RRepoGEO

REPOGEO REPORT · LITE

skygazer42/DL-Hub

Default branch main · commit 9b3c3951 · scanned 5/14/2026, 11:13:00 PM

GitHub: 1,097 stars · 58 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 skygazer42/DL-Hub, 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 H1 to emphasize practical projects and LLMs

    Why:

    CURRENT
    # DL-Hub **从零手写，循序渐进 — PyTorch 深度学习统一学习项目**
    COPY-PASTE FIX
    # DL-Hub: 从零手写，循序渐进 — 300+ PyTorch 深度学习与大模型项目实战合集
  • highlicense#2
    Add a LICENSE file to the repository root

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with a standard open-source license (e.g., MIT, Apache-2.0, GPL-3.0) that aligns with the project's intent.
  • mediumreadme#3
    Add a 'Why DL-Hub?' section highlighting its unique value

    Why:

    COPY-PASTE FIX
    Add a new section, perhaps titled 'Why DL-Hub?' or '核心优势 (Core Advantages)', immediately after the H1, explicitly stating its value as a unified, hands-on, directly hosted collection of practical projects and learning tracks, contrasting it with external link lists or fragmented resources.

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 skygazer42/DL-Hub
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Towards Data Science
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Towards Data Science · recommended 1×
  2. Kaggle Notebooks · recommended 1×
  3. PyTorch Examples · recommended 1×
  4. TensorFlow Tutorials · recommended 1×
  5. Hugging Face · recommended 1×
  • CATEGORY QUERY
    Where can I find step-by-step deep learning and LLM project implementations?
    you: not recommended
    AI recommended (in order):
    1. Towards Data Science
    2. Kaggle Notebooks
    3. PyTorch Examples
    4. TensorFlow Tutorials
    5. Hugging Face
    6. transformers
    7. freeCodeCamp.org
    8. Analytics Vidhya
    9. Krish Naik
    10. sentdex

    AI recommended 10 alternatives but never named skygazer42/DL-Hub. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a unified learning project with practical examples for various machine learning algorithms.
    you: not recommended
    AI recommended (in order):
    1. scikit-learn
    2. TensorFlow/Keras
    3. PyTorch
    4. XGBoost
    5. LightGBM
    6. CatBoost
    7. MLflow

    AI recommended 7 alternatives but never named skygazer42/DL-Hub. 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 skygazer42/DL-Hub?
    pass
    AI named skygazer42/DL-Hub explicitly

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

  • If a team adopts skygazer42/DL-Hub in production, what risks or prerequisites should they evaluate first?
    pass
    AI named skygazer42/DL-Hub 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 skygazer42/DL-Hub solve, and who is the primary audience?
    pass
    AI named skygazer42/DL-Hub explicitly

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

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skygazer42/DL-Hub — 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