AI Berkshire Development Roadmap: Current Features & Future Plans

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 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): 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. 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:

# 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 or 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: Central documentation containing the roadmap, installation guides, and skill overview.
• skills/*.md: Canonical source files for Claude Code commands (e.g., investment-research.md).
• codex-skills/*/SKILL.md: Generated Codex skill packages auto-synced from the skills directory.
• tools/financial_rigor.py: Financial validation utilities including market-cap verification and Benford's Law analysis.
• scripts/sync-codex-skills.py: Synchronization script ensuring compatibility between markdown sources and Codex artifacts.
• scripts/install-claude-commands.sh: Global command installation script for Claude Code integration.
• 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 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 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 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 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. The modular structure allows new investment checklists, research methodologies, or data validation tools to integrate seamlessly with existing CLI commands.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}