AI Berkshire is a collection of Claude Code and Codex skills that structures investment research around the methods of four value-investing thinkers and uses multiple agents for adversarial analysis. It is intended for investors who want a disciplined process for researching companies and making valuation-based decisions.
Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/xbtlin/ai-berkshirenpx agentmods add skills/xbtlin/ai-berkshire/investment-checklistWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/xbtlin/ai-berkshire/investment-checklist)<a href="https://agentmods.dev/skills/xbtlin/ai-berkshire/investment-checklist"><img src="https://agentmods.dev/badge/skills/xbtlin/ai-berkshire/investment-checklist/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/xbtlin/ai-berkshire/investment-checklist"><img src="https://agentmods.dev/badge/skills/xbtlin/ai-berkshire/investment-checklist.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00028 | $0.03166 |
| Opus 5 | $0.00014 | $0.01583 |
| Sonnet 5 | $0.00006 | $0.00633 |
| Haiku 4.5 | $0.00003 | $0.00317 |
Grade A, and why
investment-checklist scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 13d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 255 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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。
第二步：并行数据收集
使用 Task 工具为每家公司启动独立的后台 Agent 进行数据收集（所有公司同时并行启动），每个Agent负责收集：
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 13d ago First seen · 255 lines · 28 tokens per session scan A 109f9df7f726
investment-checklist is a skill published in the GitHub repository xbtlin/ai-berkshire (16,289 stars, last pushed yesterday), licensed MIT. It adds 28 tokens to every session and 3,166 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
dcf-model
Build discounted cash flow valuation workbooks in Excel.
comps-analysis
Build comparable-company valuation workbooks in Excel.
hyperliquid
Hyperliquid market data, account history, trade review.
stocks
Stock quotes, history, search, compare, crypto via Yahoo.
omni-budget
Configure spending limits, token quotas, and rate-limit policies per API key or globally. Inspect current consumption and enforce cost controls across providers.
polymarket
Query Polymarket: markets, prices, orderbooks, history.