RRepoGEO

REPOGEO REPORT · LITE

apache/hamilton

Default branch main · commit 21a4a96f · scanned 5/27/2026, 4:26:25 PM

GitHub: 2,496 stars · 189 forks

AI VISIBILITY SCORE
40 /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
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 apache/hamilton, 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
    Move core value proposition above incubation disclaimer in README

    Why:

    COPY-PASTE FIX
    Apache Hamilton is a lightweight Python library for defining testable, modular, self-documenting dataflows and data transformations. It automatically infers lineage and metadata from standard Python functions, making your data pipelines robust, scalable, and portable. Unlike broader orchestration tools, Hamilton focuses on the *logic* of data transformations, enabling clear separation of concerns.
  • hightopics#2
    Add specific topics to clarify data transformation focus

    Why:

    CURRENT
    dag, data-analysis, data-engineering, data-science, dataframe, etl, etl-framework, etl-pipeline, feature-engineering, hacktoberfest, lineage, llmops, machine-learning, mlops, orchestration, pandas, python, rag, software-engineering
    COPY-PASTE FIX
    dag, data-analysis, data-engineering, data-science, dataframe, etl, etl-framework, etl-pipeline, feature-engineering, hacktoberfest, lineage, llmops, machine-learning, mlops, orchestration, pandas, python, rag, software-engineering, dataflow-definition, data-transformation-framework, python-data-pipelines, declarative-dataflows, data-lineage-automation
  • mediumabout#3
    Refine repository description to emphasize transformation and Pythonic approach

    Why:

    CURRENT
    Apache Hamilton helps data scientists and engineers define testable, modular, self-documenting dataflows, that encode lineage/tracing and metadata. Runs and scales everywhere python does.
    COPY-PASTE FIX
    Apache Hamilton is a lightweight Python framework for defining testable, modular, self-documenting dataflows and data transformations. It automatically infers lineage and metadata from standard Python functions, enabling robust and scalable data pipelines without complex orchestration logic.

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 apache/hamilton
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
apache/airflow
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. apache/airflow · recommended 2×
  2. PrefectHQ/prefect · recommended 2×
  3. dagster-io/dagster · recommended 2×
  4. kedro-org/kedro · recommended 2×
  5. open-metadata/OpenMetadata · recommended 1×
  • CATEGORY QUERY
    How can I build modular, testable Python dataflows with automatic lineage tracking?
    you: not recommended
    AI recommended (in order):
    1. Apache Airflow (apache/airflow)
    2. OpenMetadata (open-metadata/OpenMetadata)
    3. DataHub (datahub-project/datahub)
    4. Prefect (PrefectHQ/prefect)
    5. Prefect Cloud
    6. Dagster (dagster-io/dagster)
    7. Kedro (kedro-org/kedro)
    8. Mage (mage-ai/mage-ai)
    9. dbt (dbt-labs/dbt-core)

    AI recommended 9 alternatives but never named apache/hamilton. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are good Python tools for orchestrating complex data transformation pipelines for ML?
    you: not recommended
    AI recommended (in order):
    1. Apache Airflow (apache/airflow)
    2. Prefect (PrefectHQ/prefect)
    3. Dagster (dagster-io/dagster)
    4. Kedro (kedro-org/kedro)
    5. Luigi (spotify/luigi)
    6. Metaflow (Netflix/metaflow)

    AI recommended 6 alternatives but never named apache/hamilton. 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 apache/hamilton?
    pass
    AI named apache/hamilton explicitly

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

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