xbtlin/ai-berkshire
AI 时代的伯克希尔：基于 Claude Code / Codex 的价值投资研究框架。巴菲特·芒格·段永平·李录四大师方法论 + 多Agent并行研究。| AI-era Berkshire: a value investing research framework built for Claude Code / Codex. 4 masters' methodologies + multi-agent adversarial analysis. observed · 2026-08-28
Health v2 · maintenance only
63/100
- Activity 99
- Release rhythm 46
- Longevity 10
Flags: young
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 148
- days_rel: 148
- days_push: 7
- n_releases_24m: 1
Adoption not part of the score
15907 stars · 2378 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
A collection of AI research skills for Claude Code and Codex that systematizes the value investing methodologies of Buffett, Munger, Duan Yongping, and Li Lu into structured multi-agent analysis workflows. It produces forced-conclusion investment reports with adversarial multi-perspective scoring, anti-bias checks, and financial calculation verification tools.
Use cases
- analyze whether a stock is worth buying with AI
- run value investing research on a company
- get structured buy/pass verdicts with price ranges instead of hedged AI answers
- apply Buffett Munger Duan Yongping Li Lu methodologies to stock analysis
- verify market cap and financial metrics against LLM hallucinations
- screen companies using value investing criteria
- run multi-agent adversarial investment analysis
When to choose
- you use Claude Code or Codex and want disciplined, structured value investing research
- you want multi-perspective analysis with forced conclusions rather than generic AI answers
- you need built-in anti-bias mechanisms like inversion checks and quick-reject lists
- you want reproducible financial calculation verification in Python
When to avoid
- you need real-time market data feeds or automated trading execution
- you expect guaranteed returns or professional investment advice
- you don't use Claude Code or Codex as your AI coding environment
- you need quantitative/backtesting frameworks rather than qualitative research workflows
Facets
framework · maturity active
agent-framework llm-inference prompt-engineering mcp data-science analytics fintech artificial-intelligence large-language-models analytics cli python cross-platform value-investing claude-code codex stock-analysis financial-analysis multi-agent warren-buffett charlie-munger investment-research skills-collection ai-agents
2 sources
- readme: https://github.com/xbtlin/ai-berkshire · fetched 2026-08-28 · 6dfcee3d3186
- homepage: https://github.com/xbtlin/ai-berkshire#readme · fetched 2026-08-29 · af555f0a3bde
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| xbtlin/ai-berkshire | main | 63 |
For agents
markdown · JSON · MCP: product_card(name="xbtlin/ai-berkshire")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem