RRepoGEO

REPOGEO REPORT · LITE

PKU-YuanGroup/Machine-Mindset

Default branch main · commit 5ee14c14 · scanned 6/14/2026, 5:53:22 AM

GitHub: 536 stars · 27 forks

AI VISIBILITY SCORE
22 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
1 / 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 PKU-YuanGroup/Machine-Mindset, 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
  • hightopics#1
    Add specific topics to the repository

    Why:

    COPY-PASTE FIX
    large-language-models, llm-personality, mbti, ai-mindset, cognitive-science, nlp, ai-evaluation, llm-benchmarking
  • highreadme#2
    Clarify the repository's purpose in the README's opening

    Why:

    COPY-PASTE FIX
    Add a concise sentence or two immediately after the main title, such as: "This repository provides the code, data, and framework for 'Machine Mindset: An MBTI Exploration of Large Language Models', enabling researchers to assess and understand the personality traits and cognitive styles of various LLMs."
  • mediumhomepage#3
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://huggingface.co/spaces/FarReelAILab/Machine_Mindset

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 PKU-YuanGroup/Machine-Mindset
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
langchain-ai/langchain
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. langchain-ai/langchain · recommended 2×
  2. OpenAI API · recommended 1×
  3. Anthropic API · recommended 1×
  4. Google Gemini API · recommended 1×
  5. run-llama/llama_index · recommended 1×
  • CATEGORY QUERY
    How can I assess the personality traits of different large language models?
    you: not recommended
    AI recommended (in order):
    1. OpenAI API
    2. Anthropic API
    3. Google Gemini API
    4. LangChain (langchain-ai/langchain)
    5. LlamaIndex (run-llama/llama_index)
    6. Qualtrics
    7. SurveyMonkey
    8. Vellum
    9. Humanloop
    10. Jupyter Notebooks (jupyter/notebook)
    11. Google Colab
    12. pandas (pandas-dev/pandas)
    13. nltk (nltk/nltk)
    14. spaCy (explosion/spaCy)
    15. Streamlit (streamlit/streamlit)
    16. Flask (pallets/flask)
    17. LIWC (Linguistic Inquiry and Word Count)
    18. TextBlob (sloria/TextBlob)
    19. Voyant Tools
    20. Giskard (Giskard-AI/giskard)
    21. Robust Intelligence
    22. GPT-4
    23. Claude 3
    24. Llama 3
    25. Gemini
    26. OpenAI Playground
    27. Anthropic Console
    28. Google AI Studio
    29. Hugging Face Transformers Library (huggingface/transformers)

    AI recommended 29 alternatives but never named PKU-YuanGroup/Machine-Mindset. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking frameworks to understand the cognitive style and behavioral patterns of LLMs.
    you: not recommended
    AI recommended (in order):
    1. TransformerLens (neelnanda-io/TransformerLens)
    2. Language Model Evaluation Harness (LM Eval Harness) (EleutherAI/lm-evaluation-harness)
    3. LIME (marcotcr/lime)
    4. SHAP (shap/shap)
    5. Captum (pytorch/captum)
    6. LangChain Tracing (langchain-ai/langchain)
    7. LangSmith
    8. OpenAI Evals (openai/evals)
    9. DSPy (stanfordnlp/dspy)
    10. TCAV (tensorflow/tcav)
    11. Concept Bottleneck Models (CBMs)

    AI recommended 11 alternatives but never named PKU-YuanGroup/Machine-Mindset. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    warn

    Suggestion:

  • 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 PKU-YuanGroup/Machine-Mindset?
    pass
    AI did not name PKU-YuanGroup/Machine-Mindset — likely talking about a different project

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

  • If a team adopts PKU-YuanGroup/Machine-Mindset in production, what risks or prerequisites should they evaluate first?
    pass
    AI named PKU-YuanGroup/Machine-Mindset 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 PKU-YuanGroup/Machine-Mindset solve, and who is the primary audience?
    pass
    AI did not name PKU-YuanGroup/Machine-Mindset — likely talking about a different project

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

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PKU-YuanGroup/Machine-Mindset — 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