RRepoGEO

REPOGEO REPORT · LITE

CLUEbenchmark/SuperCLUE

Default branch main · commit e734665d · scanned 5/23/2026, 2:38:02 PM

GitHub: 3,291 stars · 109 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 CLUEbenchmark/SuperCLUE, 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
  • highlicense#1
    Add an MIT License file

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the root of the repository with the text of the MIT License.
  • mediumtopics#2
    Expand repository topics for better categorization

    Why:

    CURRENT
    ["chatgpt", "chinese", "evaluation", "foundation-models", "gpt-4"]
    COPY-PASTE FIX
    ["chatgpt", "chinese", "evaluation", "foundation-models", "gpt-4", "benchmark", "llm", "ai-agent", "safety-evaluation"]
  • mediumreadme#3
    Clarify the README's opening statement

    Why:

    CURRENT
    # SuperCLUE

中文通用大模型综合性基准SuperCLUE
    COPY-PASTE FIX
    # SuperCLUE: 中文通用大模型综合性基准 | A Comprehensive Benchmark for Chinese Foundation Models

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 CLUEbenchmark/SuperCLUE
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
C-Eval
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. C-Eval · recommended 2×
  2. CMMLU · recommended 2×
  3. GAOKAO-Bench · recommended 2×
  4. AGIEval · recommended 2×
  5. SafetyBench · recommended 2×
  • CATEGORY QUERY
    How can I comprehensively evaluate the performance of Chinese large language models?
    you: not recommended
    AI recommended (in order):
    1. C-Eval
    2. CMMLU
    3. GAOKAO-Bench
    4. AGIEval
    5. CMRC 2018
    6. DRCD
    7. DuReader
    8. LCSTS
    9. CSL
    10. BLEU
    11. ROUGE
    12. DuConv
    13. LCCC
    14. ChnSentiCorp
    15. MSRA-NER
    16. FEVER
    17. SafetyBench
    18. CAIS
    19. Hugging Face Transformers
    20. OpenCompass
    21. LangChain
    22. LlamaIndex
    23. pandas
    24. numpy
    25. scikit-learn

    AI recommended 25 alternatives but never named CLUEbenchmark/SuperCLUE. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best benchmarks for assessing Chinese AI agent capabilities and safety?
    you: not recommended
    AI recommended (in order):
    1. C-Eval
    2. GAOKAO-Bench
    3. AGIEval
    4. SafetyBench
    5. CMMLU
    6. SuperGLUE-zh
    7. CLUE

    AI recommended 7 alternatives but never named CLUEbenchmark/SuperCLUE. 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 CLUEbenchmark/SuperCLUE?
    pass
    AI named CLUEbenchmark/SuperCLUE explicitly

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

  • If a team adopts CLUEbenchmark/SuperCLUE in production, what risks or prerequisites should they evaluate first?
    pass
    AI named CLUEbenchmark/SuperCLUE 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 CLUEbenchmark/SuperCLUE solve, and who is the primary audience?
    pass
    AI named CLUEbenchmark/SuperCLUE 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 CLUEbenchmark/SuperCLUE. 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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CLUEbenchmark/SuperCLUE — 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