# Deep Company Series

Writes a multi-part long-form analysis of one company.

- Repository: https://github.com/xbtlin/ai-berkshire (16972 stars)
- Category: Writing
- License: MIT
- Last updated: 2026-11-01
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add xbtlin/ai-berkshire --skill deep-company-series -g -a claude-code -y`
Codex: `npx skills add xbtlin/ai-berkshire --skill deep-company-series -g -a codex -y`
Cursor: `npx skills add xbtlin/ai-berkshire --skill deep-company-series -g -a cursor -y`
Gemini CLI: `npx skills add xbtlin/ai-berkshire --skill deep-company-series -g -a gemini-cli -y`

## Description

AI Berkshire skill: 看懂XX公司（深度公司系列）：3-8 篇长文拆一家公司. Source: skills/deep-company-series.md.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

## Codex adapter note

This skill is generated from `skills/deep-company-series.md` so Claude Code and Codex users share one canonical workflow.

- Treat `$ARGUMENTS` as 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 like `python3 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 `date` command 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.

# 看懂XX公司（深度公司系列）：3-8 篇长文拆一家公司

为 $ARGUMENTS 撰写一个《看懂XX》深度长文系列（3-8 篇，按公司复杂度定，见"篇数适配"），发布在公众号/视频号等公开渠道。**核心 IP 不是"会写"，而是"会改", , 99% 的财经文章在违反本 skill 的事实核查标准**。

参考样本：`reports/腾讯/《看懂腾讯》/`

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## 一、触发场景

用户希望为一家公司做"教科书级别"的深度研究，并以**系列长文**形式公开发布。区别于一篇研报：
- 3-8 篇（复杂公司 8 篇约 12 万字，简单公司 3 篇约 3 万字），从认知重置到决策框架完整闭环
- 每篇独立成文（适合单篇分享），但贯穿一套估值/管理层/价格判断
- 写给"愿意花 90 分钟读懂一家公司"的读者，不是写给券商客户

**不适合用本 skill 的场景**：单篇研报、季报点评、行业研究, , 那些用 `/investment-research`、`/earnings-review`、`/industry-research`。

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## 二、系列篇目模板

### 篇数适配公司复杂度（先定篇数，再定篇目）

**篇数不固定为 8。** 8 篇是腾讯这种"多业务 + 万亿投资组合 + 20 年管理层故事"的公司才撑得起的容量。写之前先问：这家公司有几个能用"一个尖锐问题"独立成文的主轴？有几个写几篇。

The rest: https://github.com/xbtlin/ai-berkshire/blob/HEAD/codex-skills/deep-company-series/SKILL.md