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 era-alphagit 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/era-alpha)<a href="https://agentmods.dev/skills/xbtlin/ai-berkshire/era-alpha"><img src="https://agentmods.dev/badge/skills/xbtlin/ai-berkshire/era-alpha/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/era-alpha"><img src="https://agentmods.dev/badge/skills/xbtlin/ai-berkshire/era-alpha.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 | $0.00036 | $0.02206 |
| Opus 5 | $0.00018 | $0.01103 |
| Sonnet 5 | $0.00007 | $0.00441 |
| Haiku 4.5 | $0.00004 | $0.00221 |
Grade A, and why
era-alpha 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 — 112 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/era-alpha.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.
时代α捕手：高增长核心资产的识别-验证-持有框架
对 $ARGUMENTS 行业/方向执行"时代α四步法"：建立行业认知地图 → 自问核心问题 → 全方位验证 → 持有到拐点。目标是找出当下最核心高增长行业中真正有定价权、有壁垒、能持续跑赢同行的 α 企业，并给出介入与退出纪律。
方法论来源与本质
源自一套职业投资人的四步操作手册，本质一句话：把财富建立在认知之上，而不是运气或情绪之上。原版四步（读一年财报建地图 → 自问核心高增长行业与核心α → 财报+调研+行业+宏观全验证后介入 → 基本面拐点前死拿）是职业选手的修炼路径，本技能内置了三项修正，使其成为可执行路径：
- 修正一（精简范围）：不覆盖所有行业，聚焦指定赛道的 2-3 个核心环节做深做透。三五家真正看透，胜过认识一千家。
- 修正二（高频数据交叉验证）：财报是三个月前的体检报告，必须用行业高频数据（周度/月度出货量、价格、订单、装机、渗透率）做实时体温计校正。
- 修正三（估值锚点）：长期看"高了还能更高"，但市盈率超历史均值 3 个标准差时介入可能长期输时间。合理或低估时重仓、明显泡沫时减仓、拐点确认时清仓，不闭眼买。
与现有技能的分工：
industry-research偏产业链全景切片；industry-funnel偏全市场漏斗筛选era-alpha偏"时代级高增长主线"的 α 识别 + 增长可持续性验证 + 持有/退出纪律，聚焦更窄、验证更深、给出明确的拐点清单
第一步：行业认知地图（原版"读365份财报"的聚焦版）
对目标赛道建立产业链认知地图，每个环节回答：
- 这个环节处于什么阶段？（导入期/成长期/成熟期/衰退期，用渗透率和增速定位）
- 商业模式与赚钱方式？（毛利率、费用率、现金流与利润的匹配度）
- 竞争格局？（CR3、定价权在谁手里、壁垒是技术/规模/生态/牌照）
- 每个环节的 α 候选是谁？（营收增速、ROE 趋势、市占率变化三个维度筛）
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 · 112 lines · 36 tokens per session scan A 707e310a0a06
era-alpha is a skill published in the GitHub repository xbtlin/ai-berkshire (16,273 stars, last pushed 3d ago), licensed MIT. It adds 36 tokens to every session and 2,206 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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