RRepoGEO

REPOGEO REPORT · LITE

MigoXLab/dingo

Default branch main · commit a4de63c6 · scanned 6/12/2026, 11:07:23 AM

GitHub: 711 stars · 72 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 MigoXLab/dingo, 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 and opening paragraph to clarify Dingo's core purpose

    Why:

    COPY-PASTE FIX
    Ensure the README's H1 and first paragraph clearly state: 'Dingo: A Comprehensive AI Data, Model and Application Quality Evaluation Platform. Dingo provides AI-powered tools for assessing the quality of machine learning datasets, validating LLM training data, detecting hallucinations, and evaluating RAG system performance.'
  • mediumabout#2
    Refine the repository's 'About' description for clearer AI categorization

    Why:

    CURRENT
    Dingo: A Comprehensive AI Data, Model and Application Quality Evaluation Tool
    COPY-PASTE FIX
    Dingo is a comprehensive AI-powered platform for evaluating the quality of machine learning datasets, LLM training data, and RAG systems, including hallucination detection.
  • lowreadme#3
    Add a 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section to the README, e.g., 'Dingo vs. Other Evaluation Tools', that briefly compares Dingo's comprehensive AI-powered approach to data, model, and application quality evaluation against tools like Great Expectations, Ragas, or Scale AI.

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 MigoXLab/dingo
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Scale AI
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Scale AI · recommended 2×
  2. Appen · recommended 2×
  3. ydataai/pandas-profiling · recommended 1×
  4. great-expectations/great_expectations · recommended 1×
  5. Surge AI · recommended 1×
  • CATEGORY QUERY
    How can I evaluate and validate the quality of my LLM training datasets?
    you: not recommended
    AI recommended (in order):
    1. Pandas-Profiling (ydataai/pandas-profiling)
    2. Great Expectations (great-expectations/great_expectations)
    3. Scale AI
    4. Appen
    5. Surge AI
    6. Argilla (argilla-io/argilla)
    7. Sentence-BERT (SBERT) (UKP-LAB/sentence-transformers)
    8. OpenAI Embeddings API
    9. Fairness Indicators (Google) (tensorflow/fairness-indicators)
    10. Aequitas (dssg/aequitas)
    11. Hugging Face's `evaluate` library (huggingface/evaluate)
    12. datasketch (ekzhu/datasketch)
    13. Isolation Forest
    14. Local Outlier Factor (LOF)
    15. Pydantic (pydantic/pydantic)
    16. JSON Schema

    AI recommended 16 alternatives but never named MigoXLab/dingo. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help detect LLM hallucinations and assess RAG system performance metrics?
    you: not recommended
    AI recommended (in order):
    1. Arize AI
    2. Galileo
    3. LangChain Evaluation
    4. DeepEval
    5. Ragas
    6. W&B Prompts
    7. Scale AI
    8. Appen

    AI recommended 8 alternatives but never named MigoXLab/dingo. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • 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 MigoXLab/dingo?
    pass
    AI named MigoXLab/dingo explicitly

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

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

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

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MigoXLab/dingo — 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