RRepoGEO

REPOGEO REPORT · LITE

UpstageAI/dataverse

Default branch main · commit a0adedc3 · scanned 6/8/2026, 1:12:49 AM

GitHub: 564 stars · 58 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 UpstageAI/dataverse, 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 LLM data ETL

    Why:

    CURRENT
    The Universe of Data. All about Data, Data Science, and Data Engineering.
    COPY-PASTE FIX
    Dataverse is a Python framework for building and managing high-quality ETL pipelines, specifically designed to simplify data preprocessing for Large Language Models (LLMs) and make Spark accessible to all.
  • hightopics#2
    Add specific topics for LLM data ETL and Spark

    Why:

    COPY-PASTE FIX
    python, etl, llm, data-engineering, data-science, spark, data-preprocessing, data-management
  • mediumabout#3
    Update the repository description to highlight LLM data processing

    Why:

    CURRENT
    The Universe of Data. All about data, data science, and data engineering
    COPY-PASTE FIX
    A Python framework for ETL pipelines, simplifying data preprocessing and management for Large Language Models (LLMs) with easy Spark integration.

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 UpstageAI/dataverse
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Pandas
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Pandas · recommended 2×
  2. Polars · recommended 2×
  3. Dask · recommended 2×
  4. Apache Spark (PySpark) · recommended 1×
  5. Apache Airflow · recommended 1×
  • CATEGORY QUERY
    What are good Python libraries for building ETL pipelines for large language model data?
    you: not recommended
    AI recommended (in order):
    1. Apache Spark (PySpark)
    2. Pandas
    3. Polars
    4. Dask
    5. Apache Airflow
    6. Prefect
    7. Dagster
    8. LangChain
    9. LlamaIndex
    10. SQLAlchemy
    11. Psycopg2
    12. PyMongo
    13. Requests
    14. BeautifulSoup4
    15. Scrapy
    16. Hugging Face Datasets

    AI recommended 16 alternatives but never named UpstageAI/dataverse. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I simplify data preprocessing and management for AI without complex Spark knowledge?
    you: not recommended
    AI recommended (in order):
    1. Pandas
    2. Polars
    3. Dask
    4. Google BigQuery
    5. Snowflake
    6. MindsDB

    AI recommended 6 alternatives but never named UpstageAI/dataverse. 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 UpstageAI/dataverse?
    pass
    AI named UpstageAI/dataverse explicitly

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

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

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

Embed your GEO score

Drop this badge into the README of UpstageAI/dataverse. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/UpstageAI/dataverse.svg)](https://repogeo.com/en/r/UpstageAI/dataverse)
HTML
<a href="https://repogeo.com/en/r/UpstageAI/dataverse"><img src="https://repogeo.com/badge/UpstageAI/dataverse.svg" alt="RepoGEO" /></a>
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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite
UpstageAI/dataverse — RepoGEO report