How the Quality-Screen Skill Filters Stocks in the AI-Berkshire Repository

The /quality-screen skill in the xbtlin/ai-berkshire repository applies seven hard financial metrics and three exemption rules to eliminate poor-quality companies, using parallel agents to scrape data from annual reports and broker research before generating Pass/Fail/Gray classifications.

The quality-screen skill functions as a deterministic “go-bad-companies-out” filter designed for rapid elimination of low-quality investment targets. Implemented in the xbtlin/ai-berkshire project, this command orchestrates multiple AI agents defined in skills/quality-screen.md to evaluate securities against rigorous quantitative standards.

Input Parsing and Scope Resolution

The skill begins execution in the Input Parser section of skills/quality-screen.md, where it detects the mode of the user’s $ARGUMENTS and normalizes each entry to a company name, ticker, and exchange.

For individual-stock mode, processing proceeds directly to data collection. For batch mode (industries, indices, or themes), the skill triggers a WebSearch Agent that searches the supplied market or sector, extracts the top 10-30 listed companies, and builds a consolidated watch-list. This logic is described under “第一步：解析输入，确定筛选范围” (Step 1: Parse Input, Determine Screening Scope) in the skill definition.

Parallel Data Collection Architecture

According to the architecture diagram in README_EN.md (lines 51-61), the Agent Layer spawns parallel background agents for each company. As specified under “第二步：并行数据收集” (Step 2: Parallel Data Collection), each agent scrapes seven critical data points with the following source priority: annual reports → broker research → financial data platforms.

The seven metrics collected are:

• 10-year average ROE (Return on Equity)
• 5-year cumulative Free Cash Flow (FCF)
• Interest-Coverage Ratio (EBIT/Interest)
• Long-term Gross-Margin
• Operating-Cash-Flow / Net-Profit (5-year average)
• Long-term Net-Margin
• 5-year Total-Equity-Dilution (flagged if >20% excluding M&A)

The Rule Engine and Exemption Processor

Located in skills/quality-screen.md lines 28-63, the Rule Engine implements a two-stage evaluation system.

Seven Hard Filters

The hard-filter table (lines 28-35) maps each metric to a reject threshold. Companies failing any single metric receive a ❌ flag immediately.

Three Exemption Rules (A-C)

Lines 36-63 define qualitative patterns allowing companies to bypass specific failures:

• Rule A: “Strategic-investment-phase” exemption for low ROE
• Rule B: “High-margin-but-low-profit-strategy” exemption for net-margin failures
• Rule C: “High-turnover-thin-margin” exemption covering both gross-margin and net-margin

The engine evaluates each metric (✅/❌/⚠️), checks applicable exemptions, and produces a final Pass/Fail/Gray status.

Result Generation and Reporting

The Result Formatter (lines 106-148 of skills/quality-screen.md) generates a markdown report containing:

• A summary table with all seven metrics and final results
• Segregated lists of passed, excluded, and exemption-approved companies
• Sector-level statistics including pass-rate and qualitative “quality-layer” groupings (in batch mode)

Usage Examples

Screening Individual Stocks

/quality-screen 腾讯, 美团, 英伟达

This command returns an evaluation table:

# 去劣筛选结果

## 汇总表

| 公司 | ①ROE | ②FCF | ③利息覆盖 | ④毛利率 | ⑤OCF/NI | ⑥净利率 | ⑦稀释 | 结果 |
|------|------|------|-----------|----------|----------|----------|--------|------|
| 腾讯 | ✅ 24% | ✅ | ✅ | ✅ 56% | ✅ | ✅ | ✅ 30% | ✅ 通过 |
| 美团 | ⚠️→✅ | ✅ | ✅ | ✅ 35% | ✅ | ✅ | ✅ | ✅ 豁免通过 |
| 英伟达 | ❌ 3% | ❌ | ❌ | ✅ 20% | ✅ | ❌ 2% | ✅ | ❌ 排除 |

Batch Screening Multiple Companies

/quality-screen 恒生指数成分股

Batch mode produces sector analysis:

## 板块总结

**通过率**：12/20 = 60%
**行业质量判断**：整体质量中等，需重点关注 ROE 低且无豁免的科技股。

## 通过的公司（12家）

[北京航空、腾讯、阿里巴巴, …]

## 排除的公司（8家）

| 公司 | 触犯指标 | 具体数据 | 排除理由 |
|------|----------|----------|----------|
| 某银行 | ③利息覆盖 | 1.5× | 银行不适用此指标 |
| 某REIT | ①ROE | -5% | ROE波动大 |

Implementation Files

The quality-screen skill relies on these key components:

• skills/quality-screen.md – Primary skill definition containing input parsing logic, the seven-metric table, exemption rules, and output templates
• README_EN.md (Architecture section, lines 51-61) – Documents the three-layer design (Skill → Agent → Tool) enabling parallel execution
• tools/financial_rigor.py – Underlying precision utilities for cross-validating financial data scraped by agents
• codex-skills/quality-screen/SKILL.md – Auto-generated Codex wrapper providing identical functionality for OpenAI Codex users

Summary

• The quality-screen skill operates as a deterministic filter using seven hard financial metrics and three contextual exemptions to remove poor-quality companies
• Parallel agent architecture in skills/quality-screen.md enables simultaneous data collection from annual reports, broker research, and financial platforms
• Batch mode leverages a WebSearch Agent to expand industry or index inputs into constituent watch-lists automatically
• Deterministic rule engine at lines 28-63 of the skill file produces Pass/Fail/Gray classifications with detailed exemption tracking
• Markdown reporting generates sector statistics and company-level breakdowns suitable for direct investment research workflows

Frequently Asked Questions

What are the seven specific metrics used in the quality-screen skill?

The skill evaluates 10-year average ROE, 5-year cumulative Free Cash Flow, Interest-Coverage Ratio, long-term Gross-Margin, Operating-Cash-Flow/Net-Profit ratio, long-term Net-Margin, and 5-year Total-Equity-Dilution. Each metric is scraped by parallel agents following a strict source hierarchy: annual reports first, then broker research, then financial platforms.

How does the exemption system work in the quality-screen skill?

Three exemption rules (A-C) defined in skills/quality-screen.md lines 36-63 allow companies to bypass specific metric failures if they meet qualitative patterns. For example, a company in a "strategic-investment-phase" may receive an exemption for low ROE (Rule A), while high-turnover businesses may bypass thin-margin thresholds (Rule C). The system flags these as ⚠️→✅ in the results table.

Can the quality-screen skill analyze entire stock indices or sectors?

Yes. When provided with an index name, industry, or theme (such as 恒生指数成分股), the skill activates batch mode. A WebSearch Agent identifies the top 10-30 listed companies in that category, builds a consolidated watch-list, and runs parallel screening across all constituents. The output includes sector-level pass rates and quality-layer groupings.

What is the role of the Agent Layer in the quality-screen implementation?

According to the architecture diagram in README_EN.md lines 51-61, the Agent Layer orchestrates the skill as a lightweight command that spawns parallel background agents. These agents handle data collection for each company simultaneously while tools/financial_rigor.py provides cross-validation utilities, ensuring both speed and reproducibility in the screening process.

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}