RRepoGEO

REPOGEO REPORT · LITE

voidful/awesome-chatgpt-dataset

Default branch main · commit eb217e3f · scanned 6/12/2026, 1:57:33 AM

GitHub: 762 stars · 65 forks

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 voidful/awesome-chatgpt-dataset, 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 the README H1 to clarify it's an "awesome list" of datasets

    Why:

    CURRENT
    # awesome-chatgpt-dataset
    COPY-PASTE FIX
    # awesome-chatgpt-dataset: A Curated List of Datasets for Training Large Language Models
  • mediumtopics#2
    Add more specific topics related to LLM training data and collections

    Why:

    CURRENT
    awesome, chatgpt, dataset, gpt4, instructions
    COPY-PASTE FIX
    awesome, chatgpt, dataset, gpt4, instructions, llm-datasets, fine-tuning-data, instruction-tuning, conversational-ai-data
  • lowhomepage#3
    Add the repository URL as the homepage

    Why:

    COPY-PASTE FIX
    https://github.com/voidful/awesome-chatgpt-dataset

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 voidful/awesome-chatgpt-dataset
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Alpaca (Stanford Alpaca)
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Alpaca (Stanford Alpaca) · recommended 1×
  2. ShareGPT (OpenAssistant Conversations Dataset) · recommended 1×
  3. Dolly 2.0 (Databricks Dolly-v2-12b) · recommended 1×
  4. FLAN (Fine-tuned LAnguage Net) · recommended 1×
  5. P3 (Public Pool of Prompts) · recommended 1×
  • CATEGORY QUERY
    Where can I find diverse instruction datasets to fine-tune a large language model?
    you: not recommended
    AI recommended (in order):
    1. Alpaca (Stanford Alpaca)
    2. ShareGPT (OpenAssistant Conversations Dataset)
    3. Dolly 2.0 (Databricks Dolly-v2-12b)
    4. FLAN (Fine-tuned LAnguage Net)
    5. P3 (Public Pool of Prompts)
    6. Super-NaturalInstructions
    7. LIMA (Less Is More for Alignment)

    AI recommended 7 alternatives but never named voidful/awesome-chatgpt-dataset. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What resources are available for collecting high-quality conversational data for AI training?
    you: not recommended
    AI recommended (in order):
    1. OpenAI API
    2. GPT-4
    3. GPT-3.5
    4. Amazon Mechanical Turk
    5. Appen
    6. Figure Eight
    7. Scale AI
    8. Hugging Face Datasets
    9. DailyDialog
    10. Persona-Chat
    11. MultiWOZ
    12. Common Voice
    13. Kaggle
    14. Reddit
    15. Twitter

    AI recommended 15 alternatives but never named voidful/awesome-chatgpt-dataset. 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 voidful/awesome-chatgpt-dataset?
    pass
    AI did not name voidful/awesome-chatgpt-dataset — 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?

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