# How to Use the Buffett-Style Investment Checklist in AI Berkshire: Complete Implementation Guide

> Master the Buffett-style investment checklist in AI Berkshire. This guide details implementation and rigorous financial reporting using precision tools. Implement today!

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

---

**The Buffett-style investment checklist in AI Berkshire is implemented as a declarative Skill at `/investment-checklist` that executes a six-gate pipeline through [`skills/investment-checklist.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/investment-checklist.md), producing rigor-checked investment reports using precision tools from [`tools/financial_rigor.py`](https://github.com/xbtlin/ai-berkshire/blob/main/tools/financial_rigor.py).**

AI Berkshire is an open-source framework that codifies Warren Buffett’s investment philosophy into reproducible AI workflows. The repository’s `investment-checklist` skill transforms qualitative value investing principles into a deterministic, data-verified analysis pipeline that eliminates float-drift errors and ensures reproducible results.

## What Is the Buffett-Style Investment Checklist?

The AI Berkshire investment checklist is a **Skill** defined in [`skills/investment-checklist.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/investment-checklist.md) that mirrors Buffett’s "buy-before-checklist" methodology. Unlike generic stock screeners, this implementation enforces financial rigor through a six-gate decision pipeline: ability-circle → good-business → moat → management → margin of safety → discipline.

When invoked, the skill spawns parallel Task agents for data gathering, then processes each ticker through the six gates using Python’s `decimal.Decimal` for exact arithmetic, eliminating floating-point errors that plague traditional financial calculators.

## How the Six-Gate Pipeline Works

The checklist evaluates every security through six deterministic gates defined in the skill template:

### 1. Ability Circle (能力圈)

Determines whether the business falls within the investor’s circle of competence.

### 2. Good Business (好生意)

Analyzes the underlying economics of the business model and industry dynamics.

### 3. Economic Moat (护城河)

Evaluates competitive advantages and barriers to entry.

### 4. Management Quality (管理层)

Assesses capital allocation discipline and shareholder alignment.

### 5. Margin of Safety (安全边际)

Calculates valuation buffers using three-scenario modeling.

### 6. Investment Discipline (纪律)

Final gate confirming checklist adherence before position entry.

## Running the Investment Checklist Skill

### Basic CLI Invocation

Activate the skill through Claude Code or Codex with the slash command:

```text
/investment-checklist 茅台, 英伟达, 苹果

```

For Codex prompts, use the Chinese invocation format:

```text
使用 investment-checklist 研究 茅台, 英伟达, 苹果

```

The skill returns a structured table with ★-ratings for each gate:

| 公司 | Checklist通过？ | 能力圈 | 好生意 | 护城河 | 管理层 | 安全边际 | 核心结论 |
|------|----------------|--------|--------|--------|--------|----------|----------|
| 茅台 | ✅ 通过 | ★★★★★ | ★★★★★ | ★★★★★ | ★★★☆☆ | ★★★★☆ | 高 |
| 英伟达 | ✅ 有条件 | ★★★★☆ | ★★★★★ | ★★★★★ | ★★★★★ | ★★★☆☆ | 中 |
| 苹果 | ✅ 通过 | ★★★★★ | ★★★★★ | ★★★★★ | ★★★★★ | ★★★★★ | 极高 |

### Python SDK Usage

Call the skill programmatically using the Codex CLI:

```python
from pathlib import Path
from subprocess import run, PIPE

def run_checklist(tickers: str):
    cmd = ["codex", "run", "/investment-checklist", tickers]
    result = run(cmd, stdout=PIPE, text=True)
    print(result.stdout)

run_checklist("茅台, 英伟达, 苹果")

```

## Financial Rigor Tools and Validation

The checklist delegates precision calculations to [`tools/financial_rigor.py`](https://github.com/xbtlin/ai-berkshire/blob/main/tools/financial_rigor.py), which provides CLI utilities for data verification. These tools ensure the six-gate analysis rests on mathematically exact foundations.

### Market Cap Verification

Validate market capitalization against reported figures:

```bash
python3 tools/financial_rigor.py verify-market-cap \
  --price 510 --shares 9.11e9 --reported 4.65e12 --currency HKD

```

### Valuation Ratio Validation

Confirm PE, PB, and dividend metrics:

```bash
python3 tools/financial_rigor.py verify-valuation \
  --price 510 --eps 23.5 --bvps 120 --fcf-per-share 18 --dividend 2.4

```

### Three-Scenario Valuation

Run Buffett-style conservative, base, and optimistic scenarios:

```bash
python3 tools/financial_rigor.py three-scenario \
  --price 510 --eps 23.5 --shares 9.11 \
  --growth 0.15 0.08 0.00 \
  --pe 25 20 15 \
  --currency HKD

```

These commands produce the exact numbers embedded in the checklist’s final tables, ensuring the **margin of safety** gate rests on verified data.

## Output Format and Reports

Upon completion, the skill generates three artifacts:

- A **per-company checklist** with ★-ratings for each of the six gates
- A **mirror-test statement** following the "5-sentence" rule for thesis clarity
- A **Markdown report** saved to `~/巴菲特Checklist-<name>.md` containing the summary table and detailed analysis

Because the checklist is fully declarative, running the same input twice yields identical structured output, enabling reproducible portfolio reviews and thesis tracking.

## Summary

- The **Buffett-style investment checklist** is implemented as the `/investment-checklist` skill in [`skills/investment-checklist.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/investment-checklist.md)
- Analysis flows through six deterministic gates: ability-circle, good-business, moat, management, margin of safety, and discipline
- **Financial rigor tools** in [`tools/financial_rigor.py`](https://github.com/xbtlin/ai-berkshire/blob/main/tools/financial_rigor.py) use `decimal.Decimal` to eliminate floating-point errors in valuation calculations
- Invoke via Claude Code slash commands, Codex prompts, or Python subprocess calls for batch processing
- Output includes star-rated checklists, mirror-test statements, and persistent Markdown reports

## Frequently Asked Questions

### How do I customize the six-gate criteria for specific industries?

Edit the prompt template in [`skills/investment-checklist.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/investment-checklist.md) to adjust gate weights or add industry-specific questions. The skill is declarative, so modifications apply consistently across all future runs. Refer to [`AGENTS.md`](https://github.com/xbtlin/ai-berkshire/blob/main/AGENTS.md) for the canonical-vs-generated skill architecture if you need to extend the pipeline with custom Task agents.

### Can I run the checklist on non-Chinese equities?

Yes. The financial rigor tools support multiple currencies via the `--currency` flag (e.g., `USD`, `HKD`, `EUR`). Simply pass the ticker symbols in the standard format recognized by your data provider, and the `verify-market-cap` and `three-scenario` utilities will handle precision arithmetic regardless of currency denomination.

### What makes the financial calculations different from Excel or standard Python floats?

The [`tools/financial_rigor.py`](https://github.com/xbtlin/ai-berkshire/blob/main/tools/financial_rigor.py) module uses Python’s `decimal.Decimal` class for all arithmetic operations, which maintains exact precision for financial calculations. This eliminates the float-drift errors common in IEEE 754 floating-point arithmetic, ensuring that valuation ratios and scenario models match audited financial statements to the cent.

### Where is the checklist output saved and how can I integrate it with other skills?

Results are automatically saved to `~/巴菲特Checklist-<name>.md` in your home directory. The Markdown format is compatible with downstream AI Berkshire skills listed in the README under "Skills 一览". You can pipe the output file path to portfolio-management skills or thesis-trackers for automated workflow chaining.