# AI Berkshire Development Roadmap: Current Features & Future Plans

> Explore the AI Berkshire development roadmap. Discover current features and planned enhancements for this modular, AI-driven investment research framework. Learn about its multi-agent architecture and integrated value-investing...

- Repository: [Xbt Lin/ai-berkshire](https://github.com/xbtlin/ai-berkshire)
- Tags: getting-started
- Published: 2026-06-28

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**The AI Berkshire development roadmap outlines a modular, AI-driven investment research framework that integrates four value-investing master frameworks with multi-agent architecture, currently featuring 14 completed milestones and 4 planned enhancements.**

AI Berkshire is an open-source project (xbtlin/ai-berkshire) that combines the investment philosophies of Buffett, Munger, Duan Yongping, and Li Lu into executable AI skills. The roadmap is maintained in the repository's main [`README.md`](https://github.com/xbtlin/ai-berkshire/blob/main/README.md) under the *项目路线图* section, where completed tasks are marked `[x]` and future work remains `[ ]`.

## Current Completed Milestones

The framework currently delivers production-ready capabilities across research, screening, and portfolio management:

- **四大师综合分析框架** (`/investment-research`): A full-company deep-research skill that integrates analysis from all four value-investing masters, accessible via the `investment-research` CLI command.

- **多Agent并行投研团队** (`/investment-team`): Parallel execution architecture deploying four independent agents simultaneously to accelerate analysis richness and speed.

- **巴菲特买入前 Checklist** (`/investment-checklist`): A six-gate fast-screening system to determine whether a stock warrants deeper investigation.

- **产业链全景扫描** (`/industry-research`) **& 行业漏斗筛选** (`/industry-funnel`): End-to-end industry research capabilities combined with funnel-based stock selection workflows.

- **未上市公司研究框架** (`/private-company-research`): "Detective-style" research methodology for private firms utilizing multi-source data synthesis.

- **金融严谨性工具** ([`tools/financial_rigor.py`](https://github.com/xbtlin/ai-berkshire/blob/main/tools/financial_rigor.py)): Precision validation utilities including decimal-based calculations, market-cap verification, multi-source cross-validation, and Benford's Law compliance checks.

- **股价异动快速归因** (`/news-pulse`): A four-dimensional parallel news-scraping and attribution engine designed to explain price spikes in real-time.

- **财报精读** (`/earnings-review` & `/earnings-team`): Detailed financial statement parsing with team-based collaborative reporting structures.

- **投资组合管理** (`/portfolio-review`): Portfolio-level functionality for position sizing, concentration limit enforcement, and rebalancing strategies.

- **投资论文追踪** (`/thesis-tracker`): Post-trade discipline system that monitors investment thesis validity over time.

- **管理层纵深研究** (`/management-deep-dive`): Deep-dive capabilities focused on leadership quality assessment and governance analysis.

- **去劣快速筛选** (`/quality-screen`): Seven hard-filter criteria designed to eliminate low-quality stocks efficiently.

- **段永平思维模拟** (`/dyp-ask`): Q&A interface that generates responses emulating Duan Yongping's investment thinking style.

- **深度系列长文** (`/deep-company-series`): Automated generation of comprehensive eight-article research series totaling 120,000 words for public dissemination.

## Upcoming Roadmap Items

The following enhancements are planned but not yet implemented, as indicated by unchecked items in the repository roadmap:

- **历史回测：AI研报 vs 实际股价表现**: Quantitative validation system to backtest AI-generated research against actual historical market outcomes. Status: Pending.

- **宏观经济周期分析框架**: Systematic macroeconomic cycle evaluation tools to complement existing company-level research capabilities. Status: Pending.

- **基于MCP的实时数据接入（Wind/Bloomberg/Yahoo Finance）**: Real-time market data pipelines integrating major financial data providers for up-to-the-minute valuations. Status: Pending.

- **进一步 Codex/Claude compatibility improvements**: Enhanced synchronization between `skills/*.md` source files and Codex skill artifacts, managed via [`scripts/sync-codex-skills.py`](https://github.com/xbtlin/ai-berkshire/blob/main/scripts/sync-codex-skills.py). Status: Ongoing.

- **Enhanced reporting & audit tooling**: Automated consistency checks, versioned report archives, and peer-review workflow automation. Status: In design.

