RRepoGEO

REPOGEO REPORT · LITE

dnakov/litter

Default branch main · commit 46d888cd · scanned 5/13/2026, 12:52:53 PM

GitHub: 1,771 stars · 140 forks

AI VISIBILITY SCORE
30 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 dnakov/litter, 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
  • highabout#1
    Add a concise description to the repository's About section

    Why:

    COPY-PASTE FIX
    Native iOS + Android client for OpenAI Codex. Connect to local or remote servers, manage sessions, and run agentic coding workflows from your phone.
  • hightopics#2
    Add relevant topics to improve categorization and search

    Why:

    COPY-PASTE FIX
    ios, android, mobile-app, ai-coding, openai-codex, rust, uniffi, agentic-ai
  • mediumhomepage#3
    Add the project homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://kittylitter.app

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 dnakov/litter
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ChatGPT
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. ChatGPT · recommended 1×
  2. Microsoft Copilot · recommended 1×
  3. Google Gemini · recommended 1×
  4. Termux · recommended 1×
  5. Juno · recommended 1×
  • CATEGORY QUERY
    What mobile clients exist for interacting with AI coding models on iOS and Android?
    you: not recommended
    AI recommended (in order):
    1. ChatGPT
    2. Microsoft Copilot
    3. Google Gemini
    4. Termux
    5. Juno
    6. CodePal
    7. Mimo

    AI recommended 7 alternatives but never named dnakov/litter. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Need a way to manage AI coding sessions and run workflows from my phone.
    you: not recommended
    AI recommended (in order):
    1. GitHub Codespaces
    2. GitHub Mobile app
    3. JupyterLab (jupyterlab/jupyterlab)
    4. Google Colab
    5. Paperspace Gradient
    6. Termux (termux/termux-app)
    7. iSH Shell (ish-app/ish)
    8. AWS EC2
    9. Google Cloud Compute Engine
    10. DigitalOcean Droplet
    11. tmux (tmux/tmux)
    12. screen
    13. Replit
    14. AWS Cloud9
    15. Codeanywhere

    AI recommended 15 alternatives but never named dnakov/litter. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 dnakov/litter?
    pass
    AI named dnakov/litter explicitly

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

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

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

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dnakov/litter — 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