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skills/industry-research.md
so Claude Code and Codex users share one canonical workflow.
- Treat as the user's request in the current Codex thread.
- When the source mentions Claude-only surfaces such as Task, Agent, WebSearch, Bash, Read, or Write, use the closest Codex capability available in this session: subagents when available, web search when needed, shell commands for local tools, and normal file edits for workspace files.
- Use shared project tools from in this repository. Prefer running commands from the repository root with paths like
python3 tools/financial_rigor.py ...
- Before starting research, run the command to confirm today's date; treat it as the baseline for "latest" data and state the data cutoff date in the report header. Never assume the current date from training data.
- Preserve the research quality rules from : cross-check financial data, use exact arithmetic tools for valuation/math, and clearly label uncertainty and source gaps.
Industry Investment Research: Full Industry Chain Panoramic Scan + Four Masters' Individual Stock Analysis Framework
Conduct systematic industry chain investment research on the $ARGUMENTS industry.
Research Objectives
Starting from an investment theme/logic chain, complete:
- Verify each link of the investment logic chain
- Draw a complete industry chain panoramic map
- Scan all listed companies globally (A-shares/H-shares/U.S. stocks/international)
- Execute the Four Masters Framework analysis on leading companies in each segment
- Output industry-level investment portfolio allocation recommendations
Step 1: Investment Logic Chain Construction and Verification
1.1 Draw the Logic Chain
Express the causal relationship from "underlying trend" to "beneficiary targets" using arrow links, for example:
Underlying Trend A
→ Leads to Demand B
→ Creates Bottleneck/Rigid Demand C
→ Benefits Industry Chain D
1.2 Verify Each Link
Question each arrow in the logic chain and find evidence:
| Link | Core Assumption | Verification Method | Data Source |
|---|
| A→B | | Search industry data/forecasts | |
| B→C | | Search supply and demand analysis | |
| C→D | | Search actual cases/contracts | |
1.3 Find "Verified Events That Have Occurred"
List real commercial events that support the logic chain (not forecasts), such as procurement agreements from major companies, policy documents, industry reports, etc.
Step 2: Industry Chain Panoramic Map Drawing
2.1 Draw the Industry Chain Structure
Break down the industry into upstream → midstream → downstream → supporting links, for example:
Upstream: Raw Material/Resource Extraction → Material Processing/Purification
Midstream: Core Equipment Manufacturing → System Integration/Engineering Construction → New Technology R&D
Downstream: Operation/Service → End Customers
Supporting: Testing/Certification → Maintenance Services → Financial Instruments (ETF/Trust)
2.2 Identify "Business Characteristics" of Each Link
Label each link with:
| Link | Business Model | Gross Profit Margin Range | Competitive Landscape | Barrier Type | Cyclicality |
|---|
| Selling Resources/Selling Equipment/Selling Services/Rent Collection | | Monopoly/Oligopoly/Fully Competitive | Resource/License/Technology/Scale | Strong/Medium/Weak |
2.3 Mark "Bottleneck Links"
Identify links in the industry chain with the tightest supply, hardest substitution, and highest profit margins—these are often where the best investment targets lie.
AI Research Bias Awareness: Special Traps in Industry Research
AI data biases will be amplified in unique ways in industry research:
Industry-Level Biases:
| Bias Type | Performance | Response |
|---|
| Mature Industry Preference | Traditional industries (banking/energy/consumption) have a huge amount of data, making AI analysis seem "more certain" | Certainty comes from business models, not the number of research reports |
| Emerging Industry Underestimation | New industries (AI applications/synthetic biology, etc.) have limited data, leading to conservative AI analysis | Use "endgame thinking" instead of "current data" to judge industry value |
| Leading Company Preference | Large companies have far more data than small companies, so AI naturally tends to recommend leaders | Small companies may have better risk-reward ratios; don't ignore them just because AI analysis is short |
| Listed Company Preference | Scanning only listed companies will miss key unlisted players in the industry chain | Must search for unlisted companies and mark them as "future IPO candidates" |
| English Language Preference | AI has stronger processing capabilities for English materials, which may underestimate Chinese/Asian market players | Must search both Chinese and English information sources |
Anti-Bias Measures in Industry Chain Scanning:
- For each link, not only list "companies easily found by AI" but also actively search for "obscure but potentially high-quality targets"
- For small-cap companies with scarce information, don't reduce recommendation just because analysis is short—judge based on core issues (essence of business, moat, management) rather than report length
- Mark the "information sufficiency" (Level A/B/C) of each company in the final report, so readers know the reliability of AI analysis
Step 3: Global Listed Company Scanning
Use the Task tool to start a background Agent and comprehensively search all listed companies in the industry.
Search List
- Relevant companies in U.S. stocks (NYSE/NASDAQ/NYSE American)
- Relevant companies in A-shares (Shanghai/Shenzhen)
- Relevant companies in H-shares
- Other international markets (Japan/Korea/Europe/Australia, etc.)
- Industry ETFs
- Key unlisted companies (potential future IPOs)
Collect for Each Company
- Company name (Chinese and English)
- Stock code and exchange
- Approximate market capitalization
- One-sentence description (position and role in the industry chain)
- Whether it is a pure target (pure nuclear power vs. nuclear power business in diversified operations)
- Industry chain segment it belongs to
Output Format
Classify by industry chain segments, with a table for each segment containing all scanned companies.
