RRepoGEO

REPOGEO REPORT · LITE

alexgreensh/token-optimizer

Default branch main · commit 7e8d2d17 · scanned 6/19/2026, 10:56:23 AM

GitHub: 1,374 stars · 113 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
35 /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
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 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 main heading to highlight the LLM-powered differentiator

    Why:

    CURRENT
    Cut the tokens you waste. Keep the work you'd lose.
    COPY-PASTE FIX
    Optimize LLM context windows and reduce token costs by intelligently identifying and eliminating 'ghost tokens' and redundancies using an LLM-powered approach. Prevent context quality decay in your AI agent applications.
  • highhomepage#2
    Add the project homepage to the repository's 'About' section

    Why:

    COPY-PASTE FIX
    https://alexgreensh.github.io/token-optimizer/
  • mediumlicense#3
    Clarify the existing license(s) directly in the README

    Why:

    COPY-PASTE FIX
    Add a section or line in your README, for example: 'This project is licensed under [Specify License Type(s) here, e.g., a custom license combining X and Y]. 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
https://github.com/langchain-ai/langchain
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. https://github.com/langchain-ai/langchain · recommended 2×
  2. https://github.com/run-llama/llama_index · recommended 2×
  3. Pinecone · recommended 2×
  4. GPT-3.5 Turbo · recommended 1×
  5. Claude 3 Haiku · recommended 1×
  • CATEGORY QUERY
    How to reduce token usage and optimize LLM costs for AI agent applications?
    you: not recommended
    AI recommended (in order):
    1. LangChain (https://github.com/langchain-ai/langchain)
    2. LlamaIndex (https://github.com/run-llama/llama_index)
    3. GPT-3.5 Turbo
    4. Claude 3 Haiku
    5. Mixtral 8x7B
    6. Anyscale Endpoints
    7. Together AI
    8. Perplexity AI
    9. Google Gemini 1.5 Flash
    10. OpenAI Fine-tuning API
    11. Hugging Face AutoTrain (https://github.com/huggingface/autotrain-advanced)
    12. Redis (https://github.com/redis/redis)
    13. Memcached (https://github.com/memcached/memcached)
    14. LangChain Cache
    15. PostgreSQL (https://github.com/postgres/postgres)
    16. LlamaIndex (https://github.com/run-llama/llama_index)
    17. LangChain (https://github.com/langchain-ai/langchain)
    18. Pinecone
    19. Weaviate (https://github.com/weaviate/weaviate)
    20. Chroma (https://github.com/chroma-core/chroma)
    21. Pydantic (https://github.com/pydantic/pydantic)
    22. Guidance (https://github.com/microsoft/guidance)

    AI recommended 22 alternatives but never named alexgreensh/token-optimizer. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tools to manage LLM context window effectively and prevent AI model quality degradation?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. LlamaIndex (run-llama/llama_index)
    3. OpenAI API
    4. Weaviate (weaviate/weaviate)
    5. Pinecone
    6. Qdrant (qdrant/qdrant)
    7. Weights & Biases (wandb/wandb)
    8. Guidance (microsoft/guidance)

    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 named alexgreensh/token-optimizer explicitly

    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 named alexgreensh/token-optimizer 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 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