RRepoGEO

REPOGEO REPORT · LITE

Pavelevich/llm-checker

Default branch main · commit 559293f9 · scanned 5/21/2026, 8:37:53 AM

GitHub: 2,214 stars · 146 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
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 Pavelevich/llm-checker, 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's main subtitle to clarify its function

    Why:

    CURRENT
    **Intelligent Ollama Model Selector**
    COPY-PASTE FIX
    **Intelligent Ollama Model Selector: Hardware Compatibility for Local LLMs**
  • hightopics#2
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    ollama, llm, sllm, cli, hardware-analysis, model-selection, local-llm, gpu, cpu, vram, memory, compatibility
  • mediumlicense#3
    Clarify the existing license in the README

    Why:

    COPY-PASTE FIX
    Add a section to the README, e.g., under 'Docs' or 'About', stating: `## License
This project uses a custom license. Please refer to the [LICENSE](LICENSE) file for full details on usage and distribution.`

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 Pavelevich/llm-checker
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Llama 3
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Llama 3 · recommended 2×
  2. ollama/ollama · recommended 1×
  3. Phi-3-mini · recommended 1×
  4. Gemma · recommended 1×
  5. Mistral · recommended 1×
  • CATEGORY QUERY
    How to determine which large language models can run efficiently on my local machine?
    you: not recommended
    AI recommended (in order):
    1. Ollama (ollama/ollama)
    2. Llama 3
    3. Phi-3-mini
    4. Gemma
    5. Mistral
    6. LM Studio
    7. Jan (janhq/jan)
    8. GGML/GGUF
    9. llama.cpp (ggerganov/llama.cpp)
    10. TinyLlama
    11. OpenHermes-2.5-Mistral-7B
    12. Zephyr-7B-beta
    13. Transformers (huggingface/transformers)
    14. bitsandbytes (TimDettmers/bitsandbytes)
    15. AWQ
    16. Llama 3
    17. Mistral-7B-Instruct-v0.2
    18. Mixtral-8x7B-Instruct-v0.1

    AI recommended 18 alternatives but never named Pavelevich/llm-checker. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tool analyzes system hardware and suggests optimal local LLM models for Ollama?
    you: not recommended
    AI recommended (in order):
    1. Ollama Itself
    2. ollama ps
    3. ollama run <model> --verbose
    4. nvidia-smi
    5. radeontop
    6. amdgpu_top
    7. htop
    8. Task Manager
    9. Hugging Face Model Cards
    10. Ollama library
    11. Ollama Discord
    12. Reddit r/LocalLLaMA

    AI recommended 12 alternatives but never named Pavelevich/llm-checker. 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 Pavelevich/llm-checker?
    pass
    AI named Pavelevich/llm-checker explicitly

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

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

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

Embed your GEO score

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MARKDOWN (README)
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Pavelevich/llm-checker — 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