AI Berkshire industry-research vs industry-funnel: Complete Skill Comparison

industry-research generates a comprehensive value-chain panorama of an entire sector, while industry-funnel executes a disciplined six-stage filter to identify the top three individual stocks for investment.

The xbtlin/ai-berkshire repository provides two distinct industry screening skills that serve sequential stages of the investment workflow. Understanding when to deploy industry-research versus industry-funnel determines whether you receive a macro-level sector map or a micro-level actionable shortlist. Both skills are defined as canonical markdown files under skills/ and leverage parallel agent execution, but they differ fundamentally in granularity, output structure, and analytical depth.

Primary Goals and Output Focus

Industry-Research: Value-Chain Mapping

The industry-research skill produces a full-panorama of an industry's value-chain, mapping every investment opportunity across upstream, mid-stream, downstream, and auxiliary segments. According to skills/industry-research.md, this skill executes a five-step workflow: build and validate a logic chain, draw the value-chain diagram, scan all global listed companies, perform four-master analysis on head-companies per segment, and deliver portfolio-allocation recommendations.

Output granularity remains at the segment level. You receive tiered company classifications (Tier 1-4), logic-chain diagrams, and tables grouping companies by their position in the value chain. This serves investors who need to understand every foothold an industry offers before selecting a focus area.

Industry-Funnel: Discriminating Stock Selection

The industry-funnel skill, as defined in skills/industry-funnel.md, performs a layer-by-layer "funnel" selection that narrows an entire market down to exactly three final target stocks. The workflow emphasizes hard elimination: full-market scan by activity and price-rise, application of five hard-value-investing criteria, rough filtering to ten or fewer companies, detailed 300-500 word analysis, and final deep dives of 800-1200 words on the three survivors.

Output granularity operates at the company level. The deliverable includes a scanned-company table with entry classes (A/B/C), a filter-by-criteria table documenting pass/fail statuses, and a final recommendation matrix categorizing selections as core, satellite, or option positions.

Technical Implementation in the Codebase

Skill Definition and Agent Orchestration

Both skills are implemented as canonical markdown specifications under skills/industry-research.md and skills/industry-funnel.md. The repository generates Codex-compatible wrappers at codex-skills/industry-research/SKILL.md and codex-skills/industry-funnel/SKILL.md, ensuring a single source of truth for both Claude-Code and Codex clients.

Each skill spawns multiple Agents that execute sub-steps in parallel. For industry-research, agents handle "全市场扫描" (full-market scanning) and segment-level four-master analysis. For industry-funnel, agents manage the "价值投资 5 条硬指标" (five hard-value criteria) filtering and the sequential elimination pipeline. These agents return structured JSON that the skill's orchestration script stitches into the final markdown report.

Audit and Validation Tools

Both skills invoke tools/report_audit.py for data-quality checks and tools/financial_rigor.py for precise valuation mathematics. These tools execute the "准出" (audit-pass) validation before publishing any output. The report_audit.py script verifies that segment tables in industry-research contain complete global coverage, while financial_rigor.py confirms market-cap calculations and multi-scenario valuations in industry-funnel meet Buffett-style rigor standards.

Practical Usage Examples

Running a Sector-Wide Analysis

Execute the value-chain scan to understand all opportunities within a sector:

/industry-research 核电

This generates 核电-industry-20260409.md, containing the logic chain, segment-level company tables, four-master analyses per segment, and a portfolio allocation matrix.

Executing a Targeted Selection

Run the funnel to distill a known sector into three actionable equities:

/industry-funnel AI算力

This produces AI算力-funnel-20260509.md, documenting the initial 30-60 candidates, the filtered shortlist of ten or fewer companies, and the final three-stock recommendation with risk/return profiles.

Integrated Workflow

Combine both skills for comprehensive coverage:

# Step 1: Map the entire sector structure

/industry-research AI算力

# Step 2: Select the optimal individual equities

/industry-funnel AI算力

The second command can reuse segment tables generated by the first, ensuring consistency between the macro sector view and the micro stock selection.

Summary

• industry-research operates at the segment level, producing exhaustive value-chain maps and tiered company classifications for entire global sectors.
• industry-funnel operates at the company level, applying five hard-value criteria to eliminate candidates and deliver exactly three deeply-analyzed stock recommendations.
• Both skills utilize parallel agent execution and mandatory audit validation via tools/report_audit.py and tools/financial_rigor.py.
• Use industry-research first to reveal sector structure, then industry-funnel to distill the most attractive individual equities.

Frequently Asked Questions

Can I use industry-funnel without first running industry-research?

Yes, but you lose the segment-level context. The industry-funnel skill performs its own full-market scan via the "全市场扫描" agent, so it can operate independently. However, as noted in the "与 industry-research 的区别" section of skills/industry-funnel.md, running industry-research first provides the value-chain logic that ensures your funnel analysis targets the correct industry segments.

How do the five hard-value criteria in industry-funnel differ from the four-master analysis in industry-research?

The five hard-value criteria in industry-funnel serve as binary elimination filters (pass/fail) designed to quickly reduce 30-60 candidates to 10 or fewer. The four-master analysis in industry-research provides qualitative, narrative depth on segment leaders without eliminating candidates. The former focuses on investment viability; the latter on competitive positioning and industry structure.

Why does industry-funnel limit output to exactly three stocks?

The constraint targets portfolio construction discipline. According to the workflow defined in skills/industry-funnel.md, the final stage categorizes selections into core, satellite, and option positions—three distinct risk/return profiles that provide sufficient diversification without diluting analysis quality. The 800-1200 word deep-dive format requires this focus to maintain the "准出" audit standards enforced by tools/report_audit.py.

Where are the skill definitions stored in the repository?

Canonical definitions reside in skills/industry-research.md and skills/industry-funnel.md. Auto-generated Codex wrappers exist at codex-skills/industry-research/SKILL.md and codex-skills/industry-funnel/SKILL.md. The high-level comparison appears in README.md lines 456-510 under the "行业筛选类" section.

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