# Portfolio Review

AI Berkshire skill: 组合管理：从\"研究公司\"到\"管理组合\".

- Repository: https://github.com/xbtlin/ai-berkshire (16972 stars)
- Category: Engineering
- License: MIT
- Last updated: 2026-11-01
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add xbtlin/ai-berkshire --skill portfolio-review -g -a claude-code -y`
Codex: `npx skills add xbtlin/ai-berkshire --skill portfolio-review -g -a codex -y`
Cursor: `npx skills add xbtlin/ai-berkshire --skill portfolio-review -g -a cursor -y`
Gemini CLI: `npx skills add xbtlin/ai-berkshire --skill portfolio-review -g -a gemini-cli -y`

## Description

AI Berkshire skill: 组合管理：从\"研究公司\"到\"管理组合\". Source: skills/portfolio-review.md.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

## Codex adapter note

This skill is generated from `skills/portfolio-review.md` so Claude Code and Codex users share one canonical workflow.

- Treat `$ARGUMENTS` as the user's request in the current Codex thread.
- When the source mentions Claude-only surfaces such as Task, Agent, WebSearch, Bash, Read, or Write, use the closest Codex capability available in this session: subagents when available, web search when needed, shell commands for local tools, and normal file edits for workspace files.
- Use shared project tools from `tools/` in this repository. Prefer running commands from the repository root with paths like `python3 tools/financial_rigor.py ...`; if the current thread starts outside the repo, locate the actual checkout path first instead of assuming a fixed home-directory path.
- Before starting research, run the `date` command to confirm today's date; treat it as the baseline for "latest" data and state the data cutoff date in the report header. Never assume the current date from training data.
- Preserve the research quality rules from `AGENTS.md`: cross-check financial data, use exact arithmetic tools for valuation/math, and clearly label uncertainty and source gaps.

# 组合管理：从"研究公司"到"管理组合"

对 $ARGUMENTS 执行投资组合审视与优化。

**支持输入格式**：
- 持仓清单，例如：`腾讯30%, 美团20%, 茅台20%, 英伟达15%, 现金15%`
- 或：`腾讯 500股 @480港元, 美团 1000股 @130港元, ...`
- 或：`我的持仓`（如果已有保存的组合文件 `reports/portfolio-latest.md`）

> "分散投资是对无知的保护。如果你知道自己在做什么，分散投资就没有意义。", , 巴菲特
>
> "我这辈子见过的真正好的投资机会，十个手指就数得完。", , 李录

## 设计理念

研究公司只是投资的一半。另一半是**组合层面的决策**：
- 买多少？（仓位）
- 用什么钱买？（资金来源, , 新钱还是换仓）
- 和已有持仓是否冲突？（相关性）
- 最优组合长什么样？（机会成本）

巴菲特从不孤立地看一只股票, , 他总是在想"这是不是我能做的最好的事？"

## 执行流程

### 第一步：解析持仓

从输入中解析出当前持仓，标准化为以下格式：

| 标的 | 代码 | 持仓量 | 成本价 | 现价 | 市值 | 占比 | 盈亏 |
|------|------|--------|-------|------|------|------|------|

如果输入只有比例没有金额，按比例分析即可。

同时检查是否存在已有的组合文件（`reports/portfolio-latest.md`），如有则读取并更新。

### 第二步：获取最新数据

The rest: https://github.com/xbtlin/ai-berkshire/blob/HEAD/codex-skills/portfolio-review/SKILL.md