How to Use the Industry-Research Skill for Supply Chain Analysis in AI-Berkshire

The industry-research skill executes a seven-step structured pipeline that constructs investment logic chains, validates hypotheses, maps full industry panoramas, and generates portfolio recommendations for supply chain analysis.

The industry-research skill in the xbtlin/ai-berkshire repository provides a comprehensive framework for supply chain analysis and industry value-chain scanning. Defined in skills/industry-research.md, this canonical workflow enables both Claude Code and Codex users to perform rigorous, data-driven assessments that map upstream, midstream, and downstream segments while identifying beneficiary companies across global markets.

What Is the Industry-Research Skill?

The industry-research skill is the canonical workflow for full-stack industry and value-chain analysis within the AI-Berkshire ecosystem. Located at skills/industry-research.md, this file serves as the single source of truth for both Claude Code and Codex implementations, providing a structured methodology for deconstructing complex supply chains into analyzable components.

The skill is also packaged for Codex compatibility in codex-skills/industry-research/SKILL.md, allowing invocation across multiple interfaces while preserving the same underlying architecture and validation logic.

The Seven-Step Supply Chain Analysis Pipeline

When you invoke the industry-research skill for supply chain analysis, the system executes a rigorous seven-step pipeline:

1. Investment-Logic Chain Construction

The workflow begins by drawing a causal chain from macro trends to beneficiary segments. This follows the Chinese investment framework: 底层趋势 (underlying trend) → 需求 (demand) → 瓶颈 (bottleneck) → 受益产业链 (beneficiary industry chain). This step establishes the theoretical foundation for why a specific supply chain segment will capture value.

2. Chain Validation

Each arrow in the causal chain is questioned and verified against evidence. The skill generates a validation table with columns for 环节 (segment), 核心假设 (core assumptions), 验证方式 (validation method), and 数据来源 (data sources), ensuring that every logical connection is empirically grounded before proceeding.

3. Full-Chain Panorama Mapping

The industry is decomposed into upstream, midstream, downstream, and auxiliary segments (上游-中游-下游-辅助环节). For each segment, the analysis captures:

• 商业模式 (business model)
• 毛利率区间 (gross margin range)
• 竞争格局 (competitive landscape)
• 壁垒类型 (barrier types)
• 周期性 (cyclicality)

4. Global Listed-Company Scan

Task agents systematically scan all major markets including 美股 (US stocks), A股 (A-shares), 港股 (Hong Kong stocks), and international listings. This produces a segment-wise table containing: 公司 (company), 代码 (ticker), 市值 (market cap), 一句话描述 (one-sentence description), 是否纯正标的 (purity indicator), and 所属环节 (segment classification).

5. Four-Master Analysis

For each Tier-1 and Tier-2 company identified, the skill runs the "Four Masters" (四大师) modules evaluating:

• 生意本质 (nature of business)
• 护城河 (moat/competitive advantage)
• 风险 (risks)
• 管理层 (management quality)
• 估值 (valuation)
• 推荐度 (recommendation rating)

6. Industry-Level Risk and Civilization Trend Assessment

The analysis incorporates systemic risk checklists, historical analogues, and the Li Lu framework (李录框架) for identifying long-term civilization trends that could impact the supply chain's structural viability.

7. Portfolio-Allocation Recommendation

The final step generates a strategic allocation table distinguishing between core positions (核心), satellite positions (卫星), options, and ETF configurations, complete with specific buy/sell signals based on the preceding analysis.

Data Validation and Financial Rigor

All steps in the industry-research skill are data-driven and employ automated validation tools. According to skills/investment-research.md, the skill invokes tools/financial_rigor.py for precise financial calculations and tools/report_audit.py for multi-source cross-validation.

These utilities ensure that market-cap figures are verified, financial statements are cross-referenced against multiple sources, and data quality meets publication standards before the final report is generated.

How to Invoke the Industry-Research Skill

You can trigger the supply chain analysis workflow using simple slash commands in Claude Code or through the Codex interface:

# Basic supply chain analysis

/industry-research 供应链

# Specific theme analysis (e.g., EV battery supply chain)

/industry-research "电动汽车电池供应链"

Upon execution, the skill automatically generates a comprehensive markdown report in the repository root following the Output Requirements section of industry-research.md. The report filename follows the convention ~/[theme]-industry-[YYYYMMDD].md, such as ~/电动汽车电池供应链-industry-20260711.md.

Summary

• The industry-research skill resides in skills/industry-research.md and provides a canonical seven-step workflow for supply chain analysis.
• The pipeline progresses from investment-logic construction through chain validation, panorama mapping, global company scanning, Four-Master analysis, risk assessment, to portfolio recommendations.
• Validation tools tools/financial_rigor.py and tools/report_audit.py ensure data accuracy and cross-verification throughout the process.
• Invocation is available via /industry-research commands in Claude Code or through the Codex package at codex-skills/industry-research/SKILL.md.
• Output generates as a structured markdown report containing tables, chain diagrams, and master-analysis sections.

Frequently Asked Questions

What file contains the canonical definition of the industry-research skill?

The canonical skill definition lives in skills/industry-research.md within the xbtlin/ai-berkshire repository. This file serves as the source of truth for both Claude Code and Codex implementations. A generated wrapper for Codex compatibility is available at codex-skills/industry-research/SKILL.md.

How does the skill validate financial data during supply chain analysis?

The skill calls tools/financial_rigor.py for precise financial calculations and tools/report_audit.py for automated data quality audits. These utilities perform market-cap verification, multi-source cross-validation, and ensure figures meet publication standards before inclusion in the final report.

Can I use the industry-research skill outside of Claude Code?

Yes. While the skill is accessible via the /industry-research slash command in Claude Code, it is also packaged as a Codex skill in codex-skills/industry-research/SKILL.md. This allows invocation from any supported interface including direct CLI usage or Codex prompts, maintaining the same architectural logic across platforms.

Which markets does the global listed-company scan cover?

The task agents scan four major market categories: 美股 (US stocks), A股 (A-shares), 港股 (Hong Kong stocks), and international listings. This comprehensive coverage ensures identification of pure-play and related companies across the entire supply chain regardless of geographic listing location.

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