RRepoGEO

REPOGEO REPORT · LITE

codefuse-ai/Awesome-Code-LLM

Default branch main · commit abde59fd · scanned 5/28/2026, 9:18:30 AM

GitHub: 3,362 stars · 238 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 codefuse-ai/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
    Reposition README H1 to clarify it's a curated list, not just *their* survey

    Why:

    CURRENT
    This is the repo for our TMLR code LLM survey. If you find this repo helpful, please support us by citing:
    COPY-PASTE FIX
    This repository is a comprehensive, curated list of language modeling research for code and software engineering activities, including related datasets. It accompanies our TMLR survey. If you find this repo helpful, please support us by citing:
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Choose and add a standard open-source license file (e.g., MIT, Apache-2.0) to the repository root.
  • mediumtopics#3
    Add 'awesome-list' and 'curated-list' to topics

    Why:

    CURRENT
    ai, awesome, datasets, llm, nlp, papers, software-engineering, survey, tmlr
    COPY-PASTE FIX
    ai, awesome, awesome-list, curated-list, datasets, llm, nlp, papers, software-engineering, survey, tmlr

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 codefuse-ai/Awesome-Code-LLM
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
arXiv.org
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. arXiv.org · recommended 1×
  2. ACM Computing Surveys · recommended 1×
  3. IEEE Transactions on Software Engineering (TSE) · recommended 1×
  4. Journal of Systems and Software · recommended 1×
  5. Google Scholar · recommended 1×
  • CATEGORY QUERY
    Where can I find a comprehensive survey of recent LLM research for software engineering?
    you: not recommended
    AI recommended (in order):
    1. arXiv.org
    2. ACM Computing Surveys
    3. IEEE Transactions on Software Engineering (TSE)
    4. Journal of Systems and Software
    5. Google Scholar
    6. Semantic Scholar
    7. GitHub Repositories
    8. Medium
    9. Towards Data Science
    10. Analytics Vidhya

    AI recommended 10 alternatives but never named codefuse-ai/Awesome-Code-LLM. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the key datasets and benchmarks used in code intelligence AI models?
    you: not recommended
    AI recommended (in order):
    1. CodeSearchNet
    2. GitHub Code LLaMA Dataset
    3. The Stack
    4. BigQuery Public Datasets
    5. HumanEval
    6. MBPP
    7. Defects4J
    8. OWASP Top 10
    9. SARD
    10. Juliet Test Suite
    11. CoNaLa

    AI recommended 11 alternatives but never named codefuse-ai/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 codefuse-ai/Awesome-Code-LLM?
    pass
    AI did not name codefuse-ai/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 codefuse-ai/Awesome-Code-LLM in production, what risks or prerequisites should they evaluate first?
    pass
    AI named codefuse-ai/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 codefuse-ai/Awesome-Code-LLM solve, and who is the primary audience?
    pass
    AI did not name codefuse-ai/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?

Embed your GEO score

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MARKDOWN (README)
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codefuse-ai/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