RRepoGEO

REPOGEO REPORT · LITE

Lux-AI-Challenge/Lux-Design-S1

Default branch master · commit ca603f4a · scanned 6/13/2026, 12:59:42 PM

GitHub: 902 stars · 146 forks

AI VISIBILITY SCORE
33 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 Lux-AI-Challenge/Lux-Design-S1, 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 clarify repo's role

    Why:

    CURRENT
    # Lux AI Challenge: Season 1
    
    The night is dark and full of terrors. Two teams must fight off the darkness, collect resources, and advance through the ages. Daytime finds a desperate rush to gather and build the resources that can carry you through the impending night. Plan and expand carefully -- any city that fails to produce enough light will be consumed by darkness.
    
    Welcome to the Lux AI Challenge Season 1!
    
    The Lux AI Challenge is a competition where competitors design agents to tackle a multi-variable optimization, resource gathering, and allocation problem in a 1v1 scenario against other competitors.
    COPY-PASTE FIX
    # Lux AI Challenge Season 1: Game Engine and Design
    
    This repository contains the official game engine, design specifications, and starter kit for the Lux AI Challenge Season 1, a competition where participants develop AI agents to navigate a complex multi-variable optimization, resource gathering, and allocation problem. The challenge involves two teams fighting off darkness, collecting resources, and advancing through ages in a 1v1 scenario.
  • mediumtopics#2
    Add more specific topics for game environments and AI

    Why:

    CURRENT
    ai, competition, hacktoberfest, lux-ai, programming, reinforcement-learning
    COPY-PASTE FIX
    ai, competition, hacktoberfest, lux-ai, programming, reinforcement-learning, game-engine, simulation, game-ai, environment
  • lowabout#3
    Clarify repository description to emphasize its function for participants

    Why:

    CURRENT
    Home to the design and engine of the @Lux-AI-Challenge Season 1, hosted on @kaggle
    COPY-PASTE FIX
    Official repository for the Lux AI Challenge Season 1, providing the game engine and design specifications for participants to develop and test their AI agents on @kaggle.

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 Lux-AI-Challenge/Lux-Design-S1
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Kaggle
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Kaggle · recommended 1×
  2. Topcoder · recommended 1×
  3. AIcrowd · recommended 1×
  4. CodinGame · recommended 1×
  5. DrivenData · recommended 1×
  • CATEGORY QUERY
    Where can I find platforms to participate in AI agent design competitions?
    you: not recommended
    AI recommended (in order):
    1. Kaggle
    2. Topcoder
    3. AIcrowd
    4. CodinGame
    5. DrivenData
    6. NeurIPS
    7. ICML
    8. ICLR

    AI recommended 8 alternatives but never named Lux-AI-Challenge/Lux-Design-S1. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are good environments for developing and testing reinforcement learning game AI agents?
    you: not recommended
    AI recommended (in order):
    1. Gymnasium
    2. Unity ML-Agents Toolkit
    3. DeepMind Lab
    4. Minigrid
    5. PettingZoo
    6. ViZDoom

    AI recommended 6 alternatives but never named Lux-AI-Challenge/Lux-Design-S1. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • 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 Lux-AI-Challenge/Lux-Design-S1?
    pass
    AI named Lux-AI-Challenge/Lux-Design-S1 explicitly

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

  • If a team adopts Lux-AI-Challenge/Lux-Design-S1 in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Lux-AI-Challenge/Lux-Design-S1 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 Lux-AI-Challenge/Lux-Design-S1 solve, and who is the primary audience?
    pass
    AI did not name Lux-AI-Challenge/Lux-Design-S1 — 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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