RRepoGEO

REPOGEO REPORT · LITE

clips/pattern

Default branch master · commit d25511f9 · scanned 6/26/2026, 8:33:14 PM

GitHub: 8,853 stars · 1,558 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
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
  • highabout#1
    Clarify the 'about' description to emphasize integrated toolkit

    Why:

    CURRENT
    Web mining module for Python, with tools for scraping, natural language processing, machine learning, network analysis and visualization.
    COPY-PASTE FIX
    Pattern is an integrated Python library for web mining, natural language processing, machine learning, network analysis, and visualization. It combines tools for scraping, NLP, ML, and graph analysis into a single, versatile toolkit.
  • highreadme#2
    Strengthen the README's opening statement to highlight comprehensive nature

    Why:

    CURRENT
    Pattern is a web mining module for Python. It has tools for: Data Mining: web services (Google, Twitter, Wikipedia), web crawler, HTML DOM parser; Natural Language Processing: part-of-speech taggers, n-gram search, sentiment analysis, WordNet; Machine Learning: vector space model, clustering, classification (KNN, SVM, Perceptron); Network Analysis: graph centrality and visualization.
    COPY-PASTE FIX
    Pattern is a comprehensive Python library for web mining, natural language processing, machine learning, and network analysis. It offers an integrated toolkit with powerful features for data mining (web services, crawlers, HTML DOM), NLP (taggers, sentiment, WordNet), machine learning (vector space, clustering, classification), and network analysis (graph centrality, visualization).
  • mediumcomparison#3
    Add a 'Comparison with other libraries' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section titled 'Comparison with other libraries' that explains how Pattern integrates functionalities often found in separate tools like Scrapy (web scraping), NLTK/spaCy (NLP), and scikit-learn (machine learning), highlighting its all-in-one approach for rapid prototyping and integrated workflows.

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
Scrapy
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Scrapy · recommended 1×
  2. NLTK · recommended 1×
  3. spaCy · recommended 1×
  4. scikit-learn · recommended 1×
  5. PyTorch · recommended 1×
  • CATEGORY QUERY
    What Python library provides web scraping, natural language processing, and machine learning capabilities?
    you: not recommended
    AI recommended (in order):
    1. Scrapy
    2. NLTK
    3. spaCy
    4. scikit-learn
    5. PyTorch
    6. TensorFlow
    7. Beautiful Soup
    8. Requests
    9. pandas
    10. Selenium
    11. Haystack
    12. Hugging Face Transformers

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

    Show full AI answer
  • CATEGORY QUERY
    How can I perform sentiment analysis on scraped web data using a Python toolkit?
    you: not recommended
    AI recommended (in order):
    1. VADER (nltk/nltk)
    2. TextBlob (sloria/TextBlob)
    3. Hugging Face Transformers (huggingface/transformers)
    4. Flair (flairNLP/flair)
    5. spaCy (explosion/spaCy)
    6. spacytextblob (deanmalmgren/spacytextblob)
    7. Google Cloud Natural Language API
    8. AWS Comprehend
    9. Azure Text Analytics

    AI recommended 9 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.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/clips/pattern.svg)](https://repogeo.com/en/r/clips/pattern)
HTML
<a href="https://repogeo.com/en/r/clips/pattern"><img src="https://repogeo.com/badge/clips/pattern.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

clips/pattern — 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