RRepoGEO

REPOGEO REPORT · LITE

trananhkma/fucking-awesome-python

Default branch master · commit 38064a12 · scanned 5/21/2026, 5:37:52 AM

GitHub: 2,067 stars · 254 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
28 /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
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 trananhkma/fucking-awesome-python, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highabout#1
    Update repository description to highlight unique features

    Why:

    CURRENT
    awesome-python with :octocat: :star: and :fork_and_knife:
    COPY-PASTE FIX
    A curated list of awesome Python resources, enhanced with GitHub stars and forks statistics, based on the original awesome-python.
  • mediumhomepage#2
    Set the repository homepage URL

    Why:

    COPY-PASTE FIX
    https://github.com/trananhkma/fucking-awesome-python

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 trananhkma/fucking-awesome-python
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Awesome Python
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Awesome Python · recommended 1×
  2. PyPI (Python Package Index) · recommended 1×
  3. Real Python · recommended 1×
  4. Full Stack Python · recommended 1×
  5. Towards Data Science (on Medium) · recommended 1×
  • CATEGORY QUERY
    Where can I find a curated list of essential Python libraries and frameworks?
    you: not recommended
    AI recommended (in order):
    1. Awesome Python
    2. PyPI (Python Package Index)
    3. Real Python
    4. Full Stack Python
    5. Towards Data Science (on Medium)
    6. ThoughtWorks Technology Radar
    7. Python for Beginners

    AI recommended 7 alternatives but never named trananhkma/fucking-awesome-python. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the most popular and well-maintained Python packages for common development needs?
    you: not recommended
    AI recommended (in order):
    1. Django (django/django)
    2. Flask (pallets/flask)
    3. FastAPI (tiangolo/fastapi)
    4. NumPy (numpy/numpy)
    5. pandas (pandas-dev/pandas)
    6. scikit-learn (scikit-learn/scikit-learn)
    7. Matplotlib (matplotlib/matplotlib)
    8. TensorFlow (tensorflow/tensorflow)
    9. PyTorch (pytorch/pytorch)
    10. Requests (psf/requests)
    11. Beautiful Soup 4 (crummy/beautifulsoup4)
    12. Click (pallets/click)
    13. pytest (pytest-dev/pytest)
    14. Black (psf/black)
    15. Poetry (python-poetry/poetry)

    AI recommended 15 alternatives but never named trananhkma/fucking-awesome-python. 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 trananhkma/fucking-awesome-python?
    pass
    AI named trananhkma/fucking-awesome-python explicitly

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

  • If a team adopts trananhkma/fucking-awesome-python in production, what risks or prerequisites should they evaluate first?
    pass
    AI named trananhkma/fucking-awesome-python 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 trananhkma/fucking-awesome-python solve, and who is the primary audience?
    pass
    AI did not name trananhkma/fucking-awesome-python — 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?

Embed your GEO score

Drop this badge into the README of trananhkma/fucking-awesome-python. 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/trananhkma/fucking-awesome-python.svg)](https://repogeo.com/en/r/trananhkma/fucking-awesome-python)
HTML
<a href="https://repogeo.com/en/r/trananhkma/fucking-awesome-python"><img src="https://repogeo.com/badge/trananhkma/fucking-awesome-python.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

trananhkma/fucking-awesome-python — 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