RRepoGEO

REPOGEO REPORT · LITE

alexgreensh/token-optimizer

Default branch main · commit 8717add7 · scanned 5/20/2026, 10:51:38 AM

GitHub: 1,043 stars · 81 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)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
22 /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
1 / 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 alexgreensh/token-optimizer, 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's opening to clearly state the tool's unique purpose.

    Why:

    CURRENT
    Current H2: "Your AI is getting dumber and you can't see it."
    COPY-PASTE FIX
    # Token Optimizer: The LLM Context Management System for Ghost Tokens and Compaction Survival
  • mediumreadme#2
    Add a dedicated 'What makes Token Optimizer different?' section to the README.

    Why:

    COPY-PASTE FIX
    ## What makes Token Optimizer different?
Unlike general tokenizers or prompt compression tools, Token Optimizer is a comprehensive LLM context management system. It specifically targets "ghost tokens" – hidden inefficiencies that degrade context quality – and ensures your LLM's performance survives aggressive context compaction, providing measurable proof of optimization.
  • lowreadme#3
    Clarify the project's license(s) directly in the README.

    Why:

    COPY-PASTE FIX
    Add a section like: ## License
This project is licensed under [Specify the exact license(s) here, e.g., "a custom license based on Apache 2.0 and MIT principles"]. See the [LICENSE](LICENSE) file for full details.

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 alexgreensh/token-optimizer
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Pinecone
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Pinecone · recommended 2×
  2. LangChain · recommended 1×
  3. OpenAI Embeddings · recommended 1×
  4. Google Embeddings · recommended 1×
  5. LlamaIndex · recommended 1×
  • CATEGORY QUERY
    How to prevent AI model context quality decay from inefficient token usage?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. OpenAI Embeddings
    3. Google Embeddings
    4. LlamaIndex
    5. Pinecone
    6. Weaviate
    7. ChromaDB
    8. Haystack
    9. Elasticsearch
    10. FAISS
    11. Cohere Rerank API
    12. Hugging Face Transformers
    13. PromptPerfect
    14. OpenAI Function Calling
    15. gpt-3.5-turbo
    16. Mistral-7B-Instruct-v0.2

    AI recommended 16 alternatives but never named alexgreensh/token-optimizer. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tools to identify and remove hidden token inefficiencies in large language model prompts?
    you: not recommended
    AI recommended (in order):
    1. OpenAI Tokenizer (tiktoken) (openai/tiktoken)
    2. Hugging Face Tokenizers Library (huggingface/tokenizers)
    3. LangChain (langchain-ai/langchain)
    4. Anthropic's Prompt Engineering Guide
    5. OpenAI's Cookbook (openai/openai-cookbook)
    6. FAISS (facebookresearch/faiss)
    7. Pinecone
    8. Weaviate (weaviate/weaviate)

    AI recommended 8 alternatives but never named alexgreensh/token-optimizer. 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 alexgreensh/token-optimizer?
    pass
    AI did not name alexgreensh/token-optimizer — 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 alexgreensh/token-optimizer in production, what risks or prerequisites should they evaluate first?
    pass
    AI did not name alexgreensh/token-optimizer — 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?

  • In one sentence, what problem does the repo alexgreensh/token-optimizer solve, and who is the primary audience?
    pass
    AI named alexgreensh/token-optimizer explicitly

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

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alexgreensh/token-optimizer — 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