AI Berkshire
A structured value-investing research workflow for Claude Code and Codex.
- Source repo
- xbtlin/ai-berkshire
- Stars
- ★ 17k
- Last updated
- 2d ago
- License
- MIT
- Primary language
- HTML
- FA score
- 48/100 · Major gaps
At a glance
- How it runs
- Works with
- Portable with changesCodex · Claude Code
- Cost
- Free software; you pay for model usage
- Setup effort
- Medium · a few setup steps
- You'll need
- Typical use
- An individual investor preparing a long-term study of a listed company can use
/investment-researchto produce a memo spanning business quality, moat, management, risks, and valuation. - Not a fit if
- Teams not using Claude Code or Codex
- Users needing a general API, standalone web app, or other runtime
- Users wanting cheap deep research without high token costs
- Source review
- 48/100 · Major gaps 1 safety controls not found
What does this agent do, and when should you use it?
AI Berkshire is a collection of value-investing skills for Claude Code and Codex. Its 20 task-specific entry points cover company research, earnings review, industry screening, portfolio work, thesis tracking, and WeChat article production. Heavier workflows use a Team Lead to coordinate four independent master-inspired perspectives, while lighter skills run their own direct workflow. The repository maintains Claude Code command sources, generated Codex skill packages, and an optional Codex slash-prompt compatibility layer. Its outputs are research reports, screening results, valuation calculations, and suggested actions; the project states that it is not investment advice.
Users invoke workflows with commands such as /investment-research 腾讯, /investment-team 美团, or /earnings-review 腾讯 2025Q4. /investment-research organizes work into seven stages: data collection, business essence, moat analysis, inversion, management assessment, civilization trends, and valuation with margin of safety; /investment-team runs four independent perspectives in parallel and has a Team Lead synthesize them. /industry-funnel narrows a market scan to no more than 10 companies and then three final selections, while /portfolio-review, /thesis-tracker, and /news-pulse address post-investment management and price-move attribution. tools/financial_rigor.py exposes verify-market-cap, verify-valuation, cross-validate, three-scenario, benford, and calc, using Python decimal.Decimal for precise decimal calculations. Source workflows live in skills/*.md, and scripts/sync-codex-skills.py generates codex-skills/*/SKILL.md packages from them.
- An individual investor preparing a long-term study of a listed company can use
/investment-researchto produce a memo spanning business quality, moat, management, risks, and valuation. - A researcher who needs several independent perspectives quickly can run
/investment-teamfor parallel analysis of one company and review the Team Lead synthesis. - An investor reviewing an earnings release from primary materials can use
/earnings-reviewor/earnings-teamfor a named company and reporting period. - Someone investigating an investment theme such as AI compute or nuclear power can use
/industry-researchfor a value-chain view or/industry-funnelto select candidates from a market scan. - A holder facing a sharp price move can use
/news-pulseto assess company events, regulation, competitors, and market sentiment. - An investor managing several positions can use
/portfolio-review,/thesis-tracker, and/thesis-driftto monitor allocations and test whether an investment thesis has changed.
How do you install or deploy this agent?
Install and verify Codex first:
npm install -g @openai/codex
codex --versionThen install the repository's Codex skills:
git clone https://github.com/xbtlin/ai-berkshire.git
cd ai-berkshire
./scripts/install-codex-skills.shOptional slash prompts:
./scripts/install-codex-prompts.shFor Claude Code, run this after cloning:
./scripts/install-claude-commands.shThe README does not specify any dedicated API keys or financial-data credentials.
How do you use this agent?
Restart Codex after installation, then name the skill and target in a request, for example: 使用 investment-research 研究腾讯, 使用 earnings-review 分析 PDD 2025年报, or 使用 industry-funnel 筛选 AI算力. With Codex prompts installed, use /prompts:investment-research 腾讯. In Claude Code, enter commands such as /investment-team 美团 or /investment-checklist 腾讯, 阿里巴巴, 美团, 拼多多.
What are this agent's strengths and limitations?
- It separates company research, earnings analysis, industry funnels, portfolio management, and thesis-drift checks into 20 named skill entry points.
/investment-teamand/earnings-teamuse four parallel master-inspired perspectives plus Team Lead synthesis instead of a single-perspective workflow.tools/financial_rigor.pycovers market-cap checks, valuation checks, multi-source comparison, scenario valuation, Benford analysis, and precise calculations withdecimal.Decimal.- It offers Claude Code commands, generated Codex skills, and optional Codex slash prompts for the two explicitly supported clients.
- The README states that deep-research workflows consume substantial tokens because they involve repeated research, cross-validation, and multi-agent synthesis.
- Research quality is described as model-dependent, and the maintainer recommends stronger models for moat, valuation, management, and risk judgments.
- Core usage depends on Claude Code or Codex; the repository does not provide a general API, standalone web app, or deployment path for other runtimes.
- Real-time MCP data access remains a future direction; no ready-to-configure Wind, Bloomberg, or Yahoo Finance integration is provided.
