# Era Alpha

AI Berkshire skill: 时代α捕手：高增长核心资产的识别-验证-持有框架.

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

## Description

AI Berkshire skill: 时代α捕手：高增长核心资产的识别-验证-持有框架. Source: skills/era-alpha.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/era-alpha.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.

# 时代α捕手：高增长核心资产的识别-验证-持有框架

对 $ARGUMENTS 行业/方向执行"时代α四步法"：建立行业认知地图 → 自问核心问题 → 全方位验证 → 持有到拐点。目标是找出当下最核心高增长行业中真正有定价权、有壁垒、能持续跑赢同行的 α 企业，并给出介入与退出纪律。

## 方法论来源与本质

源自一套职业投资人的四步操作手册，本质一句话：**把财富建立在认知之上，而不是运气或情绪之上**。原版四步（读一年财报建地图 → 自问核心高增长行业与核心α → 财报+调研+行业+宏观全验证后介入 → 基本面拐点前死拿）是职业选手的修炼路径，本技能内置了三项修正，使其成为可执行路径：

1. **修正一（精简范围）**：不覆盖所有行业，聚焦指定赛道的 2-3 个核心环节做深做透。三五家真正看透，胜过认识一千家。
2. **修正二（高频数据交叉验证）**：财报是三个月前的体检报告，必须用行业高频数据（周度/月度出货量、价格、订单、装机、渗透率）做实时体温计校正。
3. **修正三（估值锚点）**：长期看"高了还能更高"，但市盈率超历史均值 3 个标准差时介入可能长期输时间。合理或低估时重仓、明显泡沫时减仓、拐点确认时清仓，不闭眼买。

与现有技能的分工：
- `industry-research` 偏产业链全景切片；`industry-funnel` 偏全市场漏斗筛选
- `era-alpha` 偏"时代级高增长主线"的 α 识别 + 增长可持续性验证 + 持有/退出纪律，聚焦更窄、验证更深、给出明确的拐点清单

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## 第一步：行业认知地图（原版"读365份财报"的聚焦版）

对目标赛道建立产业链认知地图，每个环节回答：

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