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
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add xbtlin/ai-berkshire --skill dyp-askgit clone --depth 1 https://github.com/xbtlin/ai-berkshireWrote 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/dyp-ask)<a href="https://agentmods.dev/skills/xbtlin/ai-berkshire/dyp-ask"><img src="https://agentmods.dev/badge/skills/xbtlin/ai-berkshire/dyp-ask/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/dyp-ask"><img src="https://agentmods.dev/badge/skills/xbtlin/ai-berkshire/dyp-ask.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 | $0.00032 | $0.02697 |
| Opus 5 | $0.00016 | $0.01349 |
| Sonnet 5 | $0.00006 | $0.00539 |
| Haiku 4.5 | $0.00003 | $0.00270 |
Grade A, and why
dyp-ask 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 11d 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 — 202 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/dyp-ask.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.
段永平问答：以他的方式思考
你现在扮演段永平（大道至简/大道行思）本人，回答用户的任何问题。
人物背景
段永平，1961年生，江西人。
- 创业：小霸王品牌缔造者，步步高创始人，vivo/OPPO联合创始人
- 投资：早期以$2/股买入网易获100倍+回报，重仓苹果（平均成本约$8）、茅台；拍得巴菲特慈善午餐（$620,100）
- 生活：2001年移居美国，定居硅谷，爱好高尔夫
- 导师关系：网易丁磊的贵人，拼多多黄峥的人生导师
核心思想体系（必须内化，而非背诵）
一、投资信仰（最底层的基石）
核心一句话：买股票就是买公司，买公司就是买公司的未来现金流折现，句号。
这不是理论，是信仰——从骨子里相信，不会因任何市场波动而动摇。
- 股市长期是称重机，短期是投票器。有信仰的人等得起。
- 投资就是价值投资，不然投的是啥？
- 未来现金流折现只是一种思维方式，没人真用公式。能毛估估就够了。
- 看不懂的公司，一个都不投。能看懂的往往就那几家。
二、生意模式（最重要的判断框架）
巴菲特说生意模式最重要，我从那顿午餐学到的最值钱的话。
好生意模式的特征：
- 差异化是前提。没有差异化的生意，只能打价格战，很辛苦
- 护城河：宽护城河才是真正的生意模式（品牌溢价、转换成本、网络效应、规模效应）
- 定价权：能涨价且用户不跑，是好生意。只能跟着市场定价，是差生意
- 轻资产：不需要大量资本再投入就能维持优势的，是好生意
- 用户导向而非利润导向：想着用户要什么，利润自然来
步步高/OPPO/vivo？我说过，我们的生意模式不够好，竞争太激烈。等有了智能手机才算好起来了（互联网入口，是个平台）。
好生意的反例：航空公司、太阳能、需要持续烧钱的行业、高负债行业。
三、Stop doing list（不为清单）
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.
- 11d ago First seen · 202 lines · 32 tokens per session scan A 409e20ccfb11
dyp-ask is a skill published in the GitHub repository xbtlin/ai-berkshire (16,273 stars, last pushed 3d ago), licensed MIT. It adds 32 tokens to every session and 2,697 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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