RRepoGEO

REPOGEO REPORT · LITE

wilpel/caveman-compression

Default branch main · commit 2a11efbf · scanned 6/15/2026, 3:08:33 AM

GitHub: 1,004 stars · 61 forks

AI VISIBILITY SCORE
23 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 wilpel/caveman-compression, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highlicense#1
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root, specifying a standard open-source license (e.g., MIT, Apache-2.0).
  • mediumabout#2
    Update the repository's 'About' description

    Why:

    CURRENT
    Caveman Compression is a semantic compression method for LLM contexts. It removes predictable grammar while preserving the unpredictable, factual content that defines meaning.
    COPY-PASTE FIX
    Caveman Compression is a **Python library** for **semantic compression** of **LLM contexts**. It removes predictable grammar while preserving unpredictable, factual content to **reduce token usage**.

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 wilpel/caveman-compression
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers
Recommended in 4 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 4×
  2. spaCy · recommended 3×
  3. OpenAI API · recommended 3×
  4. LangChain · recommended 2×
  5. NLTK · recommended 2×
  • CATEGORY QUERY
    How can I reduce token usage for large language model prompts effectively?
    you: not recommended
    AI recommended (in order):
    1. LLMLingua
    2. LongLLM
    3. GPT-3.5 Turbo
    4. BART
    5. LangChain
    6. spaCy
    7. NLTK
    8. OpenAI Fine-tuning API
    9. Hugging Face Transformers

    AI recommended 9 alternatives but never named wilpel/caveman-compression. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are techniques for semantically compressing text to fit more into LLM context windows?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. OpenAI API
    3. Gensim
    4. NLTK
    5. spaCy
    6. YAKE!
    7. RAKE
    8. spaCy
    9. Hugging Face Transformers
    10. OpenAI API
    11. LangChain
    12. LlamaIndex
    13. Faiss
    14. Pinecone
    15. Weaviate
    16. Qdrant
    17. Milvus
    18. OpenAI API
    19. Anthropic Claude
    20. Hugging Face Transformers

    AI recommended 20 alternatives but never named wilpel/caveman-compression. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 wilpel/caveman-compression?
    pass
    AI did not name wilpel/caveman-compression — 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 wilpel/caveman-compression in production, what risks or prerequisites should they evaluate first?
    pass
    AI named wilpel/caveman-compression 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 wilpel/caveman-compression solve, and who is the primary audience?
    pass
    AI named wilpel/caveman-compression 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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MARKDOWN (README)
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wilpel/caveman-compression — 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