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-teamWrote 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-team)<a href="https://agentmods.dev/skills/xbtlin/ai-berkshire/investment-team"><img src="https://agentmods.dev/badge/skills/xbtlin/ai-berkshire/investment-team/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-team"><img src="https://agentmods.dev/badge/skills/xbtlin/ai-berkshire/investment-team.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Agent Snooping · line 58 Skill reads from agent configuration directories (.claude/, .codex/, .gemini/). These directories may contain API keys, personal settings, and other credentials that the skill has no legitimate need to access.Fix: Remove all code or instructions that access agent configuration directories (.claude/, .codex/, .gemini/). If configuration values are needed, pass them explicitly as parameters or environment variabl
- high Agent Snooping · line 58 Skill reads from agent configuration directories (.claude/, .codex/, .gemini/). These directories may contain API keys, personal settings, and other credentials that the skill has no legitimate need to access.Fix: Remove all code or instructions that access agent configuration directories (.claude/, .codex/, .gemini/). If configuration values are needed, pass them explicitly as parameters or environment variabl
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 | $0.03479 |
| Opus 5 | $0.00014 | $0.01740 |
| Sonnet 5 | $0.00006 | $0.00696 |
| Haiku 4.5 | $0.00003 | $0.00348 |
Grade B, and why
investment-team scanned grade B with 1 finding 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.
Reads agent configuration directoriesmediumAgent snooping
.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.
grep -l '"WebSearch"' .claude/settings.local.json ~/.claude/settings.local.json 2>/dev/null How it starts
The opening of the file, as written. The whole thing — 245 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-team.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.
投研团队：四角色并行分析框架
对 $ARGUMENTS 进行团队化投资研究分析。使用 Team 工具创建真正的多Agent并行研究团队。
执行流程
第一步：展示团队框架
向用户展示以下团队结构，确认后启动：
| 角色 | 职责 | 分析框架 |
|---|---|---|
| team-lead（你自己） | 统筹协调、汇总研判、输出最终报告 | 四大师综合框架 |
| business-analyst | 商业模式 & 护城河分析 | 段永平视角 |
| financial-analyst | 财务报表 & 估值分析 | 巴菲特视角 |
| industry-researcher | 行业格局 & 竞争态势 | 芒格视角 |
| risk-assessor | 风险评估 & 管理层研判 | 李录视角 |
第一步半：AI研究偏见评估
在创建团队前，先向用户展示该公司的"AI可研究性"评估：
信息丰富度评级（决定研究策略）：
| 等级 | 特征 | 研究策略调整 |
|---|---|---|
| A级（信息充裕） | 上市多年、券商覆盖广 | 团队重点放在反面检验和非共识视角，避免输出与市场一致的"正确的废话" |
| B级（信息适中） | 上市不久、覆盖有限 | 每个Agent的推算数据必须标注置信度，team-lead汇总时标注"数据充分度" |
| C级（信息稀缺） | 冷门/新上市/新兴市场 | 团队转为"第一性原理模式"：不追求报告完整性，聚焦商业本质的几个核心问题 |
关键提醒：资料多≠确定性高，资料少≠确定性低。AI能输出的置信度 ≠ 投资的真实确定性。确定性来自商业模式本身，不来自资料数量。
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 · 245 lines · 28 tokens per session scan B 4b5060622c32
investment-team is a skill published in the GitHub repository xbtlin/ai-berkshire (16,289 stars, last pushed today), licensed MIT. It adds 28 tokens to every session and 3,479 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it B with 1 finding (reads agent configuration directories). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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