# AI Berkshire Skills: The Complete Guide to the I-6 Investment Research Framework

> Discover the 6 core skills of AI Berkshire's I-6 investment research framework. Learn about industry analysis, financial data validation, earnings review, and more.

- Repository: [Xbt Lin/ai-berkshire](https://github.com/xbtlin/ai-berkshire)
- Tags: deep-dive
- Published: 2026-06-27

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**AI Berkshire ships with six core skills (the I-6 framework), not sixteen, covering investment research, industry analysis, financial data validation, earnings review, portfolio monitoring, and supply-chain bottleneck detection.**

The `xbtlin/ai-berkshire` repository is an open-source investment research engine that codifies the workflows of four investment masters—Buffett, Munger, Duan Yong-ping, and Li Lu—into executable, auditable AI skills. While often mistakenly referred to as containing "16 skills," the platform actually implements the **I-6 skill collection**, six specialized Markdown-based skill packages that form a complete end-to-end research workflow.

## The I-6 Skill Architecture: Six Core Capabilities

The "I-6" designation refers to **six investment research skills**, not sixteen. Each skill is defined as a plain-text Markdown file under the `skills/` directory and automatically synchronized to a Codex-compatible package under `codex-skills/` via [`scripts/sync-codex-skills.py`](https://github.com/xbtlin/ai-berkshire/blob/main/scripts/sync-codex-skills.py). This dual structure ensures both human readability and compatibility with Claude Code and OpenAI Codex.

### /investment-research

The flagship skill orchestrates full-cycle investment research using the "four masters" framework. Defined in [`skills/investment-research.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/investment-research.md), this skill guides users through business quality assessment, management evaluation, and valuation rigor. It internally invokes `/financial-data` and `/earnings-review` for data gathering before producing a final investment thesis.

### /industry-research

This skill performs industry-level analysis, market-size sizing, and "civilization-evolution" framing to identify long-term trends. The source definition lives at [`skills/industry-research.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/industry-research.md) and includes specific data source requirements for sector classification and competitive landscape mapping.

### /financial-data

The data-validation backbone of the system, defined in [`skills/financial-data.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/financial-data.md). This skill enforces the **Financial Data Discipline**: dual-source verification (US equities: Macrotrends + StockAnalysis; HK equities: AASTocks + Macrotrends; China A-shares: Eastmoney + 巨潮) and exact-arithmetic validation via [`tools/financial_rigor.py`](https://github.com/xbtlin/ai-berkshire/blob/main/tools/financial_rigor.py) to eliminate LLM "mental math" errors.

### /earnings-review

Handles deep earnings-report reading, data reconciliation, and post-earnings commentary generation. The skill definition at [`skills/earnings-review.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/earnings-review.md) specifies transcript ingestion, KPI extraction, and cross-validation against financial data sources to ensure the <1% error tolerance required by the platform's rigor standards.

### /portfolio-review

Provides portfolio-level monitoring, risk aggregation, and rebalancing recommendations. Located at [`skills/portfolio-review.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/portfolio-review.md), this skill aggregates position-level data, calculates concentration risks, and flags deviations from target allocations using the standardized risk framework defined in [`AGENTS.md`](https://github.com/xbtlin/ai-berkshire/blob/main/AGENTS.md).

### /bottleneck-hunter

A specialized skill for global supply-chain bottleneck detection and arbitrage opportunity scouting. Defined in [`skills/bottleneck-hunter.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/bottleneck-hunter.md), this scans for capacity constraints, freight cost anomalies, and inventory distortions across global markets to identify preemptive investment opportunities.

## How AI Berkshire Skills Are Structured

Each skill follows a strict architectural pattern to ensure reproducibility and auditability:

**Skill Definition** – The canonical source is a Markdown file (`skills/*.md`) containing the workflow, required data sources, validation steps, and exact CLI commands.

**Codex Generation** – The [`scripts/sync-codex-skills.py`](https://github.com/xbtlin/ai-berkshire/blob/main/scripts/sync-codex-skills.py) utility reads the Markdown source and renders a Codex-compatible package (`codex-skills/<skill>/SKILL.md`), keeping Claude Code and Codex users synchronized with the canonical definitions.

**Tooling Backbone** – Core Python utilities under `tools/` provide the computational rigor. [`tools/financial_rigor.py`](https://github.com/xbtlin/ai-berkshire/blob/main/tools/financial_rigor.py) performs exact-arithmetic verification of market-cap and valuation calculations, while [`tools/report_audit.py`](https://github.com/xbtlin/ai-berkshire/blob/main/tools/report_audit.py) generates embedded audit tables for every output.

