How to Use the Industry-Funnel Skill to Filter Stocks in ai-berkshire

The industry-funnel skill implements a four-layer quantitative and qualitative filtering process that narrows a broad market universe of 30-60 stocks down to three high-conviction investment ideas through hard metrics, structured analysis templates, and multi-perspective guru reviews.

The xbtlin/ai-berkshire repository provides an AI-powered investment research framework that replicates Berkshire Hathaway-style security selection through composable, markdown-defined skills. The industry-funnel skill serves as the primary stock filtering engine, systematically eliminating candidates through explicit inclusion criteria documented in skills/industry-funnel.md.

Understanding the Four-Layer Filtering Architecture

The industry-funnel skill processes securities through a sequential elimination workflow, with each layer producing reproducible output formats and documented "淘汰理由" (elimination rationale).

Layer 1: Full-Market Scan (30-60 Companies)

The funnel initializes with a full-market scan that unions three activity-based subsets across A-shares, Hong Kong, U.S., and international markets: high-turnover stocks, recent strong risers, and top-market-cap members. This layer also includes a dedicated "unlisted IPO candidates" section. The combination yields an initial pool of roughly 30-60 companies, ensuring broad coverage without geographic bias.

Layer 2: Hard-Metric Funnel (≤10 Companies)

Layer 2 applies five quantitative filters to every company from the initial pool:

• PE – Reasonable valuation vs. historical/peer levels, with PEG relaxation allowed
• ROE – Greater than 15% or demonstrating a clear improving trend
• Operating cash-flow – Exceeds 70% of net profit
• Debt-to-asset ratio – Below 60% (with higher caps permitted for utilities)
• Moat rating – Qualitative assessment of ★★★ or higher

Companies must satisfy all five criteria to advance automatically. Those meeting four criteria plus a "near-miss" on the fifth are flagged for manual review rather than immediate elimination. This quantitative screen reduces the universe to ≤10 firms.

Layer 3: Structured Deep-Dive

For each surviving company, the skill generates a 300-500 word structured analysis template covering business model mechanics, financial quality assessment, moat depth evaluation, top-3 risk factors, valuation snapshot, and a binary "enter final-3?" decision field. This enforces analytical consistency and surfaces residual doubts before final selection.

Layer 4: Four-Guru Deep Analysis

The final three selections undergo 800-1200 word narrative analysis from the perspectives of five investment philosophies: 段永平 (Duan Yongping), Warren Buffett, Charlie Munger, 李录 (Li Lu), and an "期权型" (optionality) lens. Each perspective produces a detailed narrative culminating in a composite star rating (★-style) and explicit position sizing recommendation (core/satellite/option/observation).

Executing the Industry-Funnel Skill Programmatically

The skill can be invoked via the provided CLI wrapper or parsed for downstream automation workflows.

Direct CLI Invocation

Use the ai_berkshire.run module to execute the funnel against a specific industry sector:

import subprocess, json

def run_industry_funnel(industry: str) -> str:
    """Runs the industry‑funnel skill and returns the raw markdown output."""
    result = subprocess.run(
        ["python3", "-m", "ai_berkshire.run", "industry-funnel", industry],
        capture_output=True,
        text=True,
        check=True,
    )
    return result.stdout

output_md = run_industry_funnel("AI 算力")
print(output_md)      # Full funnel report (layers 1‑4)

Parsing Layer 1 Tabular Output

Extract the initial screening table for DataFrame-based analysis or Excel export:

import pandas as pd
import re

def parse_layer1_table(md: str) -> pd.DataFrame:
    """Extracts the “全市场扫描” table from the markdown report."""
    # Locate the markdown table between the header and the next blank line

    table_md = re.search(r"\| 公司名 \| 代码 \| 市场 .*?\n(?P<table>(\|.*\n)+)", md, re.S).group("table")
    # Convert markdown pipes to CSV, then read with pandas

    csv_data = "\n".join([row.strip().strip("|") for row in table_md.splitlines() if row.strip()])
    return pd.read_csv(pd.compat.StringIO(csv_data), sep="|", engine="python")

Core Source Files and Architecture

The industry-funnel skill implementation spans three critical paths in the repository:

• skills/industry-funnel.md – Contains the core markdown skill definition, including layer specifications, the five hard metric thresholds, and structured analysis templates.
• codex-prompts/industry-funnel.md – Generated Codex prompt wrapper enabling execution through the OpenAI Codex CLI.
• scripts/sync-codex-skills.py – Synchronization utility that maintains parity between the canonical markdown skill definitions and the Codex-compatible prompt wrappers.

These files collectively implement the input → skill → structured output workflow, enabling integration with portfolio management systems and automated reporting pipelines.

Summary

• The industry-funnel skill applies a rigorous four-layer elimination process (30-60 → ≤10 → 3 stocks) with explicit documentation at each filtering stage.
• Five hard metrics (PE, ROE > 15%, operating cash flow > 70% of net profit, debt-to-asset < 60%, moat rating ≥ ★★★) drive the quantitative Layer 2 screening.
• Structured 300-500 word templates enforce consistent qualitative analysis for each Layer 2 survivor.
• Final selections undergo 800-1200 word multi-perspective guru review with explicit position sizing classifications (core/satellite/option/observation).
• Programmatic access available via python3 -m ai_berkshire.run industry-funnel with pandas-compatible output parsing for downstream automation.

Frequently Asked Questions

What markets does the industry-funnel skill cover?

The skill simultaneously scans A-shares, Hong Kong, U.S., and international equity markets as defined in skills/industry-funnel.md. It also includes a dedicated section for unlisted IPO candidates, ensuring comprehensive coverage without geographic bias.

What happens if a company meets four out of five hard metrics?

Companies satisfying four of the five quantitative filters but missing one are flagged for manual review rather than automatic elimination. This "near-miss" tolerance allows analysts to exercise judgment on qualitative factors while maintaining the discipline of the quantitative framework.

Can I customize the guru perspectives or analysis templates?

Yes, the analysis templates and guru perspectives are defined in skills/industry-funnel.md and can be customized by editing the markdown file directly. The scripts/sync-codex-skills.py utility ensures any modifications propagate to the codex-prompts/industry-funnel.md wrapper for CLI compatibility.

How do I extract the final three recommendations from the output?

The Layer 4 output generates structured markdown sections containing composite star ratings (★-style) and position sizing classifications (core/satellite/option/observation). You can extract these using regex patterns targeting the "四大师深度分析" section headers, similar to the Layer 1 table parsing approach.

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