How to Use the Quality-Screen Skill with Hard Indicators for Stock Filtering

The quality-screen skill in the xbtlin/ai-berkshire repository applies seven non-negotiable financial thresholds—ROE, free cash flow, interest coverage, margins, cash conversion, and equity dilution—to automatically exclude low-quality stocks from investment consideration.

The quality-screen skill is a built-in AI Berkshire workflow that automates rigorous Buffett-style quality criteria through hard-coded financial filters. Located in the skills/quality-screen.md specification, this tool evaluates companies against first-class quality standards to eliminate poor investments before deeper analysis begins.

The Seven Hard Indicators

The skill defines seven "hard" financial metrics in skills/quality-screen.md that serve as binary pass/fail gates. Any company failing a single metric is immediately excluded unless an exemption rule applies.

• 10-year average ROE: Excludes companies below 8%, measuring capital efficiency and the ability to generate returns above the cost of equity.
• 5-year cumulative free cash flow: Excludes negative values, distinguishing real cash generation from accounting earnings.
• Interest-coverage ratio: Excludes ratios below 2× (EBIT ÷ Interest), assessing debt service capacity.
• Long-term gross margin: Excludes margins below 15%, indicating pricing power and product differentiation.
• Operating-cash-flow ÷ Net profit (5-year average): Excludes ratios below 0.7, verifying earnings quality and cash conversion.
• Long-term net profit margin: Excludes margins below 5%, testing resilience against revenue fluctuations.
• 5-year total equity dilution: Excludes dilution above 20% (excluding M&A), protecting shareholder interests from excessive share issuance.

How the Quality-Screen Skill Works

According to the implementation in codex-skills/quality-screen/SKILL.md, the workflow executes through three distinct stages:

Input Parsing

The skill determines the screening scope by parsing whether you provided a single ticker, an industry sector, an index constituent list, or a thematic universe.

Parallel Data Collection

A background agent retrieves required financial figures from company filings, broker research, and financial data platforms simultaneously.

Rule Evaluation

The system applies the "7+2" rule set—seven hard filters plus two exemption rules. Any failure on a hard metric triggers immediate exclusion unless an exemption applies (e.g., a strategic investment phase for a young, high-margin company). This design guarantees no false positives, preferring to miss potential winners rather than retain clear losers.

Invoking the Quality-Screen Command

The skill exposes a slash-command /quality-screen documented in README_EN.md. Provide comma-separated tickers for single-stock mode or natural-language phrases for batch screening:

/quality-screen 腾讯, 美团, 英伟达
/quality-screen 恒生指数成分股
/quality-screen 全球AI算力链
/quality-screen 中国啤酒行业

When processing indices or themes, the skill first searches for constituent lists (typically 10-30 top companies) then applies the hard-indicator filter to each.

Understanding the Output

The skill generates a markdown table showing for each ticker:

• Pass (✅) or fail (❌) status per indicator
• Numeric values used for the decision
• A brief "Reason" column explaining exclusion triggers
• Summary statistics including pass-rate and industry ranking

Example output for single-stock mode:

| 股票 | ROE (10 yr) | FCF 5yr | 利息覆盖 | 毛利率 | 运营/净利 | 净利率 | 股本膨胀 | 结论 |
|------|-------------|---------|----------|--------|-----------|--------|----------|------|
| 腾讯 | 12% ✅ | 45B ✅ | 3.8× ✅ | 43% ✅ | 1.2 ✅ | 25% ✅ | 7% ✅ | ✅ Pass |
| 美团 | 6% ❌ | 2B ✅ | 5.1× ✅ | 31% ✅ | 1.1 ✅ | 8% ✅ | 12% ✅ | ❌ Fail (ROE) |

The prompt wrapper in codex-prompts/quality-screen.md forwards requests to the skill engine, which returns the formatted results.

Summary

• The quality-screen skill applies seven hard financial thresholds defined in skills/quality-screen.md to filter stocks.
• Metrics include ROE (>8%), cumulative FCF (positive), interest coverage (>2×), gross margin (>15%), cash conversion (>0.7), net margin (>5%), and equity dilution (<20%).
• The "7+2" rule set ensures non-negotiable exclusion unless exemption criteria apply, eliminating false positives.
• Invoke via /quality-screen with tickers, indices, or thematic phrases.
• Source files include skills/quality-screen.md, codex-skills/quality-screen/SKILL.md, and codex-prompts/quality-screen.md.

Frequently Asked Questions

What happens if a company fails only one hard indicator?

The company is immediately excluded from the pass list unless it qualifies for one of the two exemption rules. The quality-screen skill treats the seven hard indicators as non-negotiable gates according to the specification in skills/quality-screen.md.

Can I adjust the threshold values for the hard indicators?

No. The thresholds are hard-coded in the skill specification to maintain consistency with first-class quality standards. The design intentionally prevents customization to avoid diluting the screening criteria.

Where does the financial data come from?

The skill uses a parallel data collection process that aggregates figures from company filings, broker research, and financial data platforms. This happens automatically when you invoke the /quality-screen command.

How is the quality-screen skill different from other screening tools?

Unlike flexible screeners that allow partial matches, the AI Berkshire quality-screen skill implements a "7+2" rule set that prioritizes specificity over sensitivity. It guarantees no false positives by excluding any company that fails hard metrics, whereas traditional tools might rank companies on a sliding scale.

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