RRepoGEO

REPOGEO REPORT · LITE

openai/frontier-evals

Default branch main · commit 51052ced · scanned 5/19/2026, 4:12:40 AM

GitHub: 1,196 stars · 154 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 openai/frontier-evals, 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's opening statement to clarify its purpose

    Why:

    CURRENT
    # Frontier Evals

Code for evals measuring frontier model capabilities.
    COPY-PASTE FIX
    # Frontier Evals

A suite of evaluations and framework for measuring the capabilities and risks of frontier AI models on complex, real-world tasks, including research replication (PaperBench), freelance software engineering (SWE-Lancer), and smart contract security (EVMbench).
  • hightopics#2
    Add relevant topics to improve categorization

    Why:

    COPY-PASTE FIX
    ai-evaluation, frontier-ai, llm-evaluation, model-benchmarking, ai-safety, research-replication, software-engineering-evals, smart-contract-security
  • mediumabout#3
    Expand the repository description for better context

    Why:

    CURRENT
    OpenAI Frontier Evals
    COPY-PASTE FIX
    A suite of evaluations and framework for measuring the capabilities and risks of frontier AI models on complex, real-world tasks like research replication, software engineering, and smart contract security.

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/frontier-evals
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
HumanEval
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. HumanEval · recommended 1×
  2. MBPP · recommended 1×
  3. Codeforces · recommended 1×
  4. AtCoder · recommended 1×
  5. LeetCode · recommended 1×
  • CATEGORY QUERY
    Need a framework to evaluate advanced AI models on real-world coding challenges.
    you: not recommended
    AI recommended (in order):
    1. HumanEval
    2. MBPP
    3. Codeforces
    4. AtCoder
    5. LeetCode
    6. APPS
    7. CodeXGLUE

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

    Show full AI answer
  • CATEGORY QUERY
    Looking for a platform to benchmark frontier AI capabilities in research replication or security.
    you: not recommended
    AI recommended (in order):
    1. MLCommons (MLPerf)
    2. Hugging Face Datasets and Evaluate Libraries
    3. OpenAI Evals
    4. EleutherAI's LM Evaluation Harness
    5. Adversarial Robustness Toolbox (ART) by IBM
    6. Fiddler AI
    7. Microsoft Counterfit

    AI recommended 7 alternatives but never named openai/frontier-evals. 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/frontier-evals?
    pass
    AI named openai/frontier-evals 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/frontier-evals in production, what risks or prerequisites should they evaluate first?
    pass
    AI named openai/frontier-evals 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/frontier-evals solve, and who is the primary audience?
    pass
    AI named openai/frontier-evals 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/frontier-evals — 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