RRepoGEO

REPOGEO REPORT · LITE

xbtlin/ai-berkshire

Default branch main · commit 9f5c287a · scanned 6/26/2026, 12:32:22 PM

GitHub: 2,808 stars · 409 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface xbtlin/ai-berkshire, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition README opening to emphasize end-to-end framework and decision support

    Why:

    CURRENT
    AI Berkshire 是一套基于 Claude Code 的投资研究 Skill 合集，将巴菲特、芒格、段永平、李录四位价值投资大师的方法论系统化、结构化，通过 AI Agent 实现专业级投资研究。
    COPY-PASTE FIX
    AI Berkshire is an **end-to-end AI investment research framework** built on Claude Code. It systematically integrates the methodologies of four value investing masters—Buffett, Munger, Duan Yongping, and Li Lu—and leverages multi-agent adversarial analysis to provide professional-grade **actionable conclusions and investment decision support**.
  • mediumreadme#2
    Add a concise 'Key Features' section to highlight application-level capabilities

    Why:

    COPY-PASTE FIX
    ## Key Features
*   **End-to-End Investment Research:** A complete framework for stock analysis and portfolio management.
*   **Multi-Agent Adversarial Analysis:** Leverages multiple AI agents, each embodying a master investor's methodology (Buffett, Munger, Duan Yongping, Li Lu), to provide robust, multi-perspective insights.
*   **Actionable Conclusions:** Generates clear 'Pass/Fail/Grey Zone' recommendations with specific price ranges, moving beyond generic AI summaries.
*   **Real-World Track Record:** Backed by demonstrated outperformance against major indices.
  • lowabout#3
    Update repository description to explicitly mention 'decision support' and 'actionable conclusions'

    Why:

    CURRENT
    AI 时代的伯克希尔：基于 Claude Code 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 on Claude Code. 4 masters' methodologies + multi-agent adversarial analysis.
    COPY-PASTE FIX
    AI 时代的伯克希尔：基于 Claude Code 的价值投资研究框架，提供**可操作的结论和投资决策支持**。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 on Claude Code, providing **actionable conclusions and investment decision support**. 4 masters' methodologies + multi-agent adversarial analysis.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface xbtlin/ai-berkshire
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 2×
  2. Pandas · recommended 1×
  3. NumPy · recommended 1×
  4. Scikit-learn · recommended 1×
  5. Beautiful Soup · recommended 1×
  • CATEGORY QUERY
    How can I build an AI agent for value investing research that provides actionable conclusions?
    you: not recommended
    AI recommended (in order):
    1. Pandas
    2. NumPy
    3. Scikit-learn
    4. Beautiful Soup
    5. Scrapy
    6. SEC API
    7. Alpha Vantage API
    8. Nasdaq Data Link
    9. Bloomberg Terminal
    10. Refinitiv Eikon
    11. Hugging Face Transformers
    12. spaCy
    13. TensorFlow
    14. PyTorch
    15. PostgreSQL
    16. MongoDB
    17. Streamlit
    18. Dash
    19. Docker
    20. Kubernetes

    AI recommended 20 alternatives but never named xbtlin/ai-berkshire. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking an LLM-powered framework for structured financial analysis and portfolio management with clear recommendations.
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. OpenAI API
    4. Hugging Face Transformers
    5. Microsoft Semantic Kernel

    AI recommended 5 alternatives but never named xbtlin/ai-berkshire. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of xbtlin/ai-berkshire?
    pass
    AI named xbtlin/ai-berkshire explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts xbtlin/ai-berkshire in production, what risks or prerequisites should they evaluate first?
    pass
    AI named xbtlin/ai-berkshire explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo xbtlin/ai-berkshire solve, and who is the primary audience?
    pass
    AI named xbtlin/ai-berkshire explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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xbtlin/ai-berkshire — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite