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 wechat-articlegit 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/wechat-article)<a href="https://agentmods.dev/skills/xbtlin/ai-berkshire/wechat-article"><img src="https://agentmods.dev/badge/skills/xbtlin/ai-berkshire/wechat-article/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/wechat-article"><img src="https://agentmods.dev/badge/skills/xbtlin/ai-berkshire/wechat-article.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 | $0.00032 | $0.03155 |
| Opus 5 | $0.00016 | $0.01577 |
| Sonnet 5 | $0.00006 | $0.00631 |
| Haiku 4.5 | $0.00003 | $0.00315 |
Grade A, and why
wechat-article 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 10d 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 — 247 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/wechat-article.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.
微信公众号文章：作者-编辑-读者三Agent协作
对 $ARGUMENTS 进行深度研究，产出一篇可直接发布的微信公众号文章。三个Agent各司其职：作者写深度初稿，编辑精修结构和表达，读者从目标受众视角审读。
支持输入格式：主题描述，例如：大模型OPD技术解读、Qwen3技术报告解读、为什么巴菲特不买科技股
设计理念
一篇好的公众号文章需要同时满足三个维度：
- 深度——对得起花时间读完的人（作者负责）
- 可读性——结构清晰、节奏好、不劝退（编辑负责）
- 真的能看懂——目标读者不会在中途放弃（读者负责）
单人写作容易"自嗨"——写的人觉得清楚，读的人看不懂。三Agent协作的本质是强制引入外部视角。
阶段一：研究与素材收集
第一步：明确文章定位
在开始写作之前，先确认以下信息（如用户未指定则主动询问）：
| 维度 | 需要确认 | 默认值 |
|---|---|---|
| 目标读者 | 技术背景程度 | 有点技术背景但非该领域专家 |
| 文章深度 | 科普/中深度/硬核 | 中深度（有公式但要解释清楚） |
| 文章长度 | 字数范围 | 3000-4000字 |
| 是否需要下载原始论文/资料 | 需要PDF/配图 | 是 |
| 写作风格 | 正式/对话式/犀利 | 对话式（像写给聪明的朋友） |
第二步：深度研究
使用 Agent 工具并行启动2-3个研究Agent，收集足够的素材：
研究Agent A：核心内容研究
- 如果是论文解读：下载论文PDF、提取核心贡献、关键图表、实验结果
- 如果是技术主题：搜索最新进展、关键论文、技术细节
- 如果是商业/投资主题：搜索最新数据、行业报告、竞争格局
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.
- 10d ago First seen · 247 lines · 32 tokens per session scan A 6b769fffc2cb
wechat-article is a skill published in the GitHub repository xbtlin/ai-berkshire (16,258 stars, last pushed 2d ago), licensed MIT. It adds 32 tokens to every session and 3,155 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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