REPOGEO REPORT · LITE

daochenzha/data-centric-AI

Default branch main · commit 2e531048 · scanned 5/23/2026, 3:13:31 PM

GitHub: 1,150 stars · 79 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

28 /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

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 daochenzha/data-centric-AI, 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 README opening to clarify it's a curated list, not a tool

Why:

CURRENT

# Awesome-Data-Centric-AI
[](https://github.com/sindresorhus/awesome)
A curated, but incomplete, list of data-centric AI resources.

COPY-PASTE FIX

# Awesome-Data-Centric-AI: A Curated List of Data-Centric AI Resources

This is an awesome list of curated resources for Data-Centric AI, covering concepts, techniques, and future perspectives. It aims to provide a comprehensive overview for researchers, engineers, and data scientists interested in improving AI models through systematic data improvement.

mediumlicense#2

Add a LICENSE file to the repository

Why:

COPY-PASTE FIX

Create a `LICENSE` file in the repository root with the text of a permissive open-source license, such as MIT License.

lowhomepage#3

Add a homepage URL to the repository metadata

Why:

COPY-PASTE FIX

Set the repository's homepage URL to `https://github.com/daochenzha/data-centric-AI` in the repository settings.

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 daochenzha/data-centric-AI

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Andrew Ng's Data-Centric AI Course on Coursera

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Andrew Ng's Data-Centric AI Course on Coursera · recommended 1×
The Data-Centric AI Handbook · recommended 1×
cleanlab/cleanlab · recommended 1×
snorkel-team/snorkel · recommended 1×
great-expectations/great_expectations · recommended 1×

CATEGORY QUERY
Where can I find a comprehensive overview of data-centric AI concepts and techniques?
you: not recommended
AI recommended (in order):
1. Andrew Ng's Data-Centric AI Course on Coursera
2. The Data-Centric AI Handbook
3. Cleanlab (cleanlab/cleanlab)
4. Snorkel AI (snorkel-team/snorkel)
AI recommended 4 alternatives but never named daochenzha/data-centric-AI. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are the essential resources for learning about data quality and engineering in machine learning?
you: not recommended
AI recommended (in order):
1. Great Expectations (great-expectations/great_expectations)
2. Apache Airflow (apache/airflow)
3. dbt (dbt-labs/dbt-core)
4. ydata-profiling (ydataai/ydata-profiling)
AI recommended 4 alternatives but never named daochenzha/data-centric-AI. 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 daochenzha/data-centric-AI?
pass
AI named daochenzha/data-centric-AI explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts daochenzha/data-centric-AI in production, what risks or prerequisites should they evaluate first?
pass
AI named daochenzha/data-centric-AI 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 daochenzha/data-centric-AI solve, and who is the primary audience?
pass
AI did not name daochenzha/data-centric-AI — 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?

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MARKDOWN (README)

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daochenzha/data-centric-AI — 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