# How to Use the Investment Checklist Feature in AI Berkshire: A Complete Guide > Unlock AI Berkshire's investment checklist feature to evaluate stocks with a six-gate framework. Learn how to generate deterministic reports with Pass/Fail/Gray verdicts using parallel agents. - Repository: [Xbt Lin/ai-berkshire](https://github.com/xbtlin/ai-berkshire) - Tags: how-to-guide - Published: 2026-06-30 --- **The investment checklist feature in AI Berkshire evaluates stocks through a six-gate framework defined in [`skills/investment-checklist.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/investment-checklist.md), spawning parallel agents to collect financial data and output deterministic Markdown reports with Pass/Fail/Gray verdicts.** The AI Berkshire repository provides an autonomous investment analysis framework that implements Warren Buffett's investment principles through structured AI agents. The **investment checklist feature** serves as the primary pre-buy screening tool, applying rigorous financial rigor to determine whether a stock meets fundamental quality standards. This guide explains how to invoke the checklist, interpret its six-gate evaluation, and leverage the parallel agent architecture for efficient multi-stock analysis. ## How the Investment Checklist Architecture Works The investment checklist operates across three distinct layers: Skill, Agent, and Tool. ### Skill Definition Layer The core logic resides in [[`skills/investment-checklist.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/investment-checklist.md)](https://github.com/xbtlin/ai-berkshire/blob/main/skills/investment-checklist.md). This markdown file defines the six-gate "pre-buy" process, information-richness ratings (A/B/C), and required data-collection steps. The skill parses the `$ARGUMENTS` string by commas, spaces, or Chinese punctuation to extract company symbols, then orchestrates the entire evaluation workflow. ### Agent and Tool Integration When invoked, the skill spawns specialized agents that delegate precise calculations to [[`tools/financial_rigor.py`](https://github.com/xbtlin/ai-berkshire/blob/main/tools/financial_rigor.py)](https://github.com/xbtlin/ai-berkshire/blob/main/tools/financial_rigor.py). The checklist calls the `verify-valuation` and `three-scenario` sub-commands to compute ROE, margin-of-safety, and scenario-based target prices using exact decimal arithmetic. ### Parallel Processing For each ticker, the skill launches independent background agents using the generic **Task** utility. This parallelism allows concurrent data collection for profitability metrics, valuation multiples, growth trends, balance-sheet health, moat evidence, and management records. ## Running the Investment Checklist ### Claude Code CLI Method After installing the AI Berkshire commands, invoke the checklist directly: ```bash investment-checklist Moutai, NVIDIA, Apple ``` The command spawns parallel agents for each company and prints a summary table to the console while generating detailed Markdown reports in your home directory. ### Codex Slash Prompt Method For Codex users, use the slash-prompt syntax: ```text /prompts:investment-checklist Tesla, Alibaba ``` Alternatively, after installing the skills package: ```bash ./scripts/install-codex-skills.sh ./scripts/install-codex-prompts.sh ``` You can then use `/investment-checklist` followed by your tickers. ### Direct Tool Invocation For custom scripts or manual verification of specific calculations, call the financial rigor tool directly: ```bash python3 tools/financial_rigor.py verify-valuation \ --price 650 --eps 4.2 --bvps 35 --fcf-per-share 2.5 --dividend 0.8 ``` This outputs exact decimal results for the Margin of Safety gate calculations. ## The Six-Gate Evaluation Framework The checklist sequentially evaluates companies through six deterministic gates: 1. **Circle of Competence** – Determines if the business model is understandable 2. **Good Business** – Assesses ROE, margins, and capital efficiency 3. **Moat** – Evaluates competitive advantages and durability 4. **Management** – Reviews capital allocation and integrity records 5. **Margin of Safety** – Calculates valuation gaps using `verify-valuation` and `three-scenario` outputs 6. **Decision Discipline** – Applies final bias checks and information-richness ratings (A/B/C) Each gate receives a ★1-5 rating. Unlisted companies are marked "N/A" and skipped automatically. ## Understanding the Output Reports After evaluation, the skill generates timestamped Markdown reports at `~/巴菲特Checklist-.md` containing: - **Score tables** – Six-gate ratings (★1-5) for each evaluation criterion - **Core financial tables** – ROE, FCF, and valuation multiples - **Mirror Test** – A mandatory 5-sentence sanity check paragraph; failure to articulate coherent logic results in an automatic "Fail" - **Final verdict** – Pass, Fail, or Gray classification with core rationale For multi-company runs, the system creates `~/巴菲特Checklist-多公司对比.md` with side-by-side comparison tables. ## Installation and Setup Clone the repository and install the appropriate command layer for your AI coding tool: ```bash git clone https://github.com/xbtlin/ai-berkshire.git cd ai-berkshire # For Claude Code users ./scripts/install-claude-commands.sh # For Codex users ./scripts/install-codex-skills.sh ./scripts/install-codex-prompts.sh ``` Once installed, the `investment-checklist` command becomes available immediately. ## Summary - The **investment checklist feature** lives in [`skills/investment-checklist.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/investment-checklist.md) and implements a six-gate Buffett-style screening process - **Parallel agents** collect data concurrently for each ticker, calling [`tools/financial_rigor.py`](https://github.com/xbtlin/ai-berkshire/blob/main/tools/financial_rigor.py) for exact decimal calculations - Invoke via `investment-checklist ` in Claude Code or `/prompts:investment-checklist` in Codex - Reports output to `~/巴菲特Checklist-.md` with ★1-5 gate scores and Pass/Fail/Gray verdicts - The **Mirror Test** requirement ensures qualitative reasoning backs every quantitative result ## Frequently Asked Questions ### How does AI Berkshire handle unlisted companies in the checklist? Unlisted companies are automatically detected during the company metadata resolution phase and marked "N/A", causing the checklist to skip evaluation for those symbols while processing valid tickers normally. ### What is the information-richness rating (A/B/C) in the checklist output? The **information-richness rating** categorizes data quality: A indicates comprehensive, reliable data; B indicates moderate data gaps requiring caution; C signals poor data quality that triggers quick-kill red-lines and typically results in a "Gray" or "Fail" verdict regardless of other metrics. ### Can I customize the six-gate evaluation criteria? The gate logic is defined in [`skills/investment-checklist.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/investment-checklist.md) and operates deterministically. While you can modify the markdown skill file directly, the framework is designed for consistent, disciplined application of Buffett-style criteria rather than ad-hoc customization. ### Where are the checklist reports saved and how long do they persist? Reports are written to your home directory (`~/巴菲特Checklist-.md`) as timestamped Markdown files. They persist indefinitely as standard text files, allowing you to build historical comparison archives and track rating changes over time.