RRepoGEO

REPOGEO REPORT · LITE

uqfoundation/pathos

Default branch master · commit aea34467 · scanned 6/22/2026, 12:32:11 AM

GitHub: 1,485 stars · 88 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
47 /100
Critical
Category recall
1 / 2
Avg rank #6.0 when recommended
Rule findings
1 pass · 1 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 uqfoundation/pathos, 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 H1 and opening paragraph to emphasize distributed computing

    Why:

    CURRENT
    pathos
parallel graph management and execution in heterogeneous computing

About the Pathos Framework
``pathos`` is a framework for heterogeneous computing. It provides a consistent
high-level interface for configuring and launching parallel computations
across heterogeneous resources.
    COPY-PASTE FIX
    # pathos: A Framework for Distributed and Heterogeneous Computing

``pathos`` is a comprehensive framework designed for robust parallel graph management and execution across diverse, heterogeneous computing resources. It provides a consistent high-level interface for configuring and launching complex distributed computations, enabling seamless scaling of Python functions.
  • hightopics#2
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    python, parallel-computing, distributed-computing, heterogeneous-computing, map-reduce, scientific-computing, framework, serialization
  • mediumreadme#3
    Clarify the project's license in the README

    Why:

    COPY-PASTE FIX
    Add a section or sentence like: "## License\nPathos is licensed under [insert specific license name(s) here, e.g., a custom license combining X and Y]. Please refer to the `LICENSE` file in the repository for complete 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
1 / 2
50% of queries surface uqfoundation/pathos
Avg rank
#6.0
Lower is better. #1 = top recommendation.
Share of voice
8%
Of all named tools, what % are you?
Top rival
joblib/joblib
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. joblib/joblib · recommended 1×
  2. multiprocessing.Pool · recommended 1×
  3. concurrent.futures.ProcessPoolExecutor · recommended 1×
  4. dask/dask · recommended 1×
  5. ray-project/ray · recommended 1×
  • CATEGORY QUERY
    How can I easily parallelize existing Python code using drop-in map/apply replacements?
    you: #6
    AI recommended (in order):
    1. joblib (joblib/joblib)
    2. multiprocessing.Pool
    3. concurrent.futures.ProcessPoolExecutor
    4. Dask (dask/dask)
    5. Ray (ray-project/ray)
    6. pathos (uqfoundation/pathos) ← you
    Show full AI answer
  • CATEGORY QUERY
    What tools enable distributed execution of Python functions across heterogeneous computing resources?
    you: not recommended
    AI recommended (in order):
    1. Ray
    2. Dask
    3. Apache Spark
    4. Celery
    5. Prefect
    6. Apache Airflow

    AI recommended 6 alternatives but never named uqfoundation/pathos. 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 uqfoundation/pathos?
    pass
    AI did not name uqfoundation/pathos — 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 uqfoundation/pathos in production, what risks or prerequisites should they evaluate first?
    pass
    AI named uqfoundation/pathos 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 uqfoundation/pathos solve, and who is the primary audience?
    pass
    AI named uqfoundation/pathos explicitly

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

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uqfoundation/pathos — 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