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-reviewWrote 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-review)<a href="https://agentmods.dev/skills/xbtlin/ai-berkshire/earnings-review"><img src="https://agentmods.dev/badge/skills/xbtlin/ai-berkshire/earnings-review/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-review"><img src="https://agentmods.dev/badge/skills/xbtlin/ai-berkshire/earnings-review.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 | $0.00030 | $0.02907 |
| Opus 5 | $0.00015 | $0.01453 |
| Sonnet 5 | $0.00006 | $0.00581 |
| Haiku 4.5 | $0.00003 | $0.00291 |
Grade A, and why
earnings-review 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 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.
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 — 234 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-review.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：一手资料深度解读
对 $ARGUMENTS 进行财报精读分析。
支持输入格式：公司名 季度，例如：腾讯 2025Q4、PDD 2025年报、美团 最新（默认读取最近一期）
"我从不看卖方研报，只读原始财报。" —— 李录
"我每天读500页。知识就是这样积累的，像复利一样。" —— 巴菲特
设计理念
大多数AI投研工具依赖二手信息（新闻、研报摘要、数据网站）。但巴菲特和李录的核心能力是读一手资料——年报、季报、电话会纪要。
二手信息的问题：
- 被筛选过——分析师选择性呈现对其观点有利的数据
- 有时滞——等别人消化完，alpha已经没了
- 缺乏语境——"收入增长15%"脱离了管理层对增长质量的讨论
本Skill直接解读一手资料，关注巴菲特和李录真正会看的内容。
执行流程
前置步骤：资料可得性评级
| 等级 | 特征 | 影响 |
|---|---|---|
| A级 | 获取到完整原文（10-K/年报/电话会纪要） | 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 | 聚焦核心财务数据变化，跳过附注挖掘，标注"一手资料不足" |
第一步：获取一手资料
使用 Task 工具启动多个后台 Agent 并行获取以下原始材料：
- 财报原文：从公司IR页面、SEC EDGAR（美股10-K/10-Q）、港交所披露易（港股）、巨潮资讯网（A股）获取
- 业绩电话会纪要/录音：从 Seeking Alpha、公司IR页面、雪球等获取
- 管理层致股东信（如有年报）：完整阅读
- 投资者日/分析师日材料（如近期有）
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 · 234 lines · 30 tokens per session scan A 2a5b59925606
earnings-review is a skill published in the GitHub repository xbtlin/ai-berkshire (16,289 stars, last pushed today), licensed MIT. It adds 30 tokens to every session and 2,907 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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