REPOGEO REPORT · LITE

OpenDCAI/DataFlow

Default branch main · commit 9932d311 · scanned 5/20/2026, 9:42:08 AM

GitHub: 3,799 stars · 390 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

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 OpenDCAI/DataFlow, 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

highabout#1

Refine the 'about' description to emphasize LLM-specific data preparation

Why:

CURRENT

Easy Data Preparation with latest LLMs-based Operators and Pipelines.

COPY-PASTE FIX

Low-code, visual pipelines for generating, cleaning, and preparing high-quality datasets specifically for LLM training and fine-tuning.

mediumreadme#2

Strengthen README's initial positioning statement

Why:

CURRENT

# DataFlow

**Generate, Clean, and Prepare LLM Data, All-in-One**

COPY-PASTE FIX

# DataFlow

**The low-code platform for generating, cleaning, and preparing high-quality datasets specifically for LLM training and fine-tuning.**

lowtopics#3

Add more specific topics related to LLM training and dataset curation

Why:

CURRENT

data, data-agent, data-cleaning, data-pipelines, data-processing, data-science, data-synthesis, gradio-interface, llms, operators, quick-data-processing, sglang-bankend, vllm-backend

COPY-PASTE FIX

data, data-agent, data-cleaning, data-pipelines, data-processing, data-science, data-synthesis, gradio-interface, llms, operators, quick-data-processing, sglang-bankend, vllm-backend, llm-training, llm-fine-tuning, dataset-curation

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 OpenDCAI/DataFlow

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

apache/spark

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

apache/spark · recommended 1×
dask/dask · recommended 1×
pandas-dev/pandas · recommended 1×
huggingface/datasets · recommended 1×
cleanlab/cleanlab · recommended 1×

CATEGORY QUERY
How to efficiently prepare and clean large datasets for training LLMs?
you: not recommended
AI recommended (in order):
1. Apache Spark (apache/spark)
2. Dask (dask/dask)
3. Pandas (pandas-dev/pandas)
4. Hugging Face Datasets (huggingface/datasets)
5. Cleanlab (cleanlab/cleanlab)
6. spaCy (explosion/spaCy)
7. NLTK (nltk/nltk)
8. Great Expectations (great-expectations/great_expectations)
AI recommended 8 alternatives but never named OpenDCAI/DataFlow. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Looking for a low-code tool to build data pipelines for LLM-based applications.
you: not recommended
AI recommended (in order):
1. LangChain (langchain-ai/langchain)
2. LlamaIndex (run-llama/llama_index)
3. Microsoft Azure Machine Learning Studio (Designer)
4. Google Cloud Vertex AI Workbench (Pipelines)
5. Dataiku DSS (Data Science Studio)
6. Knime Analytics Platform
7. Zapier
8. Make
AI recommended 8 alternatives but never named OpenDCAI/DataFlow. 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 OpenDCAI/DataFlow?
pass
AI named OpenDCAI/DataFlow explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts OpenDCAI/DataFlow in production, what risks or prerequisites should they evaluate first?
pass
AI named OpenDCAI/DataFlow 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 OpenDCAI/DataFlow solve, and who is the primary audience?
pass
AI named OpenDCAI/DataFlow explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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MARKDOWN (README)

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OpenDCAI/DataFlow — 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