Token导航 LogoToken导航TokenDH.com
研究检索external-servicegithub未标认证来源可访问许可证需确认审计异常

stock-performance股票表现

Agent Skill

stock-performance 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 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

1,670

周安装

71

GitHub Stars

37

下载量

585
CodexClaudeCursorGemini CLI

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

unknown

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:stock-performance(股票表现)
来源仓库:https://github.com/octagonai/skills
仓库路径:skills/stock-performance
安装命令:
npx skills add https://github.com/octagonai/skills --skill stock-performance
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 npx skills 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。

skills.shnpx skills
npx skills add https://github.com/octagonai/skills --skill stock-performance

简介

用于股票表现数据的查找与筛选,支持收益率与波动率分析。

  • 适用于绩效归因、基准对比或趋势判断场景。
  • 通过调用 GitHub 仓库中的数据接口返回历史表现指标。
  • 建议核对计算周期与复权方式以确保可比性。
  • 使用前应确认数据覆盖范围与更新频率。stock-performance 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Stock Performance

Retrieve daily closing prices, trading volume, and performance metrics for public companies using the Octagon MCP server.

Prerequisites

Ensure Octagon MCP is configured in your AI agent (Cursor, Claude Desktop, Windsurf, etc.). See references/mcp-setup.md for installation instructions.

Workflow

1. Identify Analysis Parameters

Determine the following before querying:

  • Ticker: Stock symbol (e.g., AAPL, MSFT, GOOGL)
  • Time Period: Number of days or date range
  • Metrics (optional): Price, volume, returns

2. Execute Query via Octagon MCP

Use the octagon-agent tool with a natural language prompt:

Retrieve the daily closing prices for <TICKER> over the last <N> days.

MCP Call Format:

{
  "server": "octagon-mcp",
  "toolName": "octagon-agent",
  "arguments": {
    "prompt": "Retrieve the daily closing prices for AAPL over the last 30 days."
  }
}

3. Expected Output

The agent returns structured price data including:

DateClosing PriceVolume
2026-02-02$270.0173,677,607
2026-01-30$259.4892,443,408
2026-01-29$258.2867,253,009
.........

Data Sources: octagon-stock-data-agent, octagon-web-search-agent

4. Interpret Results

See references/interpreting-results.md for guidance on:

  • Analyzing price trends
  • Evaluating volume patterns
  • Calculating returns
  • Identifying support/resistance levels

Example Queries

Daily Closing Prices:

Retrieve the daily closing prices for AAPL over the last 30 days.

Extended Historical Data:

Get historical stock prices for MSFT for the past 90 days.

Volume Analysis:

Retrieve daily trading volume for TSLA over the last 2 weeks.

Price Range:

What are the high and low prices for NVDA over the past month?

Multi-Stock Comparison:

Compare the stock performance of AAPL, MSFT, and GOOGL over the last 30 days.

52-Week Analysis:

What is the 52-week high and low for AMZN?

Key Metrics

Price Metrics

MetricDescription
Closing PriceEnd-of-day price
Opening PriceStart-of-day price
HighIntraday high
LowIntraday low
Adjusted CloseDividend/split adjusted

Volume Metrics

MetricDescription
Daily VolumeShares traded per day
Average VolumeTypical daily volume
Relative VolumeCurrent vs. average
Volume TrendDirection over time

Return Metrics

MetricCalculation
Daily Return(Close - Prior Close) / Prior Close
Period Return(End - Start) / Start
Cumulative ReturnRunning return over period
Annualized ReturnPeriod return scaled to 1 year

Price Analysis Framework

Trend Analysis

PatternCharacteristics
UptrendHigher highs, higher lows
DowntrendLower highs, lower lows
SidewaysRange-bound movement
BreakoutMove beyond range

Volatility Assessment

MeasureDescription
Price RangeHigh - Low over period
Daily RangeAverage daily high-low
Standard DeviationPrice dispersion
BetaRelative to market

Support/Resistance

LevelDescription
SupportPrice floor, buying interest
ResistancePrice ceiling, selling pressure
Moving AveragesDynamic support/resistance
Round NumbersPsychological levels

Volume Analysis

Volume Patterns

PatternInterpretation
High Volume + Price UpStrong buying conviction
High Volume + Price DownStrong selling pressure
Low Volume + Price UpWeak rally, may reverse
Low Volume + Price DownLack of selling interest

Volume Indicators

IndicatorUsage
Volume SpikeUnusual activity, potential catalyst
Volume Dry-upConsolidation, waiting mode
Volume TrendConfirms price trend
On-Balance VolumeCumulative volume direction

Time Period Analysis

Short-Term (1-30 Days)

FocusUse Case
Recent PerformanceCurrent momentum
Trading SignalsEntry/exit timing
News ImpactEvent analysis
VolatilityRisk assessment

Medium-Term (1-6 Months)

FocusUse Case
Trend IdentificationDirection confirmation
SeasonalityCyclical patterns
Earnings ImpactQuarterly effects
Sector RotationRelative performance

Long-Term (1+ Years)

FocusUse Case
Major TrendsSecular moves
52-Week RangeValuation context
Recovery/DeclineMajor shifts
Dividend YieldIncome analysis

Comparative Analysis

Peer Comparison

MetricWhat to Compare
ReturnRelative performance
VolatilityRisk comparison
CorrelationMovement similarity
VolumeLiquidity comparison

Benchmark Comparison

BenchmarkUsage
S&P 500Large cap reference
Sector ETFIndustry context
NasdaqTech comparison
Russell 2000Small cap reference

Analysis Tips

  1. Consider context: Market conditions affect individual stocks.
  2. Adjust for events: Earnings, dividends, splits affect prices.
  3. Use volume confirmation: Price moves need volume support.
  4. Multiple timeframes: Longer and shorter perspectives.
  5. Compare to peers: Relative performance matters.
  6. Watch key levels: Round numbers, 52-week highs/lows.

Use Cases

  • Trading analysis: Entry and exit timing
  • Performance tracking: Portfolio monitoring
  • Event analysis: Earnings, news impact
  • Volatility assessment: Risk evaluation
  • Peer comparison: Relative performance

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

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

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

Codex

37.27%
按下载量换算218

Claude

29.65%
按下载量换算173

Cursor

20.29%
按下载量换算119

Gemini CLI

8.61%
按下载量换算50

安全审计

Gen Agent Trust Hub

未通过

Socket

通过

Snyk

可疑

权限和风险

external-service

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

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。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。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

继续浏览同类 Skills