RRepoGEO

REPOGEO REPORT · LITE

signerless/llm-checker

Default branch main · commit 238b0df2 · scanned 7/1/2026, 3:41:43 AM

GitHub: 2,765 stars · 182 forks

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 signerless/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 opening to explicitly state the tool's core function

    Why:

    CURRENT
    # LLM Checker

**Intelligent Ollama Model Selector**

AI-powered CLI that analyzes your hardware and recommends optimal LLM models.
    COPY-PASTE FIX
    # LLM Checker

**The CLI for Local LLM Hardware Compatibility & Model Recommendation**

AI-powered CLI that analyzes your hardware and recommends optimal LLM models you can run locally, with full Ollama integration.
  • hightopics#2
    Add specific topics to improve categorization

    Why:

    COPY-PASTE FIX
    llm, ollama, local-llm, hardware-compatibility, model-recommendation, cli, gpu, cpu, vram, quantization
  • mediumreadme#3
    Clarify the project's license in the README

    Why:

    COPY-PASTE FIX
    ## License

This project is licensed under the terms specified in the [LICENSE](LICENSE) file. It is a custom license, please review the file for full details.

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 signerless/llm-checker
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Ollama
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Ollama · recommended 2×
  2. LM Studio · recommended 2×
  3. TinyLlama 1.1B · recommended 1×
  4. Phi-2 · recommended 1×
  5. Mistral 7B · recommended 1×
  • CATEGORY QUERY
    What local LLM models can my computer hardware effectively run?
    you: not recommended
    AI recommended (in order):
    1. TinyLlama 1.1B
    2. Phi-2
    3. Mistral 7B
    4. OpenHermes 2.5 Mistral 7B
    5. Zephyr 7B Beta
    6. Llama 2 13B
    7. Nous Hermes 2 Mixtral 8x7B
    8. OpenHermes 2.5 Mixtral 8x7B
    9. Llama 2 70B
    10. Mixtral 8x7B
    11. Yi-34B
    12. Ollama
    13. LM Studio
    14. Jan AI
    15. text-generation-webui
    16. llama.cpp

    AI recommended 16 alternatives but never named signerless/llm-checker. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tool to discover optimal local LLM models compatible with my system and Ollama?
    you: not recommended
    AI recommended (in order):
    1. Ollama
    2. LM Studio
    3. Hugging Face Hub
    4. Jan
    5. LocalLLaMA Subreddit

    AI recommended 5 alternatives but never named signerless/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 signerless/llm-checker?
    pass
    AI named signerless/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 signerless/llm-checker in production, what risks or prerequisites should they evaluate first?
    pass
    AI named signerless/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 signerless/llm-checker solve, and who is the primary audience?
    pass
    AI named signerless/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

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

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MARKDOWN (README)
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HTML
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signerless/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
signerless/llm-checker — RepoGEO report