RRepoGEO

REPOGEO REPORT · LITE

xorq-labs/xorq

Default branch main · commit 8f9de3ce · scanned 6/3/2026, 12:46:46 AM

GitHub: 510 stars · 28 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 xorq-labs/xorq, 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 MLOps category

    Why:

    CURRENT
    Xorq is an executable memory system for tabular data work. Xorq gives agents a catalog of executable pipelines instead of markdown notes.
    COPY-PASTE FIX
    Xorq is an MLOps platform and executable memory system for tabular data work. It transforms ephemeral agent work like pandas scripts and sklearn pipelines into durable, composable, executable artifacts.
  • mediumtopics#2
    Add MLOps and data orchestration specific topics

    Why:

    CURRENT
    arrow, data-pipeline, data-transformations, dataframe, machine-learning, multi-engine, python, sklearn, sql
    COPY-PASTE FIX
    arrow, data-pipeline, data-transformations, dataframe, machine-learning, multi-engine, python, sklearn, sql, mlops, data-orchestration, reproducible-ml, data-catalog
  • lowreadme#3
    Explicitly mention 'ML pipelines' and 'data scripts' in the problem statement

    Why:

    CURRENT
    Coding agents are great at accomplishing closed-loop task but in the process accumulate tech-debt and unnecessary complexity. For example, if you ask a coding agent to build a dashboard, you are more likely than not to get a folder of one-off Python scripts that import each other in non-obvious ways, an embedded JSON holding intermediate state, and a `requirements.txt` that was last regenerated two sessions ago.
    COPY-PASTE FIX
    Coding agents are great at accomplishing closed-loop tasks, but in the process accumulate tech-debt and unnecessary complexity, especially with ad-hoc data scripts and ML pipelines. For example, if you ask a coding agent to build a dashboard, you are more likely than not to get a folder of one-off Python scripts that import each other in non-obvious ways, an embedded JSON holding intermediate state, and a `requirements.txt` that was last regenerated two sessions ago.

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 xorq-labs/xorq
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Prefect
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Prefect · recommended 2×
  2. Apache Airflow · recommended 2×
  3. Kedro · recommended 2×
  4. MLflow · recommended 1×
  5. Metaflow · recommended 1×
  • CATEGORY QUERY
    How to make ad-hoc data scripts and ML pipelines durable and reproducible?
    you: not recommended
    AI recommended (in order):
    1. MLflow
    2. Metaflow
    3. DVC
    4. Prefect
    5. Apache Airflow
    6. Kedro
    7. Pachyderm

    AI recommended 7 alternatives but never named xorq-labs/xorq. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tool to catalog and execute composable data transformation pipelines for tabular data?
    you: not recommended
    AI recommended (in order):
    1. Apache Airflow
    2. Prefect
    3. Dagster
    4. Luigi
    5. Mage
    6. dbt
    7. Kedro

    AI recommended 7 alternatives but never named xorq-labs/xorq. 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 xorq-labs/xorq?
    pass
    AI named xorq-labs/xorq explicitly

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

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

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

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite