RRepoGEO

REPOGEO REPORT · LITE

yaotingwangofficial/Awesome-MCoT

Default branch main · commit 26143708 · scanned 6/27/2026, 12:53:04 AM

GitHub: 1,012 stars · 34 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)

3 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 yaotingwangofficial/Awesome-MCoT, 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
    Clearly state the repository's nature as an 'awesome list' or 'curated resource' in the README

    Why:

    CURRENT
    The current README starts with an H2 and then an 'Introduction' section.
    COPY-PASTE FIX
    Add a sentence at the very top of the README, immediately after the main title, explicitly stating: 'This repository is an awesome list and curated collection of resources for Multimodal Chain-of-Thought (MCoT) reasoning, serving as a companion to our comprehensive survey paper.'
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected)
    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root, specifying an appropriate open-source license (e.g., CC-BY-4.0 for content, or MIT/Apache-2.0 if code is included).
  • mediumabout#3
    Refine the 'About' section description to emphasize 'awesome list' and 'curated resources'

    Why:

    CURRENT
    Multimodal Chain-of-Thought Reasoning: A Comprehensive Survey
    COPY-PASTE FIX
    Multimodal Chain-of-Thought Reasoning: A Comprehensive Survey and Curated Awesome List of Resources

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 yaotingwangofficial/Awesome-MCoT
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI CLIP
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI CLIP · recommended 1×
  2. Google PaLM-E · recommended 1×
  3. ImageBind · recommended 1×
  4. Hugging Face Transformers · recommended 1×
  5. Google Gemini · recommended 1×
  • CATEGORY QUERY
    What are effective strategies for implementing multimodal chain-of-thought reasoning in AI?
    you: not recommended
    AI recommended (in order):
    1. OpenAI CLIP
    2. Google PaLM-E
    3. ImageBind
    4. Hugging Face Transformers
    5. Google Gemini
    6. Kosmos-1
    7. LangChain
    8. LlamaIndex

    AI recommended 8 alternatives but never named yaotingwangofficial/Awesome-MCoT. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a comprehensive survey on multimodal reasoning approaches for large language models.
    you: not recommended
    AI recommended (in order):
    1. A Survey on Multimodal Large Language Models
    2. Multimodal Foundation Models: From Specialists to General-Purpose Assistants
    3. A Comprehensive Survey of Large Language Models
    4. Vision-Language Pre-training: A Survey
    5. Multimodal Machine Learning: A Survey and Taxonomy

    AI recommended 5 alternatives but never named yaotingwangofficial/Awesome-MCoT. 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 yaotingwangofficial/Awesome-MCoT?
    pass
    AI named yaotingwangofficial/Awesome-MCoT explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts yaotingwangofficial/Awesome-MCoT in production, what risks or prerequisites should they evaluate first?
    pass
    AI named yaotingwangofficial/Awesome-MCoT 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 yaotingwangofficial/Awesome-MCoT solve, and who is the primary audience?
    pass
    AI did not name yaotingwangofficial/Awesome-MCoT — 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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yaotingwangofficial/Awesome-MCoT — 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