RRepoGEO

REPOGEO REPORT · LITE

codefuse-ai/codefuse-devops-eval

Default branch main · commit f0f12d43 · scanned 5/31/2026, 9:32:50 PM

GitHub: 654 stars · 48 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/codefuse-devops-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
  • hightopics#1
    Add specific topics to improve AI categorization

    Why:

    COPY-PASTE FIX
    llm-evaluation, devops, aiops, benchmark, dataset, foundation-models, time-series-anomaly-detection, log-parsing, tool-learning, llm-benchmark, large-language-models
  • highreadme#2
    Clarify the project's license in the README

    Why:

    COPY-PASTE FIX
    This project is licensed under [insert specific license name(s) here, e.g., Apache-2.0 and MIT]. Please see the [LICENSE](LICENSE) file for full details.
  • mediumhomepage#3
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://huggingface.co/datasets/codefuse-admin/devopseval-exam

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/codefuse-devops-eval
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
MLPerf Inference
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. MLPerf Inference · recommended 1×
  2. Hugging Face Evaluate Library · recommended 1×
  3. LangChain Evaluation Module · recommended 1×
  4. OpenAI Evals · recommended 1×
  5. Prometheus · recommended 1×
  • CATEGORY QUERY
    How to benchmark large language models for performance in AIOps tasks?
    you: not recommended
    AI recommended (in order):
    1. MLPerf Inference
    2. Hugging Face Evaluate Library
    3. LangChain Evaluation Module
    4. OpenAI Evals
    5. Prometheus
    6. Grafana
    7. Scikit-learn
    8. NLTK
    9. Weights & Biases
    10. MLflow

    AI recommended 10 alternatives but never named codefuse-ai/codefuse-devops-eval. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are good datasets to evaluate LLM capabilities in time series anomaly detection?
    you: not recommended
    AI recommended (in order):
    1. NAB (Numenta Anomaly Benchmark)
    2. UCR Anomaly Archive
    3. Yahoo S5 (A5 Benchmark)
    4. SWaT (Secure Water Treatment) Dataset
    5. WADI (Water Distribution) Dataset
    6. ECG5000 (from UCR Time Series Classification Archive)
    7. Power Demand Data (from UCI Machine Learning Repository)

    AI recommended 7 alternatives but never named codefuse-ai/codefuse-devops-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 codefuse-ai/codefuse-devops-eval?
    pass
    AI did not name codefuse-ai/codefuse-devops-eval — 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/codefuse-devops-eval in production, what risks or prerequisites should they evaluate first?
    pass
    AI named codefuse-ai/codefuse-devops-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 codefuse-ai/codefuse-devops-eval solve, and who is the primary audience?
    pass
    AI did not name codefuse-ai/codefuse-devops-eval — 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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codefuse-ai/codefuse-devops-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