BruceLanLan

BruceLanLan / buffett-oracle-analyzer

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巴菲特神谕分析师 - AI驱动上市公司深度分析Skill，融合巴菲特投资智慧与12模块分析框架，覆盖美股/港股/A股

40
8
100% credibility
Found Apr 08, 2026 at 40 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A set of custom instructions for AI chatbots to generate detailed, Warren Buffett-inspired investment analysis reports on publicly traded companies across multiple markets, accompanied by a stock data fetching tool.

How It Works

1
🔍 Discover the tool

You stumble upon Buffett Oracle Analyzer, a handy guide that lets AI chatbots analyze stocks just like Warren Buffett would.

2
🖥️ Open your AI chat

Head to Claude.ai on your web browser and start a fresh project for stock insights.

3
📋 Add smart instructions

Copy the special guidelines from the project into your chat's custom settings so the AI knows how to think like an investor.

4
🎯 Ask about a stock

Simply type a company name or code like 'NVDA' and watch the AI dive deep into its analysis.

5
⏳ Wait for the magic

The AI gathers info and crunches numbers behind the scenes to build a thorough report.

📊 Get your report

You receive a clear, Buffett-style breakdown with scores, valuations, risks, and smart buy/sell ideas to guide your thinking.

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Star Growth

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AI-Generated Review

What is buffett-oracle-analyzer?

This Python-based Buffett oracle analyzer turns Claude AI into a stock research powerhouse: feed it a ticker like NVDA or 00700.HK, and it spits out a deep-dive report blending Warren Buffett's value investing principles with a 12-module framework covering moats, valuations, earnings calls, and technicals across US, HK, and A-share markets. It solves the pain of manual stock screening by delivering actionable insights like Buffett scorecards, target prices, and position sizing in one prompt. Run the included data fetcher script for real-time Yahoo Finance pulls on quotes, financials, and indicators.

Why is it gaining traction?

It stands out by packaging Buffett's wisdom into structured, repeatable AI prompts—no need to craft your own chains—while supporting multiple markets and spitting out executable decisions like buy zones and stop-losses. Developers dig the ready-to-paste Claude skill setup and example reports for stocks like Costco or Tencent, making it dead simple to test. The hook? Turning generic LLMs into a personal oracle for value investing without building from scratch.

Who should use this?

Retail traders scripting quick screens on Chinese or global stocks. AI prompt tinkerers building finance bots who want a Buffett-flavored template. Python devs automating Yahoo data grabs for backtests or dashboards, especially those eyeing multi-market coverage.

Verdict

Grab it if you're prototyping AI investment tools—solid docs and cases make it easy to extend, despite 40 stars and 1.0% credibility signaling early maturity. Skip for production without your own data validation.

(178 words)

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