RRepoGEO

REPOGEO REPORT · LITE

scaledown-team/scaledown

Default branch main · commit 43826532 · scanned 6/4/2026, 3:58:53 PM

GitHub: 872 stars · 1,216 forks

AI VISIBILITY SCORE
30 /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
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 scaledown-team/scaledown, 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
    Add a clear 'About' description to correct AI's domain misunderstanding

    Why:

    COPY-PASTE FIX
    Intelligent context optimization framework that reduces LLM token usage while preserving semantic meaning through intelligent code selection and prompt compression.
  • hightopics#2
    Add relevant topics to improve category visibility

    Why:

    COPY-PASTE FIX
    llm, large-language-models, context-optimization, token-reduction, prompt-compression, code-selection, semantic-search, ai-tools
  • mediumreadme#3
    Reinforce LLM context optimization in README's opening

    Why:

    CURRENT
    # ScaleDown

ScaleDown is an intelligent context optimization framework that reduces LLM token usage while preserving semantic meaning through intelligent code selection and prompt compression.
    COPY-PASTE FIX
    # ScaleDown: LLM Context Optimization Framework

ScaleDown is an intelligent context optimization framework designed for Large Language Models (LLMs) that reduces token usage while preserving semantic meaning through intelligent code selection and prompt compression.

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 scaledown-team/scaledown
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 2×
  2. LlamaIndex · recommended 2×
  3. Pinecone · recommended 1×
  4. Weaviate · recommended 1×
  5. Chroma · recommended 1×
  • CATEGORY QUERY
    How can I reduce LLM token consumption and optimize prompt context for cost efficiency?
    you: not recommended
    AI recommended (in order):
    1. Pinecone
    2. Weaviate
    3. Chroma
    4. LangChain
    5. LlamaIndex
    6. gpt-3.5-turbo
    7. Mistral 7B / Mixtral 8x7B
    8. Hugging Face
    9. t5-small
    10. bert-base-uncased
    11. Mistral AI
    12. Llama 2
    13. tiktoken
    14. LangSmith
    15. Helicone

    AI recommended 15 alternatives but never named scaledown-team/scaledown. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools intelligently compress prompts and select relevant code for large language models?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack
    4. OpenAI API
    5. Faiss
    6. Annoy
    7. Hnswlib
    8. Hugging Face Transformers
    9. Sentence Transformers
    10. spaCy
    11. NLTK
    12. ChromaDB
    13. Qdrant

    AI recommended 13 alternatives but never named scaledown-team/scaledown. 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 scaledown-team/scaledown?
    pass
    AI named scaledown-team/scaledown explicitly

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

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

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

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MARKDOWN (README)
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scaledown-team/scaledown — 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