RRepoGEO

REPOGEO REPORT · LITE

amphi-ai/amphi-etl

Default branch main · commit 6ade7b8f · scanned 5/29/2026, 4:48:10 PM

GitHub: 1,362 stars · 105 forks

AI VISIBILITY SCORE
33 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 amphi-ai/amphi-etl, 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 statement to clarify its unique offering

    Why:

    CURRENT
    Visual Data Preparation Powered by Python
Simple, intutive and easy to use with AI.
    COPY-PASTE FIX
    Amphi ETL is a visual data preparation tool for Python, designed to simplify ETL workflows directly within JupyterLab. It empowers data scientists and ML engineers to intuitively transform unstructured data into structured formats, leveraging AI for enhanced efficiency.
  • mediumtopics#2
    Add more specific topics for better categorization

    Why:

    CURRENT
    analytics-automation, data, data-analysis, data-pipelines, data-preparation, data-science, datatransformation, etl, structured-data, unstructured-data
    COPY-PASTE FIX
    analytics-automation, data, data-analysis, data-pipelines, data-preparation, data-science, datatransformation, etl, structured-data, unstructured-data, jupyterlab-extension, visual-etl, low-code-data-prep, ai-data-transformation
  • lowlicense#3
    Clarify the project's license in the README

    Why:

    COPY-PASTE FIX
    Add a section or sentence to the README, e.g., '## License
Amphi ETL is released under [SPECIFY LICENSE NAME(S) HERE]. Please refer to the [LICENSE file](LICENSE) for full details.'

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 amphi-ai/amphi-etl
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. Delta Live Tables · recommended 2×
  3. PrefectHQ/prefect · recommended 2×
  4. KNIME Analytics Platform · recommended 1×
  5. Databricks Workflows · recommended 1×
  • CATEGORY QUERY
    What are good visual data preparation tools for Python to simplify ETL workflows?
    you: not recommended
    AI recommended (in order):
    1. KNIME Analytics Platform
    2. Databricks Lakehouse Platform
    3. Databricks Workflows
    4. Delta Live Tables
    5. Apache Airflow (apache/airflow)
    6. Astronomer
    7. Google Cloud Composer
    8. Prefect (PrefectHQ/prefect)
    9. Prefect Cloud
    10. Prefect Server
    11. Meltano (meltano/meltano)
    12. dbt (Data Build Tool) (dbt-labs/dbt-core)
    13. Streamlit (streamlit/streamlit)

    AI recommended 13 alternatives but never named amphi-ai/amphi-etl. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for AI-powered tools to build data pipelines and transform data in JupyterLab.
    you: not recommended
    AI recommended (in order):
    1. Databricks Lakehouse Platform
    2. Databricks SQL
    3. Delta Live Tables
    4. Google Cloud Vertex AI Workbench
    5. Dataflow
    6. BigQuery ML
    7. Amazon SageMaker Studio
    8. AWS Glue
    9. Amazon EMR
    10. SageMaker Data Wrangler
    11. Microsoft Azure Synapse Analytics
    12. Azure Machine Learning
    13. Kedro (kedro-org/kedro)
    14. Prefect (PrefectHQ/prefect)
    15. Dagster (dagster-io/dagster)
    16. Apache Spark (apache/spark)
    17. MLlib
    18. Koalas (databricks/koalas)
    19. Pandas API on Spark

    AI recommended 19 alternatives but never named amphi-ai/amphi-etl. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 amphi-ai/amphi-etl?
    pass
    AI did not name amphi-ai/amphi-etl — 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 amphi-ai/amphi-etl in production, what risks or prerequisites should they evaluate first?
    pass
    AI named amphi-ai/amphi-etl 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 amphi-ai/amphi-etl solve, and who is the primary audience?
    pass
    AI named amphi-ai/amphi-etl 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 amphi-ai/amphi-etl. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/amphi-ai/amphi-etl.svg)](https://repogeo.com/en/r/amphi-ai/amphi-etl)
HTML
<a href="https://repogeo.com/en/r/amphi-ai/amphi-etl"><img src="https://repogeo.com/badge/amphi-ai/amphi-etl.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

amphi-ai/amphi-etl — 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