REPOGEO REPORT · LITE

OpenDataBox/awesome-data-llm

Default branch main · commit 54a84414 · scanned 6/10/2026, 1:43:04 PM

GitHub: 789 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

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 OpenDataBox/awesome-data-llm, 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 as an 'awesome list' in README opening

Why:

CURRENT

> A collection of papers and projects related to LLMs and corresponding data-centric methods.

COPY-PASTE FIX

> This is an awesome list and curated collection of papers and projects related to LLMs and corresponding data-centric methods.

highlicense#2
Add a LICENSE file to the repository
Why:
COPY-PASTE FIX
```
(Create a LICENSE file, e.g., MIT or Apache-2.0, and add it to the repository root.)
```

mediumtopics#3

Add 'awesome-list' and 'survey' to repository topics

Why:

CURRENT

data-acquisition, data-deduplication, data-filtering, data-mixing, data-provenance, data-selection, data-synthesis, data-transformation, llm, vlm

COPY-PASTE FIX

awesome-list, survey, data-acquisition, data-deduplication, data-filtering, data-mixing, data-provenance, data-selection, data-synthesis, data-transformation, llm, vlm

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 OpenDataBox/awesome-data-llm

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

databricks/dolly

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

databricks/dolly · recommended 1×
togethercomputer/RedPajama-Data · recommended 1×
tatsu-lab/stanford_alpaca · recommended 1×
lmsys/vicuna · recommended 1×
FLAN · recommended 1×

CATEGORY QUERY
What are effective data-centric methods for improving large language model performance and reliability?
you: not recommended
AI recommended (in order):
1. Dolly 2.0 (databricks/dolly)
2. RedPajama-Data (togethercomputer/RedPajama-Data)
3. Alpaca (tatsu-lab/stanford_alpaca)
4. Vicuna (lmsys/vicuna)
5. FLAN
6. ChatGPT
7. InstructGPT
8. Self-Instruct
9. ShareGPT
10. Snorkel (snorkel-team/snorkel)
11. Label Studio (heartexlabs/label-studio)
12. BioGPT (microsoft/BioGPT)
13. BloombergGPT
AI recommended 13 alternatives but never named OpenDataBox/awesome-data-llm. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Seeking tools and techniques for data preparation, synthesis, and transformation in LLM development.
you: not recommended
AI recommended (in order):
1. Snorkel
2. Argilla
3. Cleanlab
4. Pandas
5. Hugging Face Datasets library
6. Dataiku
7. Synthetic Data Vault (SDV)
AI recommended 7 alternatives but never named OpenDataBox/awesome-data-llm. 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 OpenDataBox/awesome-data-llm?
pass
AI named OpenDataBox/awesome-data-llm explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts OpenDataBox/awesome-data-llm in production, what risks or prerequisites should they evaluate first?
pass
AI named OpenDataBox/awesome-data-llm 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 OpenDataBox/awesome-data-llm solve, and who is the primary audience?
pass
AI did not name OpenDataBox/awesome-data-llm — 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

Drop this badge into the README of OpenDataBox/awesome-data-llm. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/OpenDataBox/awesome-data-llm.svg)](https://repogeo.com/en/r/OpenDataBox/awesome-data-llm)

HTML

<a href="https://repogeo.com/en/r/OpenDataBox/awesome-data-llm"><img src="https://repogeo.com/badge/OpenDataBox/awesome-data-llm.svg" alt="RepoGEO" /></a>

Pro

Subscribe to Pro for deep diagnoses

OpenDataBox/awesome-data-llm — 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