RRepoGEO

REPOGEO REPORT · LITE

clips/pattern

Default branch master · commit d25511f9 · scanned 5/16/2026, 1:03:07 AM

GitHub: 8,858 stars · 1,559 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 clips/pattern, 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
    Clarify repo identity in README to prevent misidentification as CLIPS expert system

    Why:

    CURRENT
    Pattern is a web mining module for Python.
    COPY-PASTE FIX
    Pattern is a comprehensive Python library for web mining, natural language processing, and machine learning. (This is the 'Pattern' library for Python, distinct from the CLIPS expert system.) It offers an integrated suite of tools for data extraction, text analysis, and predictive modeling.
  • mediumreadme#2
    Add a 'Why Pattern?' section highlighting its integrated approach

    Why:

    COPY-PASTE FIX
    ## Why Pattern?

Unlike using multiple specialized libraries for web scraping (e.g., Beautiful Soup, Scrapy), NLP (e.g., NLTK, TextBlob), and machine learning (e.g., scikit-learn), Pattern offers an integrated, all-in-one solution. This allows for seamless workflows from data extraction to analysis and modeling within a single, consistent Python module.
  • lowtopics#3
    Add more specific keywords to topics list

    Why:

    CURRENT
    machine-learning, natural-language-processing, network-analysis, python, sentiment-analysis, web-mining, wordnet
    COPY-PASTE FIX
    machine-learning, natural-language-processing, network-analysis, python, sentiment-analysis, web-mining, wordnet, web-scraping, text-processing, data-mining-python

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 clips/pattern
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
crummy/BeautifulSoup
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. crummy/BeautifulSoup · recommended 1×
  2. nltk/nltk · recommended 1×
  3. cjhutto/vaderSentiment · recommended 1×
  4. scrapy/scrapy · recommended 1×
  5. sloria/TextBlob · recommended 1×
  • CATEGORY QUERY
    What Python library helps with web scraping and then processing text for sentiment?
    you: not recommended
    AI recommended (in order):
    1. Beautiful Soup (crummy/BeautifulSoup)
    2. NLTK (nltk/nltk)
    3. VADER (cjhutto/vaderSentiment)
    4. Scrapy (scrapy/scrapy)
    5. TextBlob (sloria/TextBlob)
    6. Requests (psf/requests)
    7. spaCy (explosion/spaCy)
    8. spacytextblob (SamEdwardes/spacytextblob)
    9. Selenium (SeleniumHQ/selenium)
    10. Flair (flairNLP/flair)
    11. Playwright (microsoft/playwright)
    12. Hugging Face Transformers (huggingface/transformers)

    AI recommended 12 alternatives but never named clips/pattern. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Need a comprehensive Python module for web data extraction, NLP, and machine learning tasks.
    you: not recommended
    AI recommended (in order):
    1. Scrapy
    2. spaCy
    3. scikit-learn
    4. Beautiful Soup
    5. NLTK
    6. Requests
    7. Pandas
    8. TextBlob
    9. Haystack
    10. PyTorch
    11. TensorFlow

    AI recommended 11 alternatives but never named clips/pattern. 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 clips/pattern?
    pass
    AI named clips/pattern explicitly

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

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

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

Embed your GEO score

Drop this badge into the README of clips/pattern. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite