use when users want to analyze NVDA vs AMD, or any two tickers side by side.

UncategorizedID: alphamoemoe/foci/compare

Install this agent skill to your local

pnpm dlx add-skill https://github.com/AlphaMoeMoe/FOCI/tree/HEAD/plugins/AlphaMoeMoe/skills/compare

Skill Files

Browse the full folder contents for compare.

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plugins/AlphaMoeMoe/skills/compare/SKILL.md

Skill Metadata

Name
compare
Description
Compare sentiment and blogger opinions between two stocks. Use when users want to analyze NVDA vs AMD, or any two tickers side by side.

Stock Comparison

Compare sentiment and blogger opinions between two stocks.

Triggers

  • "对比 NVDA 和 AMD"
  • "比较这两只股票"
  • "compare NVDA vs AMD"
  • "NVDA 和 AMD 哪个好"
  • /compare NVDA AMD
  • /compare {ticker1} {ticker2}

Arguments

  • ticker1 - First stock ticker (required)
  • ticker2 - Second stock ticker (required)

Instructions

When the user wants to compare two stocks, follow these steps:

  1. Get Sentiment for Both Stocks Call get_ticker_sentiment for both tickers in parallel to get sentiment data.

  2. Search for Related Viewpoints Call search_viewpoints with each ticker to find detailed blogger opinions.

  3. Analyze and Compare Create a side-by-side comparison covering:

    • Overall sentiment score
    • Number of bullish vs bearish bloggers
    • Key arguments for each side
    • Common themes in viewpoints
  4. Present Results Format the output as:

    ## 股票对比: TICKER1 vs TICKER2
    
    | 指标 | TICKER1 | TICKER2 |
    |------|---------|---------|
    | 整体情绪 | 🟢 看涨 | 🟡 中性 |
    | 看涨博主 | X 位 | Y 位 |
    | 看跌博主 | X 位 | Y 位 |
    | 提及次数 | XX | YY |
    
    ### TICKER1 关键观点
    **看涨理由:**
    - [观点1] — 博主A
    - [观点2] — 博主B
    
    **风险提示:**
    - [风险1] — 博主C
    
    ### TICKER2 关键观点
    **看涨理由:**
    - [观点1] — 博主D
    
    **风险提示:**
    - [风险1] — 博主E
    
    ### 综合分析
    [基于数据的客观分析,指出两者的优劣势]
    

Tool Sequence

  1. get_ticker_sentiment(ticker1) + get_ticker_sentiment(ticker2) → In parallel
  2. search_viewpoints(ticker1) + search_viewpoints(ticker2) → In parallel
  3. Compile comparison table and analysis

Notes

  • Always present data objectively, let users make their own decisions
  • Highlight where bloggers disagree
  • If one ticker has significantly more coverage, note this caveat
  • Do NOT give buy/sell recommendations