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/earnings-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/earnings-team)<a href="https://agentmods.dev/skills/xbtlin/ai-berkshire/earnings-team"><img src="https://agentmods.dev/badge/skills/xbtlin/ai-berkshire/earnings-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/earnings-team"><img src="https://agentmods.dev/badge/skills/xbtlin/ai-berkshire/earnings-team.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 | $0.00034 | $0.05188 |
| Opus 5 | $0.00017 | $0.02594 |
| Sonnet 5 | $0.00007 | $0.01038 |
| Haiku 4.5 | $0.00003 | $0.00519 |
Grade A, and why
earnings-team 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 — 460 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/earnings-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.
财报精读团队：四大师并行解读 + 公众号发布
对 $ARGUMENTS 进行团队化财报精读分析。四位大师并行解读财报，编辑润色成文，读者评审把关质量，最终产出可直接发布的公众号文章。
支持输入格式：公司名 季度，例如：腾讯 2025Q4、PDD 2025年报、美团 最新
设计理念
一份好的财报分析要解决两个问题：
- 自己能看懂未来——需要四个不同视角的深度研究
- 读者能看懂价值——需要编辑润色和读者视角的质量把关
本 Skill 的流程分三阶段：
- 阶段一·研究：四大师并行精读财报（段永平看生意本质、巴菲特审财务质量、芒格读竞争变化、李录猎风险信号）
- 阶段二·合成：Team Lead 综合四个视角，产出研究报告初稿
- 阶段三·发布：编辑 Agent 改写为公众号文章 + 读者评审 Agent 提出修改意见 → Team Lead 定稿
阶段一：四大师并行研究
第一步：获取一手资料
使用 Agent 工具启动后台 Agent 并行获取以下原始材料：
| 资料类型 | 获取来源 | 优先级 |
|---|---|---|
| 财报原文 | 公司IR页面、SEC EDGAR（美股）、港交所披露易（港股）、巨潮资讯网（A股） | 最高 |
| 业绩电话会纪要 | Seeking Alpha、公司IR页面、雪球 | 最高 |
| 管理层致股东信 | 年报中提取 | 高（仅年报时） |
| 上一期财报/电话会 | 同上 | 高（用于承诺追踪） |
资料可得性评级：
| 等级 | 特征 | 影响 |
|---|---|---|
| A级 | 获取到完整原文 | 正常执行全部步骤 |
| B级 | 仅获取到部分原文或第三方汇总 | 标注"非原始来源"，降低附注分析权重 |
| C级 | 仅有新闻报道和数据网站摘要 | 聚焦核心数据变化，跳过附注挖掘，标注"一手资料不足" |
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 · 460 lines · 34 tokens per session scan A 959406ac222b
earnings-team is a skill published in the GitHub repository xbtlin/ai-berkshire (16,289 stars, last pushed today), licensed MIT. It adds 34 tokens to every session and 5,188 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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