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 private-company-researchgit 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/private-company-research)<a href="https://agentmods.dev/skills/xbtlin/ai-berkshire/private-company-research"><img src="https://agentmods.dev/badge/skills/xbtlin/ai-berkshire/private-company-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/private-company-research"><img src="https://agentmods.dev/badge/skills/xbtlin/ai-berkshire/private-company-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 | $0.00033 | $0.13618 |
| Opus 5 | $0.00016 | $0.06809 |
| Sonnet 5 | $0.00007 | $0.02724 |
| Haiku 4.5 | $0.00003 | $0.01362 |
Grade A, and why
private-company-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 12d 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 — 1,087 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/private-company-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.
未上市公司研究：多Agent并行深度研究框架
对 $ARGUMENTS 进行团队化深度研究分析。专为蚂蚁集团、小红书、SpaceX、Stripe 等未上市公司设计。
最终目标：在信息天然稀缺的条件下，尽可能还原这家公司的真实价值——不是市场给的估值，而是生意本身值多少钱。
框架特点
未上市公司 vs 上市公司研究的核心差异：
- 无标准化财报：需多源拼凑、交叉验证
- 估值锚定少：依赖融资轮次、可比公司法、情景推演
- 信息不对称大：需要更多"拼图式"研究方法
- 退出路径不确定：IPO/并购/二级转让均有可能
AI研究偏见自觉（本框架的核心前提）
未上市公司是AI研究偏见最严重的领域。必须时刻警惕以下陷阱：
核心矛盾：AI擅长结构化已有信息，但未上市公司的信息天然稀缺。这会导致：
- 虚假保守：因为资料少，AI倾向给出保守/模糊的结论，但资料少≠公司不好
- 虚假精确：为了填满报告模板，AI可能用"合理推测"伪装成"有据分析"
- 对标陷阱：强行与上市公司对标时，会继承上市公司的估值逻辑，忽略未上市公司的独特价值
- 幸存者偏差：网上能搜到的公司信息往往有正面偏向（公司主动传播的多是好消息）
应对原则：
- 宁可留白说"不知道"，也不要用推测填满表格伪装确定性
- 每个数据点必须标注置信度（🟢高/🟡中/🔴低），让读者自己判断
- 区分"可验证的事实"与"AI的推理"，用不同格式标注
- 对于信息极度稀缺的公司，切换为"第一性原理模式"——不追求报告完整，只回答几个核心问题：
- 这门生意解决什么真实问题？需求是真需求还是伪需求？
- 为什么是这个团队？他们有什么独特优势？
- 如果成功了，天花板有多高？如果失败了，最可能死在哪里？
- 当前阶段的关键验证节点是什么？
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.
- 12d ago First seen · 1,087 lines · 33 tokens per session scan A 2b708ce05449
private-company-research is a skill published in the GitHub repository xbtlin/ai-berkshire (16,289 stars, last pushed today), licensed MIT. It adds 33 tokens to every session and 13,618 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.
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.