RRepoGEO

REPOGEO REPORT · LITE

lvwzhen/law-cn-ai

Default branch main · commit c7d65bd9 · scanned 6/21/2026, 1:52:56 PM

GitHub: 4,905 stars · 657 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
28 /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
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 lvwzhen/law-cn-ai, 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
  • highlicense#1
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file (e.g., MIT, Apache-2.0, GPL-3.0) in the root of the repository.
  • highreadme#2
    Clarify the README's opening paragraph to emphasize its role as a deployable legal AI assistant

    Why:

    CURRENT
    这个项目从 `pages` 目录中获取所有的 `.mdx` 文件，并将其处理成自定义上下文，以在OpenAI 文本自动补全提示中使用。
    COPY-PASTE FIX
    lvwzhen/law-cn-ai 是一个开箱即用的 AI 法律助手，旨在帮助用户分析法律文件并回答法律问题。它通过处理 `pages` 目录中的 `.mdx` 文件，将其转化为自定义上下文，以驱动OpenAI文本自动补全提示。
  • mediumtopics#3
    Add more specific topics to improve category visibility

    Why:

    CURRENT
    chatgpt, law, openai
    COPY-PASTE FIX
    chatgpt, law, openai, legal-ai, legal-tech, document-analysis, knowledge-base, chinese-law, llm-application

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 lvwzhen/law-cn-ai
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/transformers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 1×
  2. explosion/spaCy · recommended 1×
  3. OpenAI API · recommended 1×
  4. deepset-ai/haystack · recommended 1×
  5. elastic/elasticsearch · recommended 1×
  • CATEGORY QUERY
    How to build an AI assistant for legal document analysis and query answering?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. spaCy (explosion/spaCy)
    3. OpenAI API
    4. Haystack (deepset-ai/haystack)
    5. Elasticsearch (elastic/elasticsearch)
    6. Scikit-learn (scikit-learn/scikit-learn)
    7. Doccano (doccano/doccano)

    AI recommended 7 alternatives but never named lvwzhen/law-cn-ai. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best ways to create an AI knowledge base from markdown legal texts?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. OpenAI
    3. GPT-4
    4. GPT-3.5 Turbo
    5. Anthropic Claude
    6. Google Gemini
    7. Chroma
    8. Pinecone
    9. Weaviate
    10. OpenAI's `text-embedding-ada-002`
    11. Cohere's `embed-english-v3.0`
    12. `sentence-transformers/all-MiniLM-L6-v2`
    13. LlamaIndex
    14. Faiss
    15. Elasticsearch
    16. Haystack
    17. Milvus
    18. Qdrant
    19. spaCy
    20. NLTK
    21. Scikit-learn
    22. FastAPI
    23. Pandas DataFrame
    24. Azure AI Search
    25. AWS Kendra
    26. Google Cloud Search

    AI recommended 26 alternatives but never named lvwzhen/law-cn-ai. 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 lvwzhen/law-cn-ai?
    pass
    AI named lvwzhen/law-cn-ai explicitly

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

  • If a team adopts lvwzhen/law-cn-ai in production, what risks or prerequisites should they evaluate first?
    pass
    AI named lvwzhen/law-cn-ai 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 lvwzhen/law-cn-ai solve, and who is the primary audience?
    pass
    AI did not name lvwzhen/law-cn-ai — 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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lvwzhen/law-cn-ai — 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