ai-berkshire framework

I ran into a GitHub trending repo, xbtlin/ai-berkshire, which turns value-investing thinking (Buffett/Munger and others) into a practical Claude Code workflow.

What shipped here is not “a model” but a research framework: a structured prompt/code setup that uses Claude Code-style agent runs to produce and stress-test an investment thesis. The core idea is to map established valuation heuristics into an explicit process, then run multiple agents in parallel to check for weak assumptions, missing risks, and inconsistent reasoning.

Where I’d actually use it as an engineer: when you need a repeatable investigation loop that combines (1) document ingestion, (2) stepwise reasoning templates, and (3) self-critique via adversarial passes. For example, if you’re prototyping an internal RAG/agent system for “decision research” (not just Q&A), this repo is a concrete template for how to orchestrate multiple roles and how to force critique to happen as part of the pipeline.

What to look at first: - The repo’s Claude Code entrypoint (the file/page that defines the run or agent workflow) - The prompt/modeling conventions for each “master” methodology - The multi-agent setup that performs adversarial or cross-check analysis

If you care about production patterns, the useful takeaway is the separation of concerns: the research steps are structured as templates, while the agent layer focuses on execution and critique. That’s the part I’d reuse even if the investing domain doesn’t match your project.