RRepoGEO

REPOGEO REPORT · LITE

huybery/Awesome-Code-LLM

Default branch main · commit 0ce4d7f2 · scanned 6/25/2026, 5:57:59 PM

GitHub: 1,284 stars · 73 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)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
28 /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
2 / 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
    Add a clear introductory sentence to the README

    Why:

    COPY-PASTE FIX
    This repository serves as a comprehensive and curated list of the best Code Large Language Models (LLMs) and related resources for researchers and developers.
  • mediumtopics#2
    Expand repository topics to include evaluation and research

    Why:

    CURRENT
    awesome, code-generation, large-language-models
    COPY-PASTE FIX
    awesome, code-generation, large-language-models, llm-evaluation, llm-research, software-development
  • mediumhomepage#3
    Add the repository URL as the homepage

    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. GPT-4 · recommended 2×
  3. Claude 3 Opus · recommended 1×
  4. Google Gemini 1.5 Pro · recommended 1×
  5. Code Llama · recommended 1×
  • CATEGORY QUERY
    What are the most effective large language models for software development and code generation?
    you: not recommended
    AI recommended (in order):
    1. GitHub Copilot
    2. GPT-4
    3. Claude 3 Opus
    4. Google Gemini 1.5 Pro
    5. Code Llama
    6. Tabnine
    7. DeepMind AlphaCode 2

    AI recommended 7 alternatives but never named huybery/Awesome-Code-LLM. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    I need resources to compare and evaluate various large language models for coding tasks.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Leaderboard
    2. GitHub Copilot
    3. Copilot Enterprise
    4. Gemini Code Assist
    5. AlphaCode 2
    6. Claude 3
    7. Anthropic API
    8. Amazon Bedrock
    9. GPT-4
    10. GPT-3.5 Turbo
    11. OpenAI API

    AI recommended 11 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 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?

  • 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.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite