Industry Funnel vs Industry Research Filtering Approaches in ai-berkshire

Industry funnel narrows a full-market universe down to 3-5 hand-picked companies through layered quantitative screens, while industry research constructs a complete industry-chain panorama evaluating every listed player across segments before any narrowing occurs.

The xbtlin/ai-berkshire repository provides two distinct systematic methodologies for equity analysis. Both approaches guide investors through structured sector evaluation, but they follow opposing filtering philosophies—one company-centric and selective, the other chain-centric and comprehensive.

Core Distinction: Narrowing Versus Mapping

The fundamental difference lies in the direction of analysis. Industry funnel operates as a subtractive filter, progressively eliminating candidates through hard quantitative screens until only the strongest remain. Industry research operates as an additive mapper, first constructing the complete competitive landscape across upstream, midstream, and downstream segments, then classifying every participant.

According to the source code in skills/industry-funnel.md and skills/industry-research.md, these approaches are designed to be complementary: research defines the investment arena, while the funnel picks the specific players.

The Industry Funnel Approach

Goal and Philosophy

The industry funnel targets company-level selection. Its primary objective is to digest a full-market universe of active stocks and output a concise shortlist of 3-5 targets suitable for deep investment analysis and portfolio construction.

Four-Stage Filtering Mechanics

As implemented in skills/industry-funnel.md#L13-L33, the funnel executes a rigid four-stage progression:

1. Full-market scan – Screen active stocks by momentum and market-cap to generate 30-60 candidate firms.
2. Hard-value criteria – Apply five quantitative filters (PE, ROE, cash-flow, debt-ratio, and moat (护城河) assessment) to reduce the list to ≤10 firms.
3. Granular analysis – Produce 300-500 word write-ups for remaining candidates, maintaining ≤10 firms.
4. Four-master deep dive – Conduct qualitative analysis to finalize a 3-company shortlist.

Each layer documents elimination rationale, creating an audit trail from broad universe to final selection.

Output and Deliverables

The approach generates a final report limited to 3-5 "target" stocks complete with investment theses, position sizing, and risk notes. This output suits analysts needing a concise shortlist for immediate portfolio construction.

The Industry Research Approach

Goal and Philosophy

Industry research pursues industry-chain mapping across A-share, HK, US, and international markets. Rather than filtering down, it builds a complete panorama to produce a sector-level investment portfolio covering all relevant sub-segments.

Chain-Centric Analysis Flow

The methodology in skills/industry-research.md#L16-L30 follows a distinct four-step workflow:

1. Logic-chain construction – Map trend → demand → bottleneck → sector relationships.
2. Full-chain scan – Evaluate all listed companies across global markets.
3. Tiered classification – Sort firms into Tier 1-4 categories based on market-cap, purity of exposure, and segment leadership.
4. Four-master analysis – Apply deep qualitative assessment to Tier 1-2 firms per segment.

Output and Deliverables

This approach yields a comprehensive industry-chain report containing segment tables, tiered company listings, and overall portfolio allocation guidelines (core, satellite, option, and ETF substitutes). It serves analysts requiring structural understanding of entire sectors before security selection.

Practical Code Examples

Execute these analyses through the repository's CLI or bot framework. The following markdown commands trigger the respective workflows:

# Run industry funnel for AI computing sector

/industry-funnel AI算力

# Run full industry research on innovative drugs

/industry-research 创新药

For programmatic execution using the internal CLI wrapper, use Python's subprocess module:

import subprocess

# Execute funnel analysis for robotics sector

subprocess.run(["python3", "scripts/run_skill.py", 
                "industry-funnel", "--arg", "机器人"])

# Execute research analysis for nuclear power sector  

subprocess.run(["python3", "scripts/run_skill.py", 
                "industry-research", "--arg", "核电"])

Both commands generate timestamped markdown reports under the reports/ directory (e.g., reports/机器人-funnel-20260707.md). Before publication, tools/report_audit.py provides the data-verification pipeline used by both approaches to validate findings.

When to Use Each Approach

Aspect	Industry Funnel	Industry Research
Primary Goal	Narrow to 3-5 high-conviction stocks	Build complete sector panorama
Starting Point	Full-market universe	Industry-chain logic construction
Key Filter	5 hard-value criteria + moat assessment	Tier 1-4 classification by exposure purity
Typical Output	Target shortlist with position sizing	Segment tables with portfolio allocation
Source Files	skills/industry-funnel.md, codex-prompts/industry-funnel.md	skills/industry-research.md, codex-prompts/industry-research.md

Use industry funnel when you need a concise shortlist for immediate portfolio construction. Use industry research when you must understand structural drivers across upstream, midstream, and downstream segments before allocating capital.

Summary

• Industry funnel applies a company-centric, multi-stage filter (skills/industry-funnel.md#L13-L33) that trims the universe to 3-5 stocks through quantitative screens and four-master analysis.
• Industry research employs a chain-centric, comprehensive mapping (skills/industry-research.md#L16-L30) that evaluates entire sectors and classifies firms into Tier 1-4 categories before selection.
• Both approaches utilize tools/report_audit.py for data verification and generate timestamped reports in the reports/ directory.
• The methodologies are complementary: run research first to define the arena, then apply the funnel to pick specific players.

Frequently Asked Questions

Should I run industry research before using the industry funnel?

While both can operate independently, they are designed to work sequentially. Running industry research first ensures your subsequent industry funnel analysis covers all relevant sub-segments within a sector, preventing omission of niche but critical players in the upstream or downstream chains.

What are the five hard-value criteria used in the industry funnel?

According to skills/industry-funnel.md, the five quantitative screens are: PE ratio, ROE, cash-flow metrics, debt-ratio, and moat (护城河) assessment. These filters reduce the initial 30-60 candidates to 10 or fewer firms before qualitative analysis begins.

How does industry research classify companies into tiers?

The industry research approach in skills/industry-research.md uses a Tier 1-4 classification system based on three factors: market capitalization, purity of segment exposure, and leadership position within the specific upstream, midstream, or downstream segment. Tier 1-2 firms receive full four-master analysis.

Which source file contains the report verification tools?

Both approaches rely on tools/report_audit.py for the data-verification pipeline. Additionally, codex-prompts/industry-funnel.md and codex-prompts/industry-research.md provide auto-generated prompt wrappers that ensure Codex compatibility when generating analysis reports.

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