REPOGEO REPORT · LITE

WangRongsheng/awesome-LLM-resources

Default branch main · commit 462fb081 · scanned 6/28/2026, 12:57:27 AM

GitHub: 8,600 stars · 906 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

22 /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

1 / 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 WangRongsheng/awesome-LLM-resources, 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

Add a prominent English value proposition to the README's opening

Why:

CURRENT

The README currently starts with centered Chinese text and then a tip about medical resources.

COPY-PASTE FIX

# WangRongsheng/awesome-LLM-resources: The World's Best Curated Collection of LLM Resources

mediumhomepage#2
Add the repository URL as the homepage
Why:
COPY-PASTE FIX
```
https://github.com/WangRongsheng/awesome-LLM-resources
```

lowtopics#3

Expand topics with more specific LLM sub-categories

Why:

CURRENT

awesome-list, book, course, large-language-models, llama, llm, mistral, openai, qwen, rag, retrieval-augmented-generation, webui

COPY-PASTE FIX

awesome-list, book, course, large-language-models, llama, llm, mistral, openai, qwen, rag, retrieval-augmented-generation, webui, multimodal, model-training, model-inference, small-language-models, visual-language-models

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 WangRongsheng/awesome-LLM-resources

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

huggingface/transformers

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

huggingface/transformers · recommended 1×
Papers With Code · recommended 1×
OpenAI · recommended 1×
Google AI · recommended 1×
arXiv · recommended 1×

CATEGORY QUERY
Where can I find a comprehensive collection of resources for large language model development and research?
you: not recommended
AI recommended (in order):
1. Hugging Face Hub (huggingface/transformers)
2. Papers With Code
3. OpenAI
4. Google AI
5. arXiv
6. Towards Data Science
7. LangChain (langchain-ai/langchain)
AI recommended 7 alternatives but never named WangRongsheng/awesome-LLM-resources. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are the best learning materials and tools for building applications with LLMs, including RAG and agents?
you: not recommended
AI recommended (in order):
1. LangChain
2. LlamaIndex
3. DeepLearning.AI
4. Hugging Face Transformers Library
5. OpenAI API
6. Weights & Biases
7. Pinecone
8. Weaviate
9. ChromaDB
AI recommended 9 alternatives but never named WangRongsheng/awesome-LLM-resources. 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 WangRongsheng/awesome-LLM-resources?
pass
AI named WangRongsheng/awesome-LLM-resources explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts WangRongsheng/awesome-LLM-resources in production, what risks or prerequisites should they evaluate first?
pass
AI did not name WangRongsheng/awesome-LLM-resources — 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?
In one sentence, what problem does the repo WangRongsheng/awesome-LLM-resources solve, and who is the primary audience?
pass
AI did not name WangRongsheng/awesome-LLM-resources — 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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HTML

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Pro

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WangRongsheng/awesome-LLM-resources — 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