RRepoGEO

REPOGEO REPORT · LITE

meta-soul/MetaSpore

Default branch main · commit f72e73bf · scanned 6/14/2026, 8:37:00 PM

GitHub: 545 stars · 95 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 meta-soul/MetaSpore, 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 H1 to specify core focus

    Why:

    CURRENT
    # MetaSpore: One-stop machine learning development platform
    COPY-PASTE FIX
    # MetaSpore: End-to-End Platform for Large-Scale Recommender Systems and Advanced Deep Learning
  • mediumtopics#2
    Add specific topics to improve categorization

    Why:

    CURRENT
    abtesting, ai, deeplearning, machinelearning, serving, training
    COPY-PASTE FIX
    abtesting, ai, deeplearning, machinelearning, serving, training, recommender-systems, multi-task-learning, graph-neural-networks, mlops
  • mediumhomepage#3
    Add a homepage URL to the repository's 'About' section

    Why:

    COPY-PASTE FIX
    [Insert official project homepage URL here, e.g., https://metaspore.ai]

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 meta-soul/MetaSpore
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Databricks Lakehouse Platform
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Databricks Lakehouse Platform · recommended 2×
  2. Amazon SageMaker · recommended 2×
  3. Google Cloud Vertex AI · recommended 2×
  4. Azure Machine Learning · recommended 2×
  5. MLflow · recommended 1×
  • CATEGORY QUERY
    What platforms offer end-to-end machine learning lifecycle management with A/B testing capabilities?
    you: not recommended
    AI recommended (in order):
    1. Databricks Lakehouse Platform
    2. Amazon SageMaker
    3. Google Cloud Vertex AI
    4. Azure Machine Learning
    5. MLflow
    6. Domino Data Lab
    7. DataRobot

    AI recommended 7 alternatives but never named meta-soul/MetaSpore. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a platform for distributed deep learning training and fast online model serving with PySpark.
    you: not recommended
    AI recommended (in order):
    1. Databricks Lakehouse Platform
    2. MLflow (mlflow/mlflow)
    3. Delta Lake (delta-io/delta)
    4. Amazon SageMaker
    5. Amazon EMR
    6. AWS Glue
    7. Google Cloud Vertex AI
    8. Google Cloud Dataproc
    9. Dataflow
    10. Azure Machine Learning
    11. Azure Synapse Analytics
    12. Azure Databricks
    13. Kubeflow (kubeflow/kubeflow)
    14. KServe (kserve/kserve)
    15. Spark on Kubernetes
    16. Apache Spark (apache/spark)

    AI recommended 16 alternatives but never named meta-soul/MetaSpore. 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 meta-soul/MetaSpore?
    pass
    AI named meta-soul/MetaSpore explicitly

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

  • If a team adopts meta-soul/MetaSpore in production, what risks or prerequisites should they evaluate first?
    pass
    AI named meta-soul/MetaSpore 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 meta-soul/MetaSpore solve, and who is the primary audience?
    pass
    AI named meta-soul/MetaSpore 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 meta-soul/MetaSpore. 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)
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HTML
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