## Practical Usage Examples

The following commands demonstrate currently available capabilities and anticipated future syntax:

```bash

# Deep research on a listed company (已实现)

investment-research 腾讯

# Multi-agent team analysis (已实现)

investment-team 美团

# Quick checklist screening (已实现)

investment-checklist 茅台, 英伟达, 苹果

# Financial rigor verification (已实现)

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

# Future backtesting capability (未实现 - planned)

ai-berkshire backtest --skill investment-research \
    --ticker TSMC --start 2020-01-01 --end 2023-12-31

```

All commands require CLI installation via [`scripts/install-claude-commands.sh`](https://github.com/xbtlin/ai-berkshire/blob/main/scripts/install-claude-commands.sh) or [`scripts/install-codex-skills.sh`](https://github.com/xbtlin/ai-berkshire/blob/main/scripts/install-codex-skills.sh) as documented in the *快速开始* section.

## Key Architecture Files

The roadmap implementation relies on these core files:

- **[`README.md`](https://github.com/xbtlin/ai-berkshire/blob/main/README.md)**: Central documentation containing the roadmap, installation guides, and skill overview.
- **`skills/*.md`**: Canonical source files for Claude Code commands (e.g., [`investment-research.md`](https://github.com/xbtlin/ai-berkshire/blob/main/investment-research.md)).
- **`codex-skills/*/SKILL.md`**: Generated Codex skill packages auto-synced from the skills directory.
- **[`tools/financial_rigor.py`](https://github.com/xbtlin/ai-berkshire/blob/main/tools/financial_rigor.py)**: Financial validation utilities including market-cap verification and Benford's Law analysis.
- **[`scripts/sync-codex-skills.py`](https://github.com/xbtlin/ai-berkshire/blob/main/scripts/sync-codex-skills.py)**: Synchronization script ensuring compatibility between markdown sources and Codex artifacts.
- **[`scripts/install-claude-commands.sh`](https://github.com/xbtlin/ai-berkshire/blob/main/scripts/install-claude-commands.sh)**: Global command installation script for Claude Code integration.
- **[`scripts/install-codex-skills.sh`](https://github.com/xbtlin/ai-berkshire/blob/main/scripts/install-codex-skills.sh)**: Local Codex skill generation and installation utility.
- **`reports/`**: Directory containing real-world research outputs generated by the framework.

## Summary

- AI Berkshire development roadmap tracks 14 completed milestones across research, screening, and portfolio management domains.
- The framework implements a **multi-agent architecture** found in `/investment-team` and `/earnings-team` directories.
- **Financial rigor tools** in [`tools/financial_rigor.py`](https://github.com/xbtlin/ai-berkshire/blob/main/tools/financial_rigor.py) ensure calculation precision and data validation.
- Future development focuses on **historical backtesting**, **real-time data integration**, and **enhanced audit tooling**.
- All roadmap status indicators are maintained in the **项目路线图** section of [`README.md`](https://github.com/xbtlin/ai-berkshire/blob/main/README.md) using markdown checkboxes.

## Frequently Asked Questions

### What is the AI Berkshire development roadmap?

The AI Berkshire development roadmap is a living document tracked in the repository's [`README.md`](https://github.com/xbtlin/ai-berkshire/blob/main/README.md) that outlines the evolution of a value-investing AI framework. It catalogs completed features like the four-masters analysis framework and multi-agent research teams, while identifying upcoming priorities such as historical backtesting and real-time data integration.

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

The framework employs [`tools/financial_rigor.py`](https://github.com/xbtlin/ai-berkshire/blob/main/tools/financial_rigor.py) to enforce precision through decimal-based arithmetic, market-cap verification against reported figures, multi-source cross-validation, and Benford's Law statistical checks. These safeguards prevent rounding errors and data inconsistencies in valuation workflows.

### What multi-agent capabilities are currently available?

AI Berkshire implements parallel agent execution through `/investment-team` and `/earnings-team`, where four independent agents analyze investment opportunities simultaneously. This architecture accelerates research depth while maintaining the distinct philosophical frameworks of Buffett, Munger, Duan Yongping, and Li Lu.

### How can developers extend the AI Berkshire framework?

Developers can add new capabilities by creating markdown skill definitions in `skills/*.md`, which are automatically synchronized to Codex format via [`scripts/sync-codex-skills.py`](https://github.com/xbtlin/ai-berkshire/blob/main/scripts/sync-codex-skills.py). The modular structure allows new investment checklists, research methodologies, or data validation tools to integrate seamlessly with existing CLI commands.