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/financial-dataWrote 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/financial-data)<a href="https://agentmods.dev/skills/xbtlin/ai-berkshire/financial-data"><img src="https://agentmods.dev/badge/skills/xbtlin/ai-berkshire/financial-data/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/financial-data"><img src="https://agentmods.dev/badge/skills/xbtlin/ai-berkshire/financial-data.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 | $0.00025 | $0.02506 |
| Opus 5 | $0.00013 | $0.01253 |
| Sonnet 5 | $0.00005 | $0.00501 |
| Haiku 4.5 | $0.00003 | $0.00251 |
Grade A, and why
financial-data 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 — 168 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/financial-data.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.
财务数据获取与交叉验证规范
本规范适用于所有涉及企业财务数据的研究。每个关键数据必须来自两个独立来源，误差>1%须标记。
数据源优先级
美股（PDD、腾讯ADR、网易ADR等）
| 优先级 | 来源 | URL | 获取方式 |
|---|---|---|---|
| 1（主） | macrotrends | macrotrends.net/stocks/charts/{ticker} | 直接访问，无需注册 |
| 2（副） | stockanalysis | stockanalysis.com/stocks/{ticker}/financials | 直接访问，无需注册 |
| 原始一手 | SEC EDGAR | sec.gov/cgi-bin/browse-edgar | 10-K / 10-Q 原文 |
港股（腾讯0700、网易9999、美团3690等）
| 优先级 | 来源 | URL | 获取方式 |
|---|---|---|---|
| 1（主） | aastocks | aastocks.com/tc/stocks/analysis/company-fundamental | 直接访问 |
| 2（副） | macrotrends（ADR代码） | 腾讯用TCEHY，网易用NTES | 直接访问 |
| 原始一手 | HKEX披露易 | hkexnews.hk | 年报PDF |
A股（三七互娱、吉比特等）
| 优先级 | 来源 | URL | 获取方式 |
|---|---|---|---|
| 1（主） | 东方财富 | eastmoney.com → 搜股票代码 → 财务报表 | 直接访问 |
| 2（副） | 巨潮资讯 | cninfo.com.cn | 原始年报/季报PDF |
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 · 168 lines · 25 tokens per session scan A fb051c5b6026
financial-data is a skill published in the GitHub repository xbtlin/ai-berkshire (16,273 stars, last pushed 3d ago), licensed MIT. It adds 25 tokens to every session and 2,506 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.
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