# Quality Screen

AI Berkshire skill: 去劣筛选：7条指标快速排除非一流公司.

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

## Description

AI Berkshire skill: 去劣筛选：7条指标快速排除非一流公司. Source: skills/quality-screen.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/quality-screen.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.

# 去劣筛选：7条指标快速排除非一流公司

对 $ARGUMENTS 执行去劣指标筛选，快速排除不符合一流公司标准的标的。

**支持输入格式**：

| 输入方式 | 示例 | 说明 |
|---------|------|------|
| 个股 | `腾讯, 美团, 英伟达` | 逐家筛选 |
| 行业 | `中国啤酒行业` `全球云计算` `港股运动品牌` | 先搜索该行业主要上市公司（10-20家），再逐家筛选 |
| 市场/指数 | `恒生指数成分股` `沪深300` `纳斯达克100` | 拉取成分股列表，逐家筛选 |
| 主题 | `中国高股息50强` `全球AI算力链` | 先搜索主题相关公司，再逐家筛选 |

行业/市场/主题模式下，输出额外包含：通过率统计、行业内排名、板块对比总结。

## 设计原则

- **目标**：不错杀任何一流好公司，但能排除确定的非一流公司
- **逻辑**：7条硬指标 + 2条豁免规则，宁可漏网不可误杀
- **适用范围**：所有上市公司（银行/保险不适用第3条利息覆盖倍数）

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## 7条去劣指标

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