How the Industry-Funnel Skill Reduces 30-60 Stocks to 3 Final Picks

The industry-funnel skill implements a four-stage hard-filter workflow that systematically narrows a broad market universe of 30-60 securities down to three investment-ready candidates through objective value criteria, structured deep-dives, and portfolio complementarity analysis.

The industry-funnel skill in the xbtlin/ai-berkshire repository provides a systematic stock selection pipeline inspired by Warren Buffett-style value investing. According to the source code in [skills/industry-funnel.md](https://github.com/xbtlin/ai-berkshire/blob/main/skills/industry-funnel.md), this skill transforms raw market data into curated investment recommendations by enforcing transparent elimination criteria at every stage.

Stage 1: Full-Market Universe Generation

The workflow begins by aggregating every relevant ticker across A-shares, Hong Kong, U.S., and private-company pipelines. The skill constructs three distinct sets and takes their union:

• Active-trade set (A): Top 30 securities by 30-day average turnover per market
• Momentum set (B): Union of top 20 gainers over 30-day and 90-day windows
• Market-cap set (C): Industry-wide top 30 by market capitalization

The union A ∪ B ∪ C produces the initial universe of 30-60 stocks that enters the filtering pipeline.

Stage 2: Value-Investing Hard-Filters

This stage applies five objective financial criteria plus a qualitative moat assessment. According to the skill definition, only securities meeting all five criteria (or four plus one "near-miss") survive:

1. PE ratio: Must be reasonable versus peers, or PEG < 1.5
2. ROE: Greater than 15% (or demonstrating clear improvement)
3. Operating Cash Flow: Exceeds 70% of net profit
4. Debt/Equity: Below 60% (relaxed to 70% for utilities)
5. Moat Rating: Minimum three stars (★★★) based on brand, network effects, scale, technology, or cost advantages

This hard-filter typically reduces the universe to ≤10 candidates. Every eliminated stock is logged with a concise "淘汰理由" (elimination reason), creating a transparent audit trail.

Stage 3: Structured Deep-Dive Analysis

Each surviving candidate undergoes a template-driven analysis producing 300-500 word reports. The structured template forces analysts to examine:

• Business model sustainability
• Financial quality indicators
• Moat depth and durability
• Top-three risk factors
• Valuation snapshot

This stage surfaces hidden weaknesses that quantitative filters might miss, maintaining the candidate pool at ≤10 while enriching the data for final selection.

Stage 4: Four-Master Final Review

The ultimate reduction to three final picks relies on portfolio complementarity rather than pure scoring. The skill mandates:

• At least one "high-certainty, low-elasticity" position (classic Buffett style)
• At least one "medium-certainty, medium-elasticity" position (growth style)
• Optional "high-elasticity, high-risk" position (option style)

If a sector lacks three viable candidates, the skill recommends a "2 + 1 observation" structure rather than forcing a poor fit.

Executing the Industry-Funnel Skill

Invoke the skill through the repository's command-line interface:

python3 ai_berkshire.py /industry-funnel "AI Compute"

The tool outputs a filtered table after Stage 2 displaying the five hard-filter pass/fail flags:

| 公司 | PE | ROE | 现金流/净利 | 负债率 | 护城河 | 综合 | 留/弃 | 淘汰理由 |
|------|-----|-----|------------|--------|--------|------|-------|----------|
| NVIDIA | 28 | 31% | 85% | 38% | ★★★★ | ✔ | ✔ | – |
| XYZ   | 110| 4%  | 45% | 75% | ★★   | ✖ | ✖ | PE過高、護城河不足 |

Final recommendations are written to timestamped reports:

$ cat reports/AI_Compute-funnel-20260725.md

## 终选 3 家

| 公司 | 类型 | 推荐度 | 仓位 | 核心逻辑 | 关键风险 |
|------|------|--------|------|----------|----------|
| NVIDIA | 核心 | ★★★★★ | 55% | AI 生态龙头、护城河稳固 | 半导体周期、地缘风险 |
| 海光信息 | 卫星 | ★★★★☆ | 30% | 光模块增长、技术壁垒 | 客户集中、产能扩张 |
| XYZ | 期权 | ★★★☆☆ | 15% | 高弹性、转型潜力 | 研发失控、利润波动 |

The skill automatically appends a "数据抽检" (data spot-check) block that can be validated using the audit utility:

python3 tools/report_audit.py reports/AI_Compute-funnel-20260725.md

Source Files and Configuration

File	Purpose
skills/industry-funnel.md	Canonical workflow definition, filter thresholds, and output templates
codex-skills/industry-funnel/SKILL.md	Generated Codex artifact for Claude Code integration
tools/report_audit.py	Validation utility for the final data spot-check step
codex-prompts/industry-funnel.md	Prompt template for Codex-compatible environments

These files implement the complete reduction pipeline, from the initial market scan in [skills/industry-funnel.md](https://github.com/xbtlin/ai-berkshire/blob/main/skills/industry-funnel.md) to the audit trail validation in [tools/report_audit.py](https://github.com/xbtlin/ai-berkshire/blob/main/tools/report_audit.py).

Summary

• Four-stage reduction: Systematically filters 30-60 stocks to ≤10, then to 3 through objective criteria and qualitative analysis.
• Hard-filter discipline: Enforces strict PE, ROE, cash flow, debt, and moat requirements that eliminate subjective bias.
• Transparent audit trail: Every eliminated candidate receives a documented "elimination reason" preventing black-box decisions.
• Portfolio-aware selection: Final picks prioritize complementarity across certainty/elasticity profiles rather than standalone scores.

Frequently Asked Questions

What happens if fewer than three stocks pass the hard-filters?

The skill adopts a "2 + 1 observation" model rather than forcing poor fits. It selects only the viable candidates that meet the quality criteria and explicitly flags the shortfall for analyst review, maintaining discipline over completeness.

How is the moat rating determined in Stage 2?

The rating evaluates five dimensions defined in the skill configuration: brand strength, network effects, scale advantages, technology barriers, and cost structure. A security must achieve a minimum of three stars (★★★) to satisfy the filter, with four or five stars preferred for "core" positions.

Can the hard-filter thresholds be customized for specific industries?

Yes, the debt-to-equity threshold automatically adjusts to 70% for utility companies while remaining at 60% for other sectors. Additional industry-specific adjustments can be configured in the filter logic within skills/industry-funnel.md.

Where does the skill store the elimination audit trail?

The audit trail is embedded directly in the intermediate output tables and final report files under reports/{Sector}-funnel-YYYYMMDD.md. Each eliminated stock includes a specific "淘汰理由" column entry that can be extracted for compliance review or strategy backtesting.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}