RRepoGEO

REPOGEO REPORT · LITE

VikParuchuri/textbook_quality

Default branch master · commit 3548c557 · scanned 6/4/2026, 10:53:04 AM

GitHub: 508 stars · 46 forks

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 VikParuchuri/textbook_quality, 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 and opening sentence to clarify synthetic LLM pretraining data generation

    Why:

    CURRENT
    # Textbook Quality

This project generates very long, textbook quality pretraining data.
    COPY-PASTE FIX
    # Textbook Quality: Synthetic Data Generator for LLM Pretraining

This project generates very long, high-quality *synthetic* pretraining datasets specifically designed for Large Language Models (LLMs), mimicking the structure and depth of textbook content.
  • mediumtopics#2
    Add more specific topics to improve categorization for synthetic data generation

    Why:

    CURRENT
    ai, dataset, llm
    COPY-PASTE FIX
    ai, dataset, llm, synthetic-data, data-generation, llm-pretraining
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://github.com/VikParuchuri/textbook_quality

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 VikParuchuri/textbook_quality
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI API
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI API · recommended 1×
  2. Hugging Face Transformers Library · recommended 1×
  3. EleutherAI's GPT-J/GPT-NeoX · recommended 1×
  4. Snorkel AI · recommended 1×
  5. nlpaug · recommended 1×
  • CATEGORY QUERY
    How can I generate high-quality synthetic pretraining data for large language models?
    you: not recommended
    AI recommended (in order):
    1. OpenAI API
    2. Hugging Face Transformers Library
    3. EleutherAI's GPT-J/GPT-NeoX
    4. Snorkel AI
    5. nlpaug
    6. TextAttack
    7. LangChain
    8. LlamaIndex

    AI recommended 8 alternatives but never named VikParuchuri/textbook_quality. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are good options for creating long-form, textbook-quality datasets for LLM training?
    you: not recommended
    AI recommended (in order):
    1. LaTeX
    2. Overleaf
    3. Jupyter Book
    4. Sphinx
    5. GitBook
    6. Microsoft Word
    7. Google Docs
    8. AsciiDoc
    9. Asciidoctor

    AI recommended 9 alternatives but never named VikParuchuri/textbook_quality. 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 VikParuchuri/textbook_quality?
    pass
    AI named VikParuchuri/textbook_quality explicitly

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

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