REPOGEO REPORT · LITE

Renumics/awesome-open-data-centric-ai

Default branch main · commit 48545070 · scanned 6/5/2026, 6:38:06 AM

GitHub: 732 stars · 41 forks

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 Renumics/awesome-open-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

Clarify repo type in README's opening statement

Why:

CURRENT

Open source tooling for data-centric AI on unstructured data

COPY-PASTE FIX

A comprehensive, curated list of open-source tooling for data-centric AI on unstructured data.

mediumreadme#2

Add a dedicated section clarifying the repo's purpose as a list

Why:

COPY-PASTE FIX

Add a new section, perhaps titled 'What is this list?' or 'How to use this Awesome List?', with content like: 'This repository is a curated collection of open-source tools and resources for data-centric AI. It is not a software library or framework to be installed, but rather a guide to help you discover and evaluate existing tools like [mention a few examples from the list itself, e.g., Cleanlab, Argilla, etc.].'

lowtopics#3

Add 'data-quality' topic

Why:

CURRENT

active-learning, awesome-list, bias-detection, computer-vision, data-centric-ai, data-curation, data-drift, data-versioning, data-visualization, deep-learning, documentation-only, explainable-ai, feature-vector, machine-learning, nlp, noisy-labels, outlier-detection, robust-machine-learning, synthetic-data, uncertainty-estimation

COPY-PASTE FIX

active-learning, awesome-list, bias-detection, computer-vision, data-centric-ai, data-curation, data-drift, data-quality, data-versioning, data-visualization, deep-learning, documentation-only, explainable-ai, feature-vector, machine-learning, nlp, noisy-labels, outlier-detection, robust-machine-learning, synthetic-data, uncertainty-estimation

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 Renumics/awesome-open-data-centric-ai

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

OpenRefine

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

OpenRefine · recommended 2×
Great Expectations · recommended 1×
Pandas Profiling · recommended 1×
Deepchecks · recommended 1×
DataPrep · recommended 1×

CATEGORY QUERY
Seeking open-source tools to improve data quality for machine learning models.
you: not recommended
AI recommended (in order):
1. Great Expectations
2. Pandas Profiling
3. Deepchecks
4. DataPrep
5. Cleanlab
6. OpenRefine
AI recommended 6 alternatives but never named Renumics/awesome-open-data-centric-ai. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What frameworks help with data curation and bias detection in unstructured datasets?
you: not recommended
AI recommended (in order):
1. spaCy
2. Prodigy
3. Hugging Face Transformers
4. Argilla
5. DIFTA
6. Fairlearn
7. Aequitas
8. NLTK
9. OpenRefine
AI recommended 9 alternatives but never named Renumics/awesome-open-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
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 Renumics/awesome-open-data-centric-ai?
pass
AI named Renumics/awesome-open-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 Renumics/awesome-open-data-centric-ai in production, what risks or prerequisites should they evaluate first?
pass
AI named Renumics/awesome-open-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 Renumics/awesome-open-data-centric-ai solve, and who is the primary audience?
pass
AI did not name Renumics/awesome-open-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?

Embed your GEO score

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MARKDOWN (README)

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Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite