# Investment Checklist

AI Berkshire skill: 巴菲特价值投资买入前 Checklist.

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

## Description

AI Berkshire skill: 巴菲特价值投资买入前 Checklist. Source: skills/investment-checklist.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/investment-checklist.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.

# 巴菲特价值投资买入前 Checklist

对 $ARGUMENTS 执行巴菲特价值投资买入前 Checklist 分析。

**支持输入格式**：单个或多个公司，用逗号/顿号/空格分隔。例如：`腾讯, 茅台, 英伟达` 或 `NVDA AAPL MSFT`

## 执行流程

### 第一步：解析输入，识别所有待分析公司

从 $ARGUMENTS 中解析出所有公司名称/代码。对每家公司确定：
- 公司全称、股票代码、上市交易所
- 如果公司未上市，标记为"未上市"并给出简要说明（是否有间接投资途径），跳过完整Checklist

### 第一步半：AI研究偏见预警

对每家公司进行"信息丰富度"快速评级（A/B/C），并在报告中标注：

| 等级 | 判断标准 | 对Checklist的影响 |
|------|---------|-----------------|
| A级 | 上市多年、数据充裕 | 正常执行，但警惕"共识陷阱", , 所有指标看起来都清晰不代表真的确定 |
| B级 | 数据有限需推算 | 每个推算指标标注置信度，"好生意"判断加权考虑数据可靠性 |
| C级 | 信息极度稀缺 | 不勉强填满六关表格，诚实标注"数据不足无法判断"，聚焦可验证的核心问题 |

**核心原则**：Checklist的目标是**排除坏选择**。对于C级公司，"数据不足"不等于"不通过"，也不等于"通过", , 应诚实标注为"灰色地带，需补充一手信息"，而不是因为AI无法填满表格就判为否决。

段永平说过："看不懂"有两种, , 一种是生意太复杂真的看不懂，一种是你还没花时间去看。AI研究的局限是容易把"资料少"和"看不懂"混为一谈。

### 第二步：并行数据收集

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