RRepoGEO

REPOGEO REPORT · LITE

shyamsn97/mario-gpt

Default branch main · commit e62bb2b0 · scanned 6/30/2026, 7:37:06 PM

GitHub: 1,143 stars · 105 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
22 /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
1 / 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 shyamsn97/mario-gpt, 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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    ['mario', 'gpt', 'text-to-level', 'game-generation', 'procedural-content-generation', 'llm', 'gpt2', 'neurips-2023', 'level-design']
  • highreadme#2
    Add a concise, category-specific opening sentence to the README

    Why:

    CURRENT
    The current README starts with a title and then jumps into 'How does it work?' and architecture.
    COPY-PASTE FIX
    MarioGPT is a novel approach to **text-to-level generation for video games**, specifically Super Mario Bros., leveraging a fine-tuned GPT-2 model to create playable levels from natural language prompts. This repository provides the official code for the NeurIPS 2023 paper 'MarioGPT: Open-Ended Text2Level Generation through Large Language Models'.
  • mediumreadme#3
    Add a 'Why MarioGPT?' or 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    ## Why MarioGPT?
Unlike traditional procedural content generation methods (e.g., rule-based systems, evolutionary algorithms, or Wave Function Collapse) that often require explicit design rules or extensive parameter tuning, MarioGPT leverages the power of Large Language Models (LLMs) to generate game levels directly from natural language prompts. This allows for more intuitive, open-ended, and diverse level creation, bridging the gap between human creativity and AI-driven content generation.

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 shyamsn97/mario-gpt
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Wave Function Collapse
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Wave Function Collapse · recommended 2×
  2. TensorFlow · recommended 1×
  3. Keras · recommended 1×
  4. PyTorch · recommended 1×
  5. ML-Agents · recommended 1×
  • CATEGORY QUERY
    How can I programmatically generate video game levels using AI models?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow
    2. Keras
    3. PyTorch
    4. ML-Agents
    5. DEAP
    6. PyGAD
    7. Stable Baselines3
    8. Ray RLlib
    9. OpenAI Gym
    10. MiniZinc
    11. Clingo
    12. Wave Function Collapse

    AI recommended 12 alternatives but never named shyamsn97/mario-gpt. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools exist for generating game environments from text prompts using language models?
    you: not recommended
    AI recommended (in order):
    1. Unity
    2. Unity ML-Agents
    3. Unity Sentis
    4. OpenAI API
    5. Hugging Face Transformers
    6. MapMagic 2
    7. Gaia Pro
    8. Unreal Engine
    9. MetaHumans
    10. Procedural Content Generation (PCG) Framework
    11. Blockade Labs' Skybox AI
    12. DreamFusion
    13. Magic3D
    14. Plask
    15. Move.ai
    16. Kaedim
    17. Midjourney
    18. Stable Diffusion
    19. Scenario.gg
    20. Wave Function Collapse
    21. Marching Cubes
    22. Perlin Noise

    AI recommended 22 alternatives but never named shyamsn97/mario-gpt. 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 shyamsn97/mario-gpt?
    pass
    AI did not name shyamsn97/mario-gpt — 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 shyamsn97/mario-gpt in production, what risks or prerequisites should they evaluate first?
    pass
    AI named shyamsn97/mario-gpt 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 shyamsn97/mario-gpt solve, and who is the primary audience?
    pass
    AI did not name shyamsn97/mario-gpt — 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?

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite
shyamsn97/mario-gpt — RepoGEO report