# Investment Team

AI Berkshire skill: 投研团队：四角色并行分析框架.

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

## Install

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

## Description

AI Berkshire skill: 投研团队：四角色并行分析框架. Source: skills/investment-team.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/investment-team.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.

# 投研团队：四角色并行分析框架

对 $ARGUMENTS 进行团队化投资研究分析。使用 Team 工具创建真正的多Agent并行研究团队。

## 执行流程

### 第一步：展示团队框架

向用户展示以下团队结构，确认后启动：

| 角色 | 职责 | 分析框架 |
|------|------|----------|
| **team-lead**（你自己） | 统筹协调、汇总研判、输出最终报告 | 四大师综合框架 |
| **business-analyst** | 商业模式 & 护城河分析 | 段永平视角 |
| **financial-analyst** | 财务报表 & 估值分析 | 巴菲特视角 |
| **industry-researcher** | 行业格局 & 竞争态势 | 芒格视角 |
| **risk-assessor** | 风险评估 & 管理层研判 | 李录视角 |

### 第一步半：AI研究偏见评估

在创建团队前，先向用户展示该公司的"AI可研究性"评估：

**信息丰富度评级**（决定研究策略）：
| 等级 | 特征 | 研究策略调整 |
|------|------|------------|
| A级（信息充裕） | 上市多年、券商覆盖广 | 团队重点放在**反面检验**和**非共识视角**，避免输出与市场一致的"正确的废话" |
| B级（信息适中） | 上市不久、覆盖有限 | 每个Agent的推算数据必须标注置信度，team-lead汇总时标注"数据充分度" |
| C级（信息稀缺） | 冷门/新上市/新兴市场 | 团队转为"第一性原理模式"：不追求报告完整性，聚焦商业本质的几个核心问题 |

The rest: https://github.com/xbtlin/ai-berkshire/blob/HEAD/codex-skills/investment-team/SKILL.md