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-researchWrote 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-research)<a href="https://agentmods.dev/skills/xbtlin/ai-berkshire/investment-research"><img src="https://agentmods.dev/badge/skills/xbtlin/ai-berkshire/investment-research/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-research"><img src="https://agentmods.dev/badge/skills/xbtlin/ai-berkshire/investment-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk warn
- 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 | $0.00043 | $0.05242 |
| Opus 5 | $0.00022 | $0.02621 |
| Sonnet 5 | $0.00009 | $0.01048 |
| Haiku 4.5 | $0.00004 | $0.00524 |
Grade A, and why
investment-research 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 — 330 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-research.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.
投资研究：巴菲特-芒格-段永平-李录 四大师综合分析框架
对 $ARGUMENTS 进行系统化投资研究分析。
研究框架
基于巴菲特、芒格、段永平、李录四位投资大师的方法论，按以下七个模块顺序执行研究：
前置步骤：AI研究偏见自觉（必须执行）
在开始研究前，先评估该公司的"AI可研究性"，识别潜在的数据偏见：
信息丰富度评级：
| 等级 | 特征 | AI研究陷阱 | 应对策略 |
|---|---|---|---|
| A级（信息充裕） | 上市多年、券商覆盖多、媒体报道密集 | 共识过强，AI输出趋同于市场定价，alpha有限 | 重点做反面检验：聪明人为什么不买？被忽略的风险是什么？ |
| B级（信息适中） | 上市1-3年、覆盖有限、部分数据需推算 | AI可能用"合理推测"填补空白，看起来完整实则虚假确定性 | 每个推算数据标注置信度，区分"有据推算"和"凭空填充" |
| C级（信息稀缺） | 刚上市/冷门股/新兴市场、几乎无覆盖 | AI会因资料不足而过度保守，误判为"看不清=不好" | 用第一性原理提问（见下方），从有限信息中提取商业本质 |
C级公司的第一性原理研究法： 当公开资料不足时，不要试图拼凑出"看起来完整"的报告，而是聚焦以下底层问题：
- 客户是谁？为什么付钱？有没有替代选择？
- 复购靠什么驱动？是习惯、锁定、还是持续创造新价值？
- 竞争对手拿100亿能复制这门生意吗？
- 管理层做过什么关键决策？这些决策反映了什么判断力和价值观？
偏见自查清单（研究全程保持警惕）：
- 我的"确定性"感受是来自生意本质，还是来自资料数量？
- 如果把这家公司的资料量减少一半，我的结论会变吗？
- AI输出的分析是否与市场共识高度雷同？如果是，我的信息优势在哪？
- 是否存在"公开资料很少但生意本质极好"的可能性被低估了？
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 · 330 lines · 43 tokens per session scan A e5bc942d6d17
investment-research is a skill published in the GitHub repository xbtlin/ai-berkshire (16,289 stars, last pushed today), licensed MIT. It adds 43 tokens to every session and 5,242 once invoked, about $0.0002 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.
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