RRepoGEO

REPOGEO REPORT · LITE

DataArcTech/DataArc-SynData-Toolkit

Default branch main · commit b6f0b97e · scanned 6/18/2026, 5:08:27 AM

GitHub: 1,756 stars · 69 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
23 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 fail
Objective metadata checks
AI knows your name
2 / 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 DataArcTech/DataArc-SynData-Toolkit, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highlicense#1
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a LICENSE file (e.g., MIT, Apache-2.0, or GPL-3.0) in the repository root to clarify usage rights.
  • mediumreadme#2
    Emphasize LLM training data generation in the README's opening line

    Why:

    CURRENT
    *A modular, highly user-friendly synthetic data generation toolkit supporting multi-source, multi-language data synthesis.*
    COPY-PASTE FIX
    *A modular, highly user-friendly synthetic data generation toolkit specifically designed for creating multi-source, multi-language training data for Large Language Models (LLMs).*

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 DataArcTech/DataArc-SynData-Toolkit
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI API
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI API · recommended 1×
  2. huggingface/transformers · recommended 1×
  3. langchain-ai/langchain · recommended 1×
  4. run-llama/llama_index · recommended 1×
  5. faker-bot/faker · recommended 1×
  • CATEGORY QUERY
    How to generate synthetic training data for LLMs with minimal coding effort?
    you: not recommended
    AI recommended (in order):
    1. OpenAI API
    2. Hugging Face Transformers (huggingface/transformers)
    3. LangChain (langchain-ai/langchain)
    4. LlamaIndex (run-llama/llama_index)
    5. Faker (faker-bot/faker)
    6. mimesis (lk-geimfari/mimesis)
    7. synth-ai (synth-ai/synth-ai)
    8. nlpaug (makcedward/nlpaug)
    9. textattack (TextAttack/TextAttack)
    10. Google Cloud Vertex AI
    11. Microsoft Azure OpenAI Service

    AI recommended 11 alternatives but never named DataArcTech/DataArc-SynData-Toolkit. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help synthesize multi-source, multi-language data for AI model development?
    you: not recommended
    AI recommended (in order):
    1. Databricks Lakehouse Platform
    2. Google Cloud Platform (GCP)
    3. BigQuery
    4. BigQuery ML
    5. Dataflow (apache/beam)
    6. Cloud Translation API
    7. AWS Lake Formation
    8. Amazon SageMaker
    9. AWS Glue
    10. Amazon Translate
    11. Microsoft Azure Synapse Analytics
    12. Azure Machine Learning
    13. Azure Translator
    14. Apache Spark (apache/spark)
    15. Apache Flink (apache/flink)
    16. Snowflake
    17. Snowpark
    18. External Functions
    19. dbt (data build tool) (dbt-labs/dbt-core)

    AI recommended 19 alternatives but never named DataArcTech/DataArc-SynData-Toolkit. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 DataArcTech/DataArc-SynData-Toolkit?
    pass
    AI did not name DataArcTech/DataArc-SynData-Toolkit — 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 DataArcTech/DataArc-SynData-Toolkit in production, what risks or prerequisites should they evaluate first?
    pass
    AI named DataArcTech/DataArc-SynData-Toolkit 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 DataArcTech/DataArc-SynData-Toolkit solve, and who is the primary audience?
    pass
    AI named DataArcTech/DataArc-SynData-Toolkit explicitly

    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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MARKDOWN (README)
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DataArcTech/DataArc-SynData-Toolkit — 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