# Industry Screening Skills in AI Berkshire: A Complete Guide to Sector Analysis

> Explore industry screening skills in AI Berkshire. Discover how Industry Research and Industry Funnel systematically evaluate sectors to find the best investment opportunities.

- Repository: [Xbt Lin/ai-berkshire](https://github.com/xbtlin/ai-berkshire)
- Tags: how-to-guide
- Published: 2026-07-29

---

**AI Berkshire provides two dedicated industry-screening skills—Industry Research and Industry Funnel—that systematically evaluate sectors and identify the most investable opportunities.**

AI Berkshire (xbtlin/ai-berkshire) is an open-source investment research framework that includes specialized **industry screening skills** for filtering global sectors and conducting deep-dive analysis. These skills automate the construction of industry value-chain maps and apply quantitative criteria to surface high-conviction investment themes.

## Industry Research Skill: Deep-Dive Sector Analysis

The **Industry Research** skill, defined in [`skills/industry-research.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/industry-research.md), performs comprehensive industry chain scans using the "four-master" analysis framework. This skill builds and validates investment logic chains, draws complete industry value-chain maps, and scans global listed companies across A-share, Hong Kong, and US markets.

### Core Analytical Components

The skill applies the **four-master** framework to headline companies, analyzing:

- **Business** model sustainability
- **Moat** and competitive advantages
- **Risk** factors and regulatory concerns
- **Management** quality and governance

Before generating final reports, the skill utilizes [`tools/report_audit.py`](https://github.com/xbtlin/ai-berkshire/blob/main/tools/report_audit.py) to perform data-audit checks and ensure analytical integrity.

### Output Format

Running this skill generates markdown reports (e.g., `~/新能源产业链投资研究报告.md`) containing:

- Complete industry value-chain maps
- Company comparison tables
- Four-master analysis sections
- Sector-level portfolio allocation recommendations

## Industry Funnel Skill: High-Level Sector Screening

The **Industry Funnel** skill, located in [`skills/industry-funnel.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/industry-funnel.md), acts as a preliminary filter that rapidly screens dozens of sectors to surface the most promising investment themes. Unlike the deep-research skill, this tool focuses on speed and breadth of coverage.

### Screening Criteria and Scoring

The skill defines specific screening criteria including:

- Trend strength and momentum
- Market size and growth potential
- Competitive dynamics
- Regulatory environment stability

It ranks industries by a weighted composite score and outputs a short list of "funnel-passed" sectors ready for deeper investigation.

### Integration with Quality Screening

According to the xbtlin/ai-berkshire source code, the **Quality-Screen** skill ([`skills/quality-screen.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/quality-screen.md)) works hand-in-hand with the Industry Funnel. After generating the initial sector shortlist, users can apply quantitative quality metrics such as ROE thresholds and cash-flow stability filters to validate the preliminary results.

## Codex Prompt Integration

Both industry screening skills are exposed as generated Codex slash-prompts for chat-based invocation:

- [`codex-prompts/industry-research.md`](https://github.com/xbtlin/ai-berkshire/blob/main/codex-prompts/industry-research.md) wraps the deep-research functionality
- [`codex-prompts/industry-funnel.md`](https://github.com/xbtlin/ai-berkshire/blob/main/codex-prompts/industry-funnel.md) provides the screening interface

These prompts enable analysts to invoke complex screening workflows directly from command-line chat interfaces without manually constructing CLI commands.

## Command Line Usage Examples

The repository includes [`scripts/run-skill.py`](https://github.com/xbtlin/ai-berkshire/blob/main/scripts/run-skill.py) to execute screening skills from the terminal.

### Running Industry Research

To execute a comprehensive analysis of a specific sector:

```bash

# Execute the industry-research skill for the "新能源" sector

python3 scripts/run-skill.py industry-research "新能源"

```

This command generates a detailed markdown report in your home directory containing the full chain analysis and four-master evaluations.

### Running Industry Funnel

To screen all available sectors with default parameters:

```bash

# Execute the industry-funnel skill with default screening parameters

python3 scripts/run-skill.py industry-funnel

```

The output displays a ranked table of sectors (e.g., "新能源", "半导体", "数字医疗") with funnel scores and highlights the top candidates.

### Automated Funnel-to-Research Workflow

Combine both skills to automate end-to-end sector screening:

```bash

# 1️⃣ Get top-3 sectors from the funnel

SECTORS=$(python3 scripts/run-skill.py industry-funnel --top 3 --plain)

# 2️⃣ Loop through each sector and launch full research reports

for S in $SECTORS; do
    python3 scripts/run-skill.py industry-research "$S"
done

```

This pipeline first filters for the three highest-scoring sectors, then automatically generates comprehensive research reports for each shortlisted candidate.

## Summary

- **Industry Research** ([`skills/industry-research.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/industry-research.md)) provides comprehensive value-chain analysis using the four-master framework and generates detailed investment reports audited by [`tools/report_audit.py`](https://github.com/xbtlin/ai-berkshire/blob/main/tools/report_audit.py)
- **Industry Funnel** ([`skills/industry-funnel.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/industry-funnel.md)) offers rapid quantitative screening across multiple sectors with weighted scoring algorithms
- Both skills can be invoked via [`scripts/run-skill.py`](https://github.com/xbtlin/ai-berkshire/blob/main/scripts/run-skill.py) or through generated Codex prompts stored in `codex-prompts/`
- **Quality-Screen** ([`skills/quality-screen.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/quality-screen.md)) complements the Industry Funnel by applying quantitative financial metrics to validated sector shortlists

## Frequently Asked Questions

### What is the difference between Industry Research and Industry Funnel?

Industry Funnel acts as a high-level filter that quickly screens dozens of sectors to surface promising themes based on trend strength and market dynamics, while Industry Research conducts comprehensive deep-dives including value-chain mapping and four-master analysis on specific sectors identified as investable.

### How do I run industry screening from the command line in AI Berkshire?

Use `python3 scripts/run-skill.py industry-research "sector_name"` for deep research or `python3 scripts/run-skill.py industry-funnel` for initial screening, with optional flags like `--top 3` to limit results and `--plain` to enable shell scripting integration.

### What output files does the Industry Research skill generate?

The skill generates markdown reports (e.g., `~/新能源产业链投资研究报告.md`) containing complete industry chain maps, global company tables, four-master analyses, and sector-level portfolio allocation tables, all validated by the [`tools/report_audit.py`](https://github.com/xbtlin/ai-berkshire/blob/main/tools/report_audit.py) module before publication.

### Where are the Codex prompts for industry screening stored?

The generated Codex prompts are stored in [`codex-prompts/industry-research.md`](https://github.com/xbtlin/ai-berkshire/blob/main/codex-prompts/industry-research.md) and [`codex-prompts/industry-funnel.md`](https://github.com/xbtlin/ai-berkshire/blob/main/codex-prompts/industry-funnel.md), enabling chat-based invocation of these screening skills without requiring direct CLI interaction.