Then stratify by investment certainty:
- Tier 1: Large market capitalization, pure target, industry leader
- Tier 2: Medium market capitalization, pure or high-proportion, segment leader
- Tier 3: Small market capitalization, development stage, high risk and high elasticity
- Tier 4: Large enterprises with relevant businesses in diversified operations
Step 4: Four Masters' Analysis of Leading Companies in Each Segment
For Tier 1 and Tier 2 companies in each industry chain segment, perform the following analysis (brief comments for Tier 3/4 companies):
4.1 Essence of the Business (Duan Yongping)
- One-sentence definition of what the company does in the industry chain
- Revenue structure and growth rate
- Gross profit margin/net profit margin level and trend
- Cash flow characteristics
- Follow-up Question: Is this a good business? Why?
4.2 Moat (Warren Buffett)
Score using five types of moats (★1-5):
| Moat | Strength | Evidence |
|---|
| Brand/Pricing Power | | |
| Switching Costs | | |
| Network Effects | | |
| Scale Effects | | |
| Technology/License Barriers | | |
Follow-up Question: Will the moat still exist in 10 years?
4.3 Risks (Charlie Munger)
- How is this company most likely to fail?
- How much is it worth in the worst-case scenario?
- Why don't smart people buy it?
4.4 Management (Duan Yongping + Warren Buffett)
- Who is the CEO/founder? Key decision records
- Shareholding ratio and interest alignment
- Brief evaluation (Level A/B/C)
4.5 Valuation Snapshot
- Current PE/PS/EV/EBITDA
- Comparison with competitors in the same segment
- Brief comment: Overvalued/Fairly Valued/Undervalued
4.6 Recommendation Rating
Mark with ★1-5:
- ★★★★★ = Core position candidate
- ★★★★☆ = Satellite position candidate
- ★★★☆☆ = Watchlist
- ★★☆☆☆ = High-risk option
- ★☆☆☆☆ = Not recommended
Step 5: Industry-Level Risk Assessment (Charlie Munger's "Checklist")
5.1 Systematic Risk List
| Risk | Probability | Impact | Response Strategy |
|---|
| A link in the investment logic chain is falsified | | | |
| Alternative technology emerges | | | |
| Policy/regulatory black swan | | | |
| Cyclical demand correction | | | |
| Valuation bubble burst | | | |
5.2 Historical Analogy
Find similar industry chain investment themes in history and analyze their final outcomes:
- What is the analogous industry?
- Who were the final winners? (Upstream/midstream/downstream?)
- Did most investors make money or lose money?
- What are the implications for the current industry?
5.3 Bias Self-Check
- Narrative bias: Is the story too perfect?
- Anchoring effect: Are you anchored by recent price increases?
- Herd effect: Are you buying because "everyone is buying"?
Step 6: Civilization Trend Judgment (Li Lu Framework)
- Is the underlying trend that this industry relies on a "civilization-level paradigm shift" or a "phased boom"?
- What is the closest technological revolution analogy in history?
- What will be the endgame of this industry in 10-20 years?
- Which link in the industry chain is most likely to have "winner-takes-all"?
- Which link is most likely to be disrupted?
Step 7: Investment Portfolio Allocation Recommendations
7.1 Recommended Portfolio
Output in the following structure:
| Tier | Position Proportion | Target | Industry Chain Segment | Core Logic |
|---|
| Core Position | 50-60% of theme position | | | Most certain, widest moat |
| Satellite Position | 25-35% of theme position | | | High elasticity, slightly lower certainty |
| Option Position | 5-15% of theme position | | | High risk and high return, may go to zero |
| ETF Alternative | Can replace all above | | | "Lazy solution" for those who don't want to pick stocks |
7.2 Buy/Sell Signals
| Signal Type | Specific Conditions |
|---|
| Increase Position Signal | |
| Reduce Position Signal | |
| Close Position Signal | |
7.3 Suggested Upper Limit for Theme Position
Based on the certainty and risk level of the investment logic chain, suggest the upper limit percentage of this theme in the total position.
Step 8: Comprehensive Decision Memo
Industry Overall Evaluation Table
| Dimension | Conclusion | Confidence Level |
|---|
| Investment Logic Chain (Verification Level) | | |
| Best Segment (Duan Yongping's "Right Business") | | |
| Widest Moat (Warren Buffett) | | |
| Biggest Risk (Charlie Munger) | | |
| Civilization Trend Positioning (Li Lu) | | |
| Overall Valuation Level | | |
Simulated Comments from the Four Masters
Use quote format to simulate comments from the four masters on the industry investment opportunity.
Output Requirements
- All analysis must be supported by data, with data sources attached
- Use Markdown tables to present key data
- Represent the industry chain panoramic map with a text diagram in a code block
- Analyze at least 2-3 leading companies for each segment
- Make the global company scan as complete as possible (A-shares/H-shares/U.S. stocks/international)
- Finally write the complete report to
~/[Industry Name] Industry Chain Investment Research Report.md
- Conclusions must be clear, with specific recommendations for targets, positions, and price ranges
- Include a "follow-up question" from the corresponding master at the end of each analysis module
Data Sampling Inspection (Approval Process)
After writing the report, perform data sampling inspection; it can be published only if passed:
bash
# Step 1 — Extract inspection list (15% random sampling)
python3 tools/report_audit.py extract \
--report <Report File Path>
# Step 2 — Retrieve data for each item in the list from reliable sources (see skills/financial-data.md)
# Step 3 — Output approval/rejection verdict
python3 tools/report_audit.py verdict \
--results '<Filled JSON>' \
--report <Report File Name>
【Approval】 All passed → Report can be published; 【Rejection】 Any failed → Revise and re-review.