RRepoGEO

REPOGEO REPORT · LITE

cxcscmu/Craw4LLM

Default branch main · commit f63f927e · scanned 6/5/2026, 7:48:37 PM

GitHub: 654 stars · 60 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 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 cxcscmu/Craw4LLM, 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 README's opening to highlight LLM-specific value

    Why:

    CURRENT
    # Craw4LLM

This repo contains the code for the paper "Craw4LLM: Efficient Web Crawling for LLM Pretraining".
    COPY-PASTE FIX
    # Craw4LLM: Efficient Web Crawling for LLM Pretraining

Craw4LLM is an efficient web crawling framework specifically designed to acquire high-quality, domain-specific data for Large Language Model (LLM) pre-training. This repository contains the code for our paper 'Craw4LLM: Efficient Web Crawling for LLM Pretraining'.
  • mediumtopics#2
    Add more specific topics for LLM data generation/curation

    Why:

    CURRENT
    crawler, crawling, large-language-models, llm, pre-training, pretraining, web-crawler, web-crawling
    COPY-PASTE FIX
    crawler, crawling, large-language-models, llm, pre-training, pretraining, web-crawler, web-crawling, llm-data-generation, llm-dataset-curation, data-acquisition-llm, efficient-web-crawling
  • lowreadme#3
    Add a dedicated section comparing Craw4LLM to general crawlers

    Why:

    COPY-PASTE FIX
    ## Why Craw4LLM for LLM Pre-training?

Unlike general-purpose web crawlers (e.g., Scrapy, Apache Nutch) or existing large datasets (e.g., Common Crawl), Craw4LLM is purpose-built for the unique challenges of acquiring high-quality, domain-specific data for Large Language Model pre-training. Our framework integrates efficient crawling with content selection mechanisms tailored for LLM data needs, ensuring relevance and quality beyond raw data acquisition.

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 cxcscmu/Craw4LLM
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Common Crawl
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Common Crawl · recommended 1×
  2. scrapy/scrapy · recommended 1×
  3. apache/nutch · recommended 1×
  4. internetarchive/heritrix3 · recommended 1×
  5. puppeteer/puppeteer · recommended 1×
  • CATEGORY QUERY
    How to efficiently crawl web data for pre-training large language models?
    you: not recommended
    AI recommended (in order):
    1. Common Crawl
    2. Scrapy (scrapy/scrapy)
    3. Apache Nutch (apache/nutch)
    4. Heritrix (internetarchive/heritrix3)
    5. Puppeteer (puppeteer/puppeteer)
    6. Playwright (microsoft/playwright)
    7. Beautiful Soup (crummy/beautifulsoup4)
    8. Requests (psf/requests)

    AI recommended 8 alternatives but never named cxcscmu/Craw4LLM. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a Python library for scalable web data acquisition for LLM pre-training.
    you: not recommended
    AI recommended (in order):
    1. Scrapy
    2. Playwright
    3. Requests
    4. Beautiful Soup 4
    5. Selenium
    6. MechanicalSoup

    AI recommended 6 alternatives but never named cxcscmu/Craw4LLM. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • 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 cxcscmu/Craw4LLM?
    pass
    AI named cxcscmu/Craw4LLM explicitly

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

  • If a team adopts cxcscmu/Craw4LLM in production, what risks or prerequisites should they evaluate first?
    pass
    AI named cxcscmu/Craw4LLM 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 cxcscmu/Craw4LLM solve, and who is the primary audience?
    pass
    AI named cxcscmu/Craw4LLM explicitly

    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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cxcscmu/Craw4LLM — 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