RRepoGEO

REPOGEO REPORT · LITE

amaiya/ktrain

Default branch master · commit fdbeda6e · scanned 5/29/2026, 9:31:56 PM

GitHub: 1,266 stars · 260 forks

AI VISIBILITY SCORE
35 /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
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 amaiya/ktrain, 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's main heading to emphasize simplified deep learning

    Why:

    CURRENT
    # Welcome to ktrain > a 'Swiss Army knife' for machine learning
    COPY-PASTE FIX
    # ktrain: A High-Level Python Library for Simplified Deep Learning and AI
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    Set the repository's homepage URL in the 'About' section to the official project website or documentation link (e.g., https://ktrain.readthedocs.io/).
  • lowreadme#3
    Add a 'Why ktrain?' or 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section to the README, perhaps titled 'Why ktrain?' or 'Comparison to other libraries', that clearly articulates its unique benefits and how it stands out from alternatives like Fastai or PyTorch Lightning.

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 amaiya/ktrain
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
keras-team/keras
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. keras-team/keras · recommended 1×
  2. Lightning-AI/lightning · recommended 1×
  3. fastai/fastai · recommended 1×
  4. huggingface/transformers · recommended 1×
  5. tensorflow/tensorflow · recommended 1×
  • CATEGORY QUERY
    How can I quickly build and train deep learning models without extensive boilerplate code?
    you: not recommended
    AI recommended (in order):
    1. Keras (keras-team/keras)
    2. PyTorch Lightning (Lightning-AI/lightning)
    3. Fastai (fastai/fastai)
    4. Hugging Face Transformers (huggingface/transformers)
    5. TensorFlow Keras (tensorflow/tensorflow)
    6. JAX (google/jax)
    7. Flax (google/flax)
    8. Haiku (deepmind/dm-haiku)

    AI recommended 8 alternatives but never named amaiya/ktrain. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a Python library to simplify common machine learning tasks across NLP and computer vision.
    you: not recommended
    AI recommended (in order):
    1. scikit-learn
    2. Keras
    3. Hugging Face Transformers
    4. PyTorch Lightning
    5. fastai
    6. spaCy

    AI recommended 6 alternatives but never named amaiya/ktrain. 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 amaiya/ktrain?
    pass
    AI named amaiya/ktrain explicitly

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

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