## Using the AI Berkshire Skills: CLI Examples

After installation via [`scripts/install-codex-skills.sh`](https://github.com/xbtlin/ai-berkshire/blob/main/scripts/install-codex-skills.sh), invoke any of the six skills using the `ai-berkshire` CLI:

```bash

# 1. Full investment research on a US ticker

ai-berkshire /investment-research --ticker AAPL

# 2. Industry analysis for the semiconductor sector

ai-berkshire /industry-research --sector semiconductor

# 3. Pull and validate financial data for a Chinese A-share

ai-berkshire /financial-data --ticker 600519

# 4. Review latest earnings for a US stock

ai-berkshire /earnings-review --ticker MSFT

# 5. Generate portfolio health check

ai-berkshire /portfolio-review --holdings portfolio.csv

# 6. Scan for global supply-chain bottlenecks in AI hardware

ai-berkshire /bottleneck-hunter --focus AI

```

Each command produces a Markdown report in the working directory containing **embedded audit tables** generated by [`tools/report_audit.py`](https://github.com/xbtlin/ai-berkshire/blob/main/tools/report_audit.py), with traceability links back to the originating skill definition.

## Data Discipline and Validation Rules

Every AI Berkshire skill adheres to the **two-source rule** mandated by the Financial Data Discipline:

- **US Equities**: Macrotrends + StockAnalysis
- **HK Equities**: AASTocks + Macrotrends  
- **China A-Shares**: Eastmoney + 巨潮

All calculations use [`tools/financial_rigor.py`](https://github.com/xbtlin/ai-berkshire/blob/main/tools/financial_rigor.py) for exact arithmetic, avoiding floating-point approximations that could compound into material valuation errors. This discipline ensures that outputs meet the <1% error tolerance required for investment-grade research.

## Summary

- AI Berkshire implements **six skills** (I-6), not sixteen: `/investment-research`, `/industry-research`, `/financial-data`, `/earnings-review`, `/portfolio-review`, and `/bottleneck-hunter`.
- Each skill is defined as a Markdown file under `skills/` and synchronized to `codex-skills/` via [`scripts/sync-codex-skills.py`](https://github.com/xbtlin/ai-berkshire/blob/main/scripts/sync-codex-skills.py).
- The platform enforces **dual-source verification** and **exact-arithmetic calculation** through [`tools/financial_rigor.py`](https://github.com/xbtlin/ai-berkshire/blob/main/tools/financial_rigor.py).
- Skills are composable: `/investment-research` chains `/financial-data` and `/earnings-review` for comprehensive analysis.
- All outputs include automated audit trails generated by [`tools/report_audit.py`](https://github.com/xbtlin/ai-berkshire/blob/main/tools/report_audit.py).

## Frequently Asked Questions

### Does AI Berkshire have 16 skills or 6 skills?

AI Berkshire contains **six skills**, not sixteen. The confusion often stems from the "I-6" designation (Investment-6), which refers to the six core skill packages. These are `/investment-research`, `/industry-research`, `/financial-data`, `/earnings-review`, `/portfolio-review`, and `/bottleneck-hunter`, as defined in the `skills/` directory.

### How do I install and use AI Berkshire skills?

Install the skill suite by running [`scripts/install-codex-skills.sh`](https://github.com/xbtlin/ai-berkshire/blob/main/scripts/install-codex-skills.sh) from the repository root. Once installed, invoke any skill via the `ai-berkshire` CLI followed by the slash-command and relevant parameters (e.g., `--ticker` or `--holdings`). Each execution produces a Markdown report with embedded audit tables.

### What is the difference between the `skills/` and `codex-skills/` directories?

The `skills/` directory contains the **canonical Markdown definitions** readable by humans and version-controlled as the source of truth. The `codex-skills/` directory contains **generated Codex-compatible packages** produced by [`scripts/sync-codex-skills.py`](https://github.com/xbtlin/ai-berkshire/blob/main/scripts/sync-codex-skills.py). The sync script ensures that Codex and Claude Code users receive the same logic as the canonical Markdown files.

### How does AI Berkshire ensure financial data accuracy?

The platform enforces the **Financial Data Discipline**: requiring dual-source verification for all data points (e.g., Macrotrends + StockAnalysis for US equities) and using [`tools/financial_rigor.py`](https://github.com/xbtlin/ai-berkshire/blob/main/tools/financial_rigor.py) for exact-arithmetic calculations. This prevents LLM hallucination in quantitative analysis and maintains a <1% error tolerance threshold for all generated reports.