RRepoGEO

REPOGEO REPORT · LITE

dnakov/litter

Default branch main · commit abee3ace · scanned 6/23/2026, 11:51:40 PM

GitHub: 2,283 stars · 174 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
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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highabout#1
    Set the repository description

    Why:

    COPY-PASTE FIX
    Native iOS + Android client for OpenAI Codex and other AI coding assistants. Connect to local or remote servers, manage sessions, and run agentic coding workflows from your phone.
  • mediumhomepage#2
    Set the repository homepage URL

    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
GitHub Mobile
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. GitHub Mobile · recommended 1×
  2. GitHub Codespaces · recommended 1×
  3. GitHub Copilot · recommended 1×
  4. termux/termux-app · recommended 1×
  5. Code Llama · recommended 1×
  • CATEGORY QUERY
    How can I run AI coding assistant workflows directly from my mobile device?
    you: not recommended
    AI recommended (in order):
    1. GitHub Mobile
    2. GitHub Codespaces
    3. GitHub Copilot
    4. Termux (termux/termux-app)
    5. Code Llama
    6. Ollama (ollama/ollama)
    7. Juno
    8. Acode
    9. Textastic
    10. Replit
    11. Ghostwriter
    12. CodePen
    13. JSFiddle

    AI recommended 13 alternatives but never named dnakov/litter. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best mobile clients for interacting with large language models on the go?
    you: not recommended
    AI recommended (in order):
    1. ChatGPT App
    2. Copilot
    3. Google Gemini App
    4. Perplexity AI
    5. Poe
    6. Claude
    7. Nova - AI Chatbot

    AI recommended 7 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?

Embed your GEO score

Drop this badge into the README of dnakov/litter. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/dnakov/litter.svg)](https://repogeo.com/en/r/dnakov/litter)
HTML
<a href="https://repogeo.com/en/r/dnakov/litter"><img src="https://repogeo.com/badge/dnakov/litter.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

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