RRepoGEO

REPOGEO REPORT · LITE

zwq2018/Data-Copilot

Default branch main · commit c32f9dfa · scanned 5/29/2026, 12:38:02 AM

GitHub: 1,512 stars · 152 forks

AI VISIBILITY SCORE
35 /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
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 zwq2018/Data-Copilot, 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 relevant topics to the repository

    Why:

    COPY-PASTE FIX
    llm-agent, financial-data, chinese-market, data-analysis, autonomous-ai, stock-market, economic-data
  • highreadme#2
    Reposition the README's opening to emphasize its unique niche

    Why:

    CURRENT
    Data-Copilot is a LLM-based system that help you address data-related tasks. Data-Copilot connects data sources from different domains and diverse user tastes, with the ability to autonomously manage, process, analyze, predict, and visualize data.
    COPY-PASTE FIX
    Data-Copilot is an LLM-based autonomous agent specifically designed to manage, process, analyze, predict, and visualize Chinese financial market data, including stocks, funds, and economic indicators. It connects diverse data sources and user tastes to autonomously transform raw data into informative results.
  • mediumreadme#3
    Add a concise 'Key Capabilities' section to the README

    Why:

    COPY-PASTE FIX
    ## Key Capabilities
    
    Data-Copilot autonomously handles a wide range of financial data tasks:
    *   **Intelligent Data Management:** Connects and processes diverse Chinese financial data sources.
    *   **Automated Analysis & Prediction:** Performs complex data analysis and generates predictions for market trends.
    *   **Dynamic Visualization:** Creates informative charts and graphs from raw data.
    *   **End-to-End Workflow:** Manages the entire data lifecycle from query to insightful results without manual intervention.
    *   **Focus on Chinese Markets:** Specialized support for Chinese stocks, funds, economic, and financial data.

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 zwq2018/Data-Copilot
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 1×
  2. LlamaIndex · recommended 1×
  3. OpenAI API · recommended 1×
  4. Hugging Face Transformers · recommended 1×
  5. Google Cloud Vertex AI · recommended 1×
  • CATEGORY QUERY
    How can I use large language models to autonomously process and analyze financial data?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. OpenAI API
    4. Hugging Face Transformers
    5. Google Cloud Vertex AI
    6. Microsoft Azure OpenAI Service
    7. Deepset Haystack

    AI recommended 7 alternatives but never named zwq2018/Data-Copilot. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help analyze, predict, and visualize Chinese stock market and economic data?
    you: not recommended
    AI recommended (in order):
    1. Wind Financial Terminal (Wind Data)
    2. Refinitiv Eikon
    3. Bloomberg Terminal
    4. Tushare (waditu/tushare)
    5. Pandas (pandas-dev/pandas)
    6. Matplotlib (matplotlib/matplotlib)
    7. Seaborn (mwaskom/seaborn)
    8. Plotly (plotly/plotly.py)
    9. East Money Choice (东方财富Choice数据)
    10. TradingView
    11. yfinance (ranaroussi/yfinance)
    12. Quandl
    13. Scikit-learn (scikit-learn/scikit-learn)
    14. Prophet (facebook/prophet)
    15. CEIC Data

    AI recommended 15 alternatives but never named zwq2018/Data-Copilot. 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 zwq2018/Data-Copilot?
    pass
    AI named zwq2018/Data-Copilot explicitly

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

  • If a team adopts zwq2018/Data-Copilot in production, what risks or prerequisites should they evaluate first?
    pass
    AI named zwq2018/Data-Copilot 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 zwq2018/Data-Copilot solve, and who is the primary audience?
    pass
    AI named zwq2018/Data-Copilot explicitly

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

Embed your GEO score

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MARKDOWN (README)
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  • Brand-free category queries5 vs 2 in Lite
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