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montecarlomontecarlo 命令行

Agent Skill

montecarlo 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。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

784

周安装

33

GitHub Stars

302

下载量

275
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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

3

许可证

MIT

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:montecarlo(montecarlo 命令行)
来源仓库:https://github.com/aojdevstudio/finance-guru
仓库路径:skills/montecarlo
安装命令:
npx skills add https://github.com/aojdevstudio/finance-guru --skill MonteCarlo
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。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/aojdevstudio/finance-guru --skill MonteCarlo

简介

montecarlo 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中整理仓库状态和协作事项。

  • 适用于围绕代码变更、仓库状态或协作流程进行信息处理的场景。
  • 通过 npx skills add 命令从指定 GitHub 仓库安装并使用。
  • 安装前需确认权限范围和维护状态,注意可能触发联网或文件读写操作。
  • 建议结合原始 README 和仓库内容进一步核验具体功能和使用方法。

SKILL.md

MonteCarlo

Monte Carlo simulation engine for Finance Guru's 4-layer dividend income + margin living strategy. Runs 10,000 market scenarios to project income probabilities, margin safety, and portfolio outcomes over 28 months.

Workflow Routing

WorkflowTriggerFile
RunSimulation"run monte carlo", "simulate portfolio", "stress test"workflows/RunSimulation.md
IncorporateBuyTicket"include buy ticket", "add ticket to simulation"workflows/IncorporateBuyTicket.md

Examples

Example 1: Run standard Monte Carlo simulation

User: "Run the monte carlo simulation with current portfolio"
-> Invokes RunSimulation workflow
-> Auto-detects portfolio values from notebooks/updates/Portfolio_Positions_*.csv
-> Runs 10,000 scenarios with v3.0 4-layer model
-> Outputs JSON summary + full CSV + Excel to fin-guru-private/fin-guru/analysis/

Example 2: Incorporate a buy ticket into simulation

User: "Run monte carlo with my new buy ticket from 12-31"
-> Invokes IncorporateBuyTicket workflow
-> Reads buy ticket from fin-guru-private/fin-guru/tickets/buy-ticket-2025-12-31-*.md
-> Parses YAML frontmatter + Execution Summary table from the canonical ticket format
-> Adjusts starting portfolio values based on ticket allocations
-> Runs simulation with updated positions

Example 3: Stress test margin safety

User: "What's my margin call probability?"
-> Invokes RunSimulation workflow
-> Focuses on margin_call_rate and margin_ratio metrics
-> Reports 5th percentile (worst case) margin ratio

Key Metrics Produced

Success Metrics

  • P($100k income) - Probability of reaching $100k annual dividend income
  • P($75k income) - Probability of reaching $75k annual dividend income
  • P($50k income) - Probability of reaching $50k annual dividend income
  • Margin call rate - % of scenarios triggering margin call (<3:1 ratio)
  • Backstop usage rate - % of scenarios requiring business income injection

Portfolio Metrics

  • Total portfolio value - Median, P5, P95 at month 28
  • Layer 1 (Growth) - PLTR, TSLA, VOO, etc. (no new deployment)
  • Layer 2 (Income) - Dividend funds ($11,517/month deployment)
  • Layer 3 (Hedge) - SQQQ ($800/month deployment)
  • GOOGL position - Scale-in ($1,000/month deployment)

Risk Metrics

  • Margin ratio - Portfolio / Margin debt (must stay >3:1)
  • Max drawdown - Worst peak-to-trough decline
  • Break-even timing - When dividends cover margin draws

Output Files

All outputs saved to fin-guru-private/fin-guru/analysis/:

  • monte-carlo-v3-{date}.json - Summary statistics
  • monte-carlo-v3-full-results-{date}.csv - All 10,000 scenarios
  • monte-carlo-v3-analysis-{date}.xlsx - Excel workbook with charts

Configuration

Simulation parameters are set in fin-guru-private/strategies/dividend_margin_monte_carlo.py:

  • Starting portfolio values (auto-detected or manual)
  • Monthly deployment amounts
  • Bucket allocations and yields
  • Margin schedule
  • Market regime probabilities

Model Version

v3.0 (Jan 2026) - Full 4-layer portfolio:

  • Layer 1: Growth portfolio (market returns only, no new deployment)
  • Layer 2: Income portfolio (5-bucket dividend allocation)
  • Layer 3: Hedge (SQQQ for crisis protection)
  • GOOGL: Scale-in position (diverted from Layer 2)

Fixes applied:

  • Floor at $0 for all positions (stocks can't go negative)
  • Full portfolio margin ratio (all layers count toward Fidelity margin)
  • Correct starting values from Fidelity CSV

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

04

需要参考平台分布和安装热度时

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

Claude Code

26.63%
按下载量换算73

windsurf

24.68%
按下载量换算68

trae

18.66%
按下载量换算51

OpenCode

12.56%
按下载量换算35

Codex

7.91%
按下载量换算22

Antigravity

3.41%
按下载量换算9

安全审计

Gen Agent Trust Hub

未通过

Socket

通过

Snyk

通过

权限和风险

权限需确认

当前来源未能明确判断权限范围,默认进入异常复核队列。

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。

来源信息

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