RRepoGEO

REPOGEO REPORT · LITE

km1994/LLMsNineStoryDemonTower

Default branch main · commit 3baf9100 · scanned 5/12/2026, 11:22:47 AM

GitHub: 2,160 stars · 206 forks

AI VISIBILITY SCORE
23 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 fail
Objective metadata checks
AI knows your name
2 / 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 km1994/LLMsNineStoryDemonTower, 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 the README's opening to clarify its purpose as practical guides

    Why:

    CURRENT
    ## 【LLMs 入门实战系列】
    COPY-PASTE FIX
    ## 【LLMs 入门实战系列】

这是一个分享LLMs在自然语言处理（ChatGLM、Chinese-LLaMA-Alpaca、小羊驼 Vicuna、LLaMA、GPT4ALL等）、信息检索（langchain）、语言合成、语言识别、多模态等领域（Stable Diffusion、MiniGPT-4、VisualGLM-6B、Ziya-Visual等）等方面的实战与经验的系列教程。

> 【LLMs 入门实战系列】交流群 (注：人满 可 添加 小编wx：yzyykm666 加群！)
  • hightopics#2
    Add relevant topics to improve categorization

    Why:

    COPY-PASTE FIX
    llms, large-language-models, nlp, generative-ai, machine-learning, deep-learning, chatglm, llama, alpaca, vicuna, langchain, stable-diffusion, minigpt-4, visualglm, practical-guides, examples, tutorials
  • highlicense#3
    Add a LICENSE file to clarify usage rights

    Why:

    COPY-PASTE FIX
    Create a LICENSE file (e.g., MIT or Apache-2.0) in the root directory of the repository.

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 km1994/LLMsNineStoryDemonTower
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 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 1×
  2. LangChain · recommended 1×
  3. OpenAI Cookbook · recommended 1×
  4. DeepLearning.AI · recommended 1×
  5. Towards Data Science · recommended 1×
  • CATEGORY QUERY
    Where can I find practical guides and real-world examples for implementing large language models?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. LangChain
    3. OpenAI Cookbook
    4. DeepLearning.AI
    5. Towards Data Science
    6. Analytics Vidhya

    AI recommended 6 alternatives but never named km1994/LLMsNineStoryDemonTower. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are good resources for exploring different large language models and their diverse applications?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Hub
    2. OpenAI Playground
    3. OpenAI API Documentation
    4. Google AI Studio
    5. Gemini API
    6. Anthropic Claude
    7. Papers With Code
    8. Awesome-LLM
    9. LangChain Documentation
    10. LangChain Examples

    AI recommended 10 alternatives but never named km1994/LLMsNineStoryDemonTower. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 km1994/LLMsNineStoryDemonTower?
    pass
    AI did not name km1994/LLMsNineStoryDemonTower — 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 km1994/LLMsNineStoryDemonTower in production, what risks or prerequisites should they evaluate first?
    pass
    AI named km1994/LLMsNineStoryDemonTower 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 km1994/LLMsNineStoryDemonTower solve, and who is the primary audience?
    pass
    AI named km1994/LLMsNineStoryDemonTower explicitly

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

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km1994/LLMsNineStoryDemonTower — 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