
Investment Checklist
AI Berkshire skill: 巴菲特价值投资买入前 Checklist.
Install
npx skills add xbtlin/ai-berkshire --skill investment-checklist -g -a claude-code -y- Repository
- xbtlin/ai-berkshire17k
- Category
- Engineering
- Updated
- 3d ago
- License
- MIT
- Context cost
- Lightabout 1,444 tokens
- Runs scripts
- No, instructions only
What it does
AI Berkshire skill: 巴菲特价值投资买入前 Checklist. Source: skills/investment-checklist.md.
Codex adapter note
This skill is generated from skills/investment-checklist.md so Claude Code and Codex users share one canonical workflow.
- Treat
$ARGUMENTSas 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 likepython3 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
datecommand 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。
