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canslim-top100-usCanslim 美国 100 强

Agent Skill

canslim-top100-us 用于整理文档、README、Markdown 和说明材料,适合在 OpenClaw 中需要把零散信息整理成结构清晰的文档时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

7,380

周安装

317

GitHub Stars

公开资料未说明

下载量

2,587
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:canslim-top100-us(Canslim 美国 100 强)
来源仓库:https://github.com/lkmsteven/canslim-top100-us
安装命令:
openclaw skills install canslim-top100-us
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install canslim-top100-us

简介

使用 CANSLIM 风格的信号分析按市值排名前 100 名的 S&P 500 公司,并返回 Markdown 中的排名候选名单。

SKILL.md

name
CANSLIM-Top100-US
description
Analyze the top 100 S&P 500 companies by market capitalization using CANSLIM-style signals and return a ranked shortlist in Markdown.
user-invocable
true
requires
os

CANSLIM S&P 500 Analyzer

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.

When to use

Use this skill when the user asks to:

  • Run a CANSLIM analysis on large-cap U.S. stocks.
  • Screen S&P 500 leaders by growth, momentum, and institutional-quality signals.
  • Generate a ranked shortlist of CANSLIM-style candidates.

Inputs

Expected local files:

  • Scripts/analyzer.py
  • Scripts/requirements.txt (if dependencies are not already installed)

Expected script output:

  • canslim_results.json (in root directory)
  • Optional: canslim_results.csv

Execution rules

Follow this checklist exactly:

  1. Confirm that Scripts/analyzer.py exists.
  2. If dependencies are missing, install them from Scripts/requirements.txt.
  3. Change to the Scripts directory or run python Scripts/analyzer.py from root.
  4. Wait for the script to finish successfully.
  5. Read canslim_results.json.
  6. Rank stocks by CANSLIM score from highest to lowest.
  7. Present the best candidates in a Markdown table.
  8. Explain which CANSLIM letters each top stock passed or failed.
  9. If no stock is a strong match, show the top 3 closest candidates instead.

Analysis guidance

Interpret the script output using these principles:

  • C: strong recent quarterly earnings growth.
  • A: strong annual growth trend.
  • N: price near new highs or supported by a fresh catalyst.
  • S: favorable supply-demand signal such as strong volume.
  • L: market leadership versus weaker peers.
  • I: meaningful institutional sponsorship.
  • M: favorable trend or market direction signal.

Do not invent missing metrics. If any field is unavailable, say that the data is unavailable and continue with the remaining signals.

Output format

Return:

  • A 1-2 sentence overall assessment.
  • A Markdown table with the top candidates.
  • A short bullet list explaining why the top names ranked highly.
  • A note listing any missing data, weak signals, or caveats.

Use this table format:

RankTickerCompanyScorePassedFailedNotes

Constraints

  • Use only the files generated by this skill run.
  • Do not claim the results are investment advice.
  • Do not fabricate company names, prices, or scores.
  • If the script fails, clearly report the failure and suggest checking dependencies or network access for market data.

Failure handling

If execution fails:

  • State which step failed.
  • Include the error message if available.
  • Recommend the smallest next action, such as installing dependencies or rerunning the script.

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

94.64%
按下载量换算2,448

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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