RRepoGEO

REPOGEO REPORT · LITE

drona23/claude-token-efficient

Default branch main · commit b32fa8b7 · scanned 5/17/2026, 8:32:49 PM

GitHub: 5,309 stars · 405 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 drona23/claude-token-efficient, 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 description to clarify its category

    Why:

    CURRENT
    > One file. Drop it in your project. Keeps responses terse and can reduce total tokens on output-heavy workflows.
    COPY-PASTE FIX
    > A prompt engineering technique (one CLAUDE.md file) to make Claude (and other LLMs) generate concise, terse responses. Drop it in your project to reduce output verbosity and save tokens on heavy workflows, without code changes.
  • hightopics#2
    Add relevant topics to improve categorization

    Why:

    COPY-PASTE FIX
    prompt-engineering, claude, llm, token-efficiency, output-control, verbosity-reduction, ai-prompts
  • mediumabout#3
    Update the 'About' description to reinforce its core purpose

    Why:

    CURRENT
    One CLAUDE.md file. Keeps Claude responses terse. Reduces output verbosity on heavy workflows. Drop-in, no code changes.
    COPY-PASTE FIX
    A prompt engineering file (CLAUDE.md) to make Claude (and other LLMs) generate concise, terse responses. Reduces output verbosity and saves tokens on heavy workflows, without code changes.

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 drona23/claude-token-efficient
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI Fine-tuning API
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI Fine-tuning API · recommended 2×
  2. Hugging Face Transformers · recommended 2×
  3. GPT-3.5 Turbo · recommended 1×
  4. GPT-4 · recommended 1×
  5. Mistral 7B / Mixtral 8x7B · recommended 1×
  • CATEGORY QUERY
    How to make AI models generate more concise, less chatty responses to save tokens?
    you: not recommended
    AI recommended (in order):
    1. OpenAI Fine-tuning API
    2. Hugging Face Transformers
    3. GPT-3.5 Turbo
    4. GPT-4
    5. Mistral 7B / Mixtral 8x7B
    6. Together.ai
    7. Anyscale
    8. Google Gemini Nano
    9. Python's `re` module
    10. NLTK
    11. spaCy
    12. gpt-3.5-turbo
    13. Tiktoken
    14. SentencePiece

    AI recommended 14 alternatives but never named drona23/claude-token-efficient. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tool to stop large language models from adding conversational filler and unsolicited advice?
    you: not recommended
    AI recommended (in order):
    1. OpenAI API
    2. Anthropic Claude
    3. Google Gemini API
    4. OpenAI Fine-tuning API
    5. Hugging Face Transformers
    6. Python with Regular Expressions
    7. LangChain
    8. NVIDIA NeMo Guardrails

    AI recommended 8 alternatives but never named drona23/claude-token-efficient. 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 drona23/claude-token-efficient?
    pass
    AI did not name drona23/claude-token-efficient — 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 drona23/claude-token-efficient in production, what risks or prerequisites should they evaluate first?
    pass
    AI named drona23/claude-token-efficient 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 drona23/claude-token-efficient solve, and who is the primary audience?
    pass
    AI did not name drona23/claude-token-efficient — 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?

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drona23/claude-token-efficient — 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