RRepoGEO

REPOGEO REPORT · LITE

meistrari/prompts-royale

Default branch main · commit 41217647 · scanned 6/9/2026, 7:07:36 AM

GitHub: 602 stars · 70 forks

AI VISIBILITY SCORE
33 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 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 meistrari/prompts-royale, 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 paragraph to highlight its core value as an LLM prompt evaluation platform

    Why:

    CURRENT
    Prompt engineering is an extremely iterative process. Even when we manage to settle down on a prompt, it's so difficult to test it against test cases and other possible prompts to make sure we're giving the best instructions to the model.

**Prompts Royale** is an application that allows you to really easily create many prompt candidates, write your own ones, and make them battle until a clear winner emerges.
    COPY-PASTE FIX
    Prompts Royale is an application designed for **systematic LLM prompt evaluation and comparison**. It helps prompt engineers and AI developers easily create, test, and make prompt candidates battle each other using Monte Carlo matchmaking and ELO rating to find the best performing instructions for any model.
  • hightopics#2
    Add more specific topics related to prompt evaluation and comparison

    Why:

    CURRENT
    ai, llm, nuxt, prompt, prompt-engineering
    COPY-PASTE FIX
    ai, llm, prompt, prompt-engineering, prompt-evaluation, prompt-testing, llm-evaluation, llm-testing, prompt-comparison, elo-rating
  • mediumlicense#3
    Clarify the project's license directly in the README

    Why:

    COPY-PASTE FIX
    ## License
This project is licensed under [insert specific license name(s) here, e.g., 'a custom license combining MIT and Apache-2.0']. Please 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 meistrari/prompts-royale
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Humanloop
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Humanloop · recommended 2×
  2. langchain-ai/langchain · recommended 1×
  3. run-llama/llama_index · recommended 1×
  4. wandb/wandb · recommended 1×
  5. Vellum · recommended 1×
  • CATEGORY QUERY
    How can I systematically compare and evaluate different LLM prompt engineering strategies?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. LlamaIndex (run-llama/llama_index)
    3. Weights & Biases (W&B Prompts) (wandb/wandb)
    4. Humanloop
    5. Vellum
    6. OpenAI Evals (openai/evals)
    7. PromptLayer (Magniv/promptlayer)
    8. ROUGE
    9. BLEU
    10. BERTScore (Tiiiger/bert_score)
    11. METEOR
    12. Surveymonkey
    13. Google Forms
    14. Scale AI
    15. Appen
    16. Amazon Mechanical Turk
    17. Argilla (argilla-io/argilla)
    18. Pandas (pandas-dev/pandas)
    19. NumPy (numpy/numpy)
    20. Matplotlib (matplotlib/matplotlib)
    21. Seaborn (mwaskom/seaborn)
    22. Plotly (plotly/plotly.py)

    AI recommended 22 alternatives but never named meistrari/prompts-royale. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help automate the generation and testing of large language model prompts?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. OpenAI Evals
    3. Weights & Biases Prompts
    4. Humanloop
    5. Guardrails AI
    6. PromptLayer
    7. LMQL

    AI recommended 7 alternatives but never named meistrari/prompts-royale. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 meistrari/prompts-royale?
    pass
    AI named meistrari/