- The project produces research and suggested price ranges, but explicitly disclaims investment-advice status and asks users to conduct their own due diligence.
How does this agent compare with similar options?
Compared with directly asking an AI whether a stock is worth buying, AI Berkshire adds fixed skill workflows, information-richness ratings, rapid-veto checks, inversion, and multi-perspective synthesis to constrain the research process and output. It is not a standalone replacement for Claude Code or Codex; it supplies investing workflows within those clients.
Key facts side by side with the most closely related agents.
| Agent | Source review | Form / cost | Stars | Updated | Language | Full support on |
|---|---|---|---|---|---|---|
| AI Berkshire This agent | 48 · Major gaps | Agent plugin / skillFree + model costs | ★ 17k | 2d ago | HTML | Codex · Claude Code |
| TradingCodex | 54 · Major gaps | CLIFree + model costs | ★ 380 | 3mo ago | Python | Codex |
| FinRobot | 51 · Major gaps | CLIFree + model costs | ★ 8.4k | 13d ago | Python | OpenAI API |
| Agentic Company Researcher | Insufficient evidence | Self-hosted serviceFree + model costs | ★ 2.3k | 11d ago | Python | OpenAI API |
How does FollowAgents rate this agent?
Why each dimension lost points
Evidence shows: README explicitly instructs users to use --dangerously-skip-permissions mode, which weakens user confirmation, but the repo does not enforce it and warns of risks. Data flow transparency: README describes multi-agent parallel search and cross-validation, but does not detail data flow and storage. Sensitive data handling: No code handling sensitive data found, but tools may read financial data. Dependency security: No dependency manifest found, cannot assess. External effects: Tools call external APIs and web searches, but permission scope not specified. Rollback: No rollback mechanism provided. Source attribution: README claims real track record but provides no verifiable audit trail. Deductions: Lack of explicit least-privilege design, incomplete user confirmation, opaque data flow, no rollback, and unsubstantiated source attribution.
Evidence shows: README and test files demonstrate tool consistency, tests cover encoding and number extraction bug fixes. Dependency availability: No dependencies listed, but tests show only standard library usage. Failure messages: Tests verify error paths, but not all failure scenarios. Deductions: Missing dependency manifest, incomplete failure message coverage.
Evidence shows: README provides multiple usage scenarios and examples, covering different user needs. Capability boundaries: README describes each skill's purpose and limitations, but boundaries not explicit. Trigger precision: Command examples are clear, but precise trigger conditions not defined. Environment fit: Multi-platform installation instructions provided, but not all environments tested. Deductions: Capability boundaries and trigger precision not precise enough.
Evidence shows: README is well-structured, provides installation instructions, examples, and FAQ. Naming stability: Skill names are consistent. Known limitations: README mentions high token consumption, but not other limitations. License: MIT license clear. Versioning/changelog: No changelog provided. Maintenance responsibility: Maintainer not identified. Deductions: Missing changelog and maintainer information.
Evidence shows: README displays detailed output examples, practical. Marginal value: Multi-agent parallel and cross-validation add value. Cost-benefit: README mentions high token consumption, but no cost estimates. Deductions: Cost-benefit analysis insufficient.
Evidence shows: README claims real track record, but no verifiable data sources. Cross-validation: Tools support multi-source cross-validation, but actual validation results not shown. Fact/inference separation: README distinguishes facts and inferences, but not systematically. Deductions: Performance claims lack traceability, cross-validation results not demonstrated.
- Not found in source: rollback or recovery pathBack up first, or work on a git branch or snapshot, so its changes can be undone.
- Real track record claims lack verifiable audit evidence and should not be relied upon for reliability.
- Users should be cautious with --dangerously-skip-permissions mode and ensure trusted environment.
- Dependency manifest missing, supply chain security cannot be assessed.
Recent releases
Latest entries from the project's GitHub Releases. Notes are quoted from upstream as written, not rewritten or tested by FollowAgents.
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v1.0.0 v1.0.0 - AI Berkshire 首发
Release notes excerpt
AI Berkshire v1.0.0 AI 时代的伯克希尔 — 基于 Claude Code 的价值投资研究框架。 包含 5 个投资研究 Skill | Skill | 用途 | |-------|------| | /investment-research | 巴菲特-芒格-段永平-李录 四大师综合分析 | | /investment-team | 4个AI Agent并行投研团队 | | /investment-checklist | 巴菲特买入前六关 Checklist | | /industry-research | 产业链全景扫描 + 全球标的筛选 | | /private-company-research | 未上市公司侦探式深度研究 | 快速安装 ```bash git clone https://github.com/xbtlin/ai-berkshire.git cp ai-berkshire/skills/*.md ~/.claude/commands/ ``` 然后在 Claude Code 中使用 /investment-research 腾讯 即可开始研究。View on GitHub ↗
FAQ
Is deep research expensive to run?
/quality-screen and /news-pulse are presented as lighter first steps.