/install canslim-top100-us
\r \r
CANSLIM S&P 500 Analyzer\r
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Analyze the top 100 S&P 500 stocks by market capitalization using the local analyzer.py script, then summarize the strongest candidates for the user.\r
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When to use\r
Use this skill when the user asks to:\r
- Run a CANSLIM analysis on large-cap U.S. stocks.\r
- Screen S&P 500 leaders by growth, momentum, and institutional-quality signals.\r
- Generate a ranked shortlist of CANSLIM-style candidates.\r \r
Inputs\r
Expected local files:\r
Scripts/analyzer.py\rScripts/requirements.txt(if dependencies are not already installed)\r \r Expected script output:\rcanslim_results.json(in root directory)\r- Optional:
canslim_results.csv\r \r
Execution rules\r
Follow this checklist exactly:\r
- Confirm that
Scripts/analyzer.pyexists.\r - If dependencies are missing, install them from
Scripts/requirements.txt.\r - Change to the Scripts directory or run
python Scripts/analyzer.pyfrom root.\r - Wait for the script to finish successfully.\r
- Read
canslim_results.json.\r - Rank stocks by CANSLIM score from highest to lowest.\r
- Present the best candidates in a Markdown table.\r
- Explain which CANSLIM letters each top stock passed or failed.\r
- If no stock is a strong match, show the top 3 closest candidates instead.\r \r
Analysis guidance\r
Interpret the script output using these principles:\r
C: strong recent quarterly earnings growth.\rA: strong annual growth trend.\rN: price near new highs or supported by a fresh catalyst.\rS: favorable supply-demand signal such as strong volume.\rL: market leadership versus weaker peers.\rI: meaningful institutional sponsorship.\rM: favorable trend or market direction signal.\r \r Do not invent missing metrics. If any field is unavailable, say that the data is unavailable and continue with the remaining signals.\r \r
Output format\r
Return:\r
- A 1-2 sentence overall assessment.\r
- A Markdown table with the top candidates.\r
- A short bullet list explaining why the top names ranked highly.\r
- A note listing any missing data, weak signals, or caveats.\r \r Use this table format:\r \r | Rank | Ticker | Company | Score | Passed | Failed | Notes |\r |---|---|---|---:|---|---|---|\r \r
Constraints\r
- Use only the files generated by this skill run.\r
- Do not claim the results are investment advice.\r
- Do not fabricate company names, prices, or scores.\r
- If the script fails, clearly report the failure and suggest checking dependencies or network access for market data.\r \r
Failure handling\r
If execution fails:\r
- State which step failed.\r
- Include the error message if available.\r
- Recommend the smallest next action, such as installing dependencies or rerunning the script.\r
- 确保已安装 OpenClaw(本地或 Docker 部署)
- 在对话框中输入安装命令:
/install canslim-top100-us - 安装完成后,直接呼叫该 Skill 的名称或使用
/canslim-top100-us触发 - 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
CANSLIM-Top100-US 是什么?
Analyze the top 100 S&P 500 companies by market capitalization using CANSLIM-style signals and return a ranked shortlist in Markdown. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 251 次。
如何安装 CANSLIM-Top100-US?
在 OpenClaw 或 Claude Code 对话框中运行命令「/install canslim-top100-us」即可一键安装,无需额外配置。
CANSLIM-Top100-US 是免费的吗?
是的,CANSLIM-Top100-US 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。
CANSLIM-Top100-US 支持哪些平台?
CANSLIM-Top100-US 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。
谁开发了 CANSLIM-Top100-US?
由 lkmsteven(@lkmsteven)开发并维护,当前版本 v1.0.0。