RRepoGEO

REPOGEO REPORT · LITE

openai/human-eval

Default branch master · commit 6d43fb98 · scanned 6/26/2026, 9:52:36 AM

GitHub: 3,274 stars · 444 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
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 openai/human-eval, 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 H1 to explicitly state this is the official HumanEval benchmark

    Why:

    CURRENT
    # HumanEval: Hand-Written Evaluation Set
    COPY-PASTE FIX
    # openai/human-eval: The Official HumanEval Benchmark for Evaluating Large Language Models Trained on Code
  • mediumtopics#2
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    llm, code-generation, benchmark, evaluation, machine-learning, artificial-intelligence, nlp
  • lowhomepage#3
    Add a homepage URL to the repository

    Why:

    COPY-PASTE FIX
    https://github.com/openai/human-eval

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 openai/human-eval
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
HumanEval
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. HumanEval · recommended 2×
  2. MBPP · recommended 2×
  3. LeetCode · recommended 2×
  4. HackerRank · recommended 2×
  5. APPS · recommended 2×
  • CATEGORY QUERY
    How can I benchmark the code generation capabilities of large language models?
    you: not recommended
    AI recommended (in order):
    1. HumanEval
    2. MBPP
    3. CodeXGLUE
    4. LeetCode
    5. HackerRank
    6. APPS
    7. CodeContests

    AI recommended 7 alternatives but never named openai/human-eval. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are good datasets and tools for evaluating AI code completion quality?
    you: not recommended
    AI recommended (in order):
    1. HumanEval
    2. MBPP
    3. CodeContests
    4. APPS
    5. BigCodeBench
    6. OpenAI's `evaluate` library
    7. Codeforces
    8. LeetCode
    9. HackerRank
    10. `pytest`
    11. `JUnit`
    12. `Go test`
    13. GumTree
    14. PMD's Copy/Paste Detector (CPD)

    AI recommended 14 alternatives but never named openai/human-eval. 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 openai/human-eval?
    pass
    AI named openai/human-eval explicitly

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

  • If a team adopts openai/human-eval in production, what risks or prerequisites should they evaluate first?
    pass
    AI named openai/human-eval 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 openai/human-eval solve, and who is the primary audience?
    pass
    AI named openai/human-eval explicitly

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

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openai/human-eval — 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