RRepoGEO

REPOGEO REPORT · LITE

datajuicer/data-juicer

Default branch main · commit 6c63ff5d · scanned 6/24/2026, 3:42:15 AM

GitHub: 6,577 stars · 384 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
33 /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
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 datajuicer/data-juicer, 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
    Clarify README's opening paragraph to emphasize specialization for foundation models

    Why:

    CURRENT
    Data-Juicer (DJ) transforms raw data chaos into AI-ready intelligence. It treats data processing as *composable infrastructure*—providing modular building blocks to clean, synthesize, and analyze data across the entire AI lifecycle, unlocking latent value in every byte. Whether you're deduplicating web-scale pre-training corpora, curating agent interaction traces, or preparing domain-specific RAG indices, DJ scales seamlessly from your laptop to thousand-node clusters—no glue code required.
    COPY-PASTE FIX
    Data-Juicer (DJ) is the specialized data operating system for the foundation model era, transforming raw data chaos into AI-ready intelligence specifically for large language models (LLMs) and other foundation models. Unlike generic data processing frameworks, DJ provides modular, composable infrastructure tailored to clean, synthesize, and analyze multimodal data across the entire AI lifecycle, from web-scale pre-training corpora to domain-specific RAG indices. It scales seamlessly from your laptop to thousand-node clusters, designed to unlock latent value in every byte for AI applications.
  • mediumtopics#2
    Add more specific topics related to LLM data preparation and RAG

    Why:

    CURRENT
    data, data-analysis, data-pipeline, data-processing, data-science, data-visualization, foundation-models, instruction-tuning, large-language-models, llm, llms, multi-modal, pre-training, synthetic-data
    COPY-PASTE FIX
    data, data-analysis, data-pipeline, data-processing, data-science, data-visualization, foundation-models, instruction-tuning, large-language-models, llm, llms, multi-modal, pre-training, synthetic-data, llm-data-preparation, rag-data, data-curation, multimodal-data-processing
  • lowabout#3
    Refine the repository description for clarity on data preparation

    Why:

    CURRENT
    Data processing for and with foundation models! 🍎 🍋 🌽 ➡️ ➡️🍸 🍹 🍷
    COPY-PASTE FIX
    A specialized data operating system for preparing, cleaning, and curating high-quality multimodal data for training and fine-tuning foundation models and 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 datajuicer/data-juicer
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Apache Spark
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Apache Spark · recommended 1×
  2. Delta Lake · recommended 1×
  3. Dask · recommended 1×
  4. Zarr · recommended 1×
  5. Ray · recommended 1×
  • CATEGORY QUERY
    How to efficiently prepare and clean large-scale multimodal datasets for training foundation models?
    you: not recommended
    AI recommended (in order):
    1. Apache Spark
    2. Delta Lake
    3. Dask
    4. Zarr
    5. Ray
    6. Apache Arrow
    7. Google Cloud Dataflow
    8. Apache Beam
    9. AWS Glue
    10. Pachyderm
    11. Hugging Face Datasets library

    AI recommended 11 alternatives but never named datajuicer/data-juicer. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help build modular data pipelines for LLM pre-training or RAG index curation?
    you: not recommended
    AI recommended (in order):
    1. Apache Airflow
    2. Prefect
    3. Dagster
    4. Kedro
    5. Luigi
    6. AWS Step Functions
    7. Azure Data Factory

    AI recommended 7 alternatives but never named datajuicer/data-juicer. 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 datajuicer/data-juicer?
    pass
    AI did not name datajuicer/data-juicer — 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 datajuicer/data-juicer in production, what risks or prerequisites should they evaluate first?
    pass
    AI named datajuicer/data-juicer 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 datajuicer/data-juicer solve, and who is the primary audience?
    pass
    AI named datajuicer/data-juicer explicitly

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

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite