RRepoGEO

REPOGEO REPORT · LITE

huybery/Awesome-Code-LLM

Default branch main · commit 0ce4d7f2 · scanned 5/15/2026, 3:27:36 AM

GitHub: 1,285 stars · 72 forks

AI VISIBILITY SCORE
22 /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
1 / 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 huybery/Awesome-Code-LLM, 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
    Clarify the repository's identity as an 'awesome list' in the README's opening

    Why:

    COPY-PASTE FIX
    Add this sentence immediately after the H1: "This repository is a curated 'awesome list' of resources, papers, and models related to Code Large Language Models (Code LLMs), designed for researchers and developers."
  • hightopics#2
    Expand repository topics with more specific keywords

    Why:

    CURRENT
    awesome, code-generation, large-language-models
    COPY-PASTE FIX
    awesome, code-generation, large-language-models, awesome-list, llm-resources, code-llm-research, model-evaluation, benchmarks
  • mediumhomepage#3
    Add a homepage URL to the repository settings

    Why:

    COPY-PASTE FIX
    https://github.com/huybery/Awesome-Code-LLM

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 huybery/Awesome-Code-LLM
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
GitHub Copilot
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. GitHub Copilot · recommended 2×
  2. Code Llama · recommended 2×
  3. Google Gemini · recommended 1×
  4. GPT-4 · recommended 1×
  5. Amazon CodeWhisperer · recommended 1×
  • CATEGORY QUERY
    What are the top-performing large language models specifically designed for code generation?
    you: not recommended
    AI recommended (in order):
    1. GitHub Copilot
    2. Google Gemini
    3. GPT-4
    4. Code Llama
    5. Amazon CodeWhisperer
    6. StarCoder / StarCoder2

    AI recommended 6 alternatives but never named huybery/Awesome-Code-LLM. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find a comprehensive overview of LLMs suitable for code-related research?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Models
    2. Papers With Code
    3. GitHub Repositories
    4. Google AI Blog
    5. OpenAI Blog
    6. Microsoft Research Blog
    7. AlphaCode
    8. Codex
    9. GitHub Copilot
    10. Code Llama
    11. arXiv
    12. Big Code
    13. BigCode project
    14. StarCoder
    15. HumanEval
    16. MBPP
    17. CodeXGLUE

    AI recommended 17 alternatives but never named huybery/Awesome-Code-LLM. 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 huybery/Awesome-Code-LLM?
    pass
    AI did not name huybery/Awesome-Code-LLM — 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?

  • If a team adopts huybery/Awesome-Code-LLM in production, what risks or prerequisites should they evaluate first?
    pass
    AI named huybery/Awesome-Code-LLM 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 huybery/Awesome-Code-LLM solve, and who is the primary audience?
    pass
    AI did not name huybery/Awesome-Code-LLM — 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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huybery/Awesome-Code-LLM — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite