# Earnings Team

AI Berkshire skill: 财报精读团队：四大师并行解读 + 公众号发布.

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

## Description

AI Berkshire skill: 财报精读团队：四大师并行解读 + 公众号发布. Source: skills/earnings-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/earnings-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.

# 财报精读团队：四大师并行解读 + 公众号发布

对 $ARGUMENTS 进行团队化财报精读分析。四位大师并行解读财报，编辑润色成文，读者评审把关质量，最终产出可直接发布的公众号文章。

**支持输入格式**：`公司名 季度`，例如：`腾讯 2025Q4`、`PDD 2025年报`、`美团 最新`

## 设计理念

一份好的财报分析要解决两个问题：
1. **自己能看懂未来**, , 需要四个不同视角的深度研究
2. **读者能看懂价值**, , 需要编辑润色和读者视角的质量把关

本 Skill 的流程分三阶段：
- **阶段一·研究**：四大师并行精读财报（段永平看生意本质、巴菲特审财务质量、芒格读竞争变化、李录猎风险信号）
- **阶段二·合成**：Team Lead 综合四个视角，产出研究报告初稿
- **阶段三·发布**：编辑 Agent 改写为公众号文章 + 读者评审 Agent 提出修改意见 → Team Lead 定稿

---

## 阶段一：四大师并行研究

### 第一步：获取一手资料

使用 Agent 工具启动后台 Agent **并行**获取以下原始材料：

| 资料类型 | 获取来源 | 优先级 |
|---------|---------|--------|
| 财报原文 | 公司IR页面、SEC EDGAR（美股）、港交所披露易（港股）、巨潮资讯网（A股） | 最高 |
| 业绩电话会纪要 | Seeking Alpha、公司IR页面、雪球 | 最高 |
| 管理层致股东信 | 年报中提取 | 高（仅年报时） |
| 上一期财报/电话会 | 同上 | 高（用于承诺追踪） |

**资料可得性评级**：

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