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/industry-funnelWrote 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/industry-funnel)<a href="https://agentmods.dev/skills/xbtlin/ai-berkshire/industry-funnel"><img src="https://agentmods.dev/badge/skills/xbtlin/ai-berkshire/industry-funnel/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/industry-funnel"><img src="https://agentmods.dev/badge/skills/xbtlin/ai-berkshire/industry-funnel.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 | $0.00037 | $0.03484 |
| Opus 5 | $0.00018 | $0.01742 |
| Sonnet 5 | $0.00007 | $0.00697 |
| Haiku 4.5 | $0.00004 | $0.00348 |
Grade A, and why
industry-funnel 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 — 321 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/industry-funnel.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.
行业漏斗筛选：从全市场到 3 家的价值投资精选流程
对 $ARGUMENTS 行业/方向执行漏斗式价值投资筛选，从全市场扫描逐层精选到 3 家终选标的。
适用场景
当你说出一个行业或投资方向（如"AI 算力"、"创新药"、"机器人"），想要：
- 不遗漏任何重要标的（含 A 股、港股、美股、未上市候选）
- 用统一标准过滤掉"故事股"和质量不足的公司
- 把精力聚焦到真正值得深度研究的 3 家头部
- 每层有明确的留/弃标准，可复盘可追溯
与 industry-research 的区别：
industry-research偏重产业链结构与全景，环节切片industry-funnel偏重个股筛选漏斗，从全市场逐层精选到 3 家
两者可以互补：先用 industry-research 看清产业链格局，再用 industry-funnel 精选标的。
漏斗结构总览
第一层：全市场扫描 30-60 家 （活跃度+涨幅+市值前 30 的并集）
↓ 价值投资 5 条硬指标
第二层：粗筛 ≤ 10 家 （5 条全部及格 + 护城河 ★★★ 以上）
↓ 精细分析
第三层：精细分析 ≤ 10 家 （每家 300-500 字结构化分析）
↓ 终选
第四层：四大师深度分析 3 家 （每家 800-1200 字，巴芒段李四视角）
↓
输出：投资建议 + 操作信号 + 仓位建议
每层"过滤掉的标的"必须留下淘汰理由，不能黑箱。
第一步：全市场扫描入口
1.1 活跃股票定义（三类并集）
A 类 - 成交活跃度：
- 近 30 天日均成交额排名行业前列（A 股/港股/美股各自取前 30）
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 · 321 lines · 37 tokens per session scan A 042cd59b274f
industry-funnel is a skill published in the GitHub repository xbtlin/ai-berkshire (16,289 stars, last pushed today), licensed MIT. It adds 37 tokens to every session and 3,484 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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