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us-stock-radar美股雷达

Agent Skill

us-stock-radar 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。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

9,314

周安装

396

GitHub Stars

公开资料未说明

下载量

3,263
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:us-stock-radar(美股雷达)
来源仓库:https://github.com/spyfree/us-stock-radar
安装命令:
openclaw skills install us-stock-radar
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 us-stock-radar

简介

us-stock-radar 利用公开市场数据进行股票筛选与观察名单管理。

  • 支持 A/B/C 分级排名,适用于投资组合初筛场景。
  • 通过 clawhub 安装后可用于多因子选股模型辅助决策。
  • 需确认是否依赖实时行情接口并检查延迟容忍度。us-stock-radar 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 建议核实其是否提供技术指标背离与量价异动警报功能。

SKILL.md

name
us-stock-radar
description
Professional US stock radar for screening, deep dives, and watchlist alerts using public market data. Use when the user wants ranked stock candidates, A/B/C/D signal grading, timestamped evidence, confidence-aware summaries, and cleaner pro or beginner explanations for US equities.

US Stock Radar

Run a practical US stock workflow in 3 modes:

  • screener: rank a ticker universe by multi-factor signal score
  • deep-dive: analyze one ticker with fundamentals + technical proxies
  • watchlist: monitor custom tickers and output alert candidates

This skill is a read-only heuristic market workflow, not a full institutional research terminal. Public free endpoints may be partial, delayed, or rate-limited; surface those gaps explicitly.

Workflow

  1. Run scripts/us_stock_radar.py with the appropriate mode.
  2. Read JSON output first; treat it as the source of truth.
  3. Explain conclusions with explicit caveats, confidence, and data gaps.
  4. Avoid deterministic predictions; present signal grade, trigger reasons, and partial-data warnings.
  5. If some endpoints fail, continue with degraded coverage and expose the reduction in confidence.

Quick Audit Path

For a fast review:

  1. Run python3 skills/us-stock-radar/scripts/us_stock_radar.py --sources
  2. Run python3 skills/us-stock-radar/scripts/us_stock_radar.py --mode screener --json
  3. Confirm the script only performs read-only public HTTP requests.
  4. Verify that availability, data_gaps, and degraded_mode are exposed when coverage is partial.

Commands

python3 skills/us-stock-radar/scripts/us_stock_radar.py --sources
python3 skills/us-stock-radar/scripts/us_stock_radar.py --version
python3 skills/us-stock-radar/scripts/us_stock_radar.py --mode screener --tickers "AAPL,MSFT,NVDA,AMZN,GOOGL"
python3 skills/us-stock-radar/scripts/us_stock_radar.py --mode deep-dive --ticker AAPL --audience pro
python3 skills/us-stock-radar/scripts/us_stock_radar.py --mode deep-dive --ticker TSLA --audience beginner --lang zh
python3 skills/us-stock-radar/scripts/us_stock_radar.py --mode watchlist --tickers "AAPL,NVDA,TSLA" --event-mode high-alert
python3 skills/us-stock-radar/scripts/us_stock_radar.py --mode screener --json

Safety / Scope Boundary

  • Read-only skill: query public market endpoints only.
  • Use no authentication, cookies, brokerage accounts, or private APIs.
  • Place no orders, execute no trades, and mutate no portfolio state.
  • Write no files and send no outbound messages as part of normal use.
  • Produce analysis only; not investment advice.

Output Policy

  • Default language behavior: auto.
  • If --lang auto and the prompt contains Chinese, switch final narrative to Chinese.
  • If --lang auto and no Chinese is detected, use English.
  • --json output is language-neutral.
  • Always include:

- as_of_utc - mode - event_mode - availability - data_gaps - degraded_mode - confidence - sources - heuristic notes / caveats

  • Audience modes:

- pro: concise signal summary - beginner: plain-language interpretation

  • Event modes:

- normal - high-alert (stricter thresholds)

Scoring (A/B/C/D)

Signal score combines heuristic checks such as:

  • valuation range (PE)
  • RSI health
  • volume expansion
  • price vs MA50
  • revenue growth
  • ROE quality

Grades:

  • A: score >= 5
  • B: score = 4
  • C: score = 2-3
  • D: score <= 1

Interpretation Guardrails

  • Grades are heuristic summaries, not price targets.
  • Missing fundamentals should lower confidence rather than silently default bullish/bearish.
  • Free-market endpoints can be delayed, partially populated, or blocked by region.
  • Premarket / scheduled workflows should keep timestamps explicit.

Data Sources

  • Yahoo Finance quote API: /v7/finance/quote
  • Yahoo Finance chart API: /v8/finance/chart
  • Yahoo Finance quoteSummary API: /v10/finance/quoteSummary
  • Stooq public fallback: daily quote / history CSV endpoints

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

83.82%
按下载量换算2,735

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

执行命令

安装流程涉及命令执行,可能通过 openclaw skills install us-stock-radar 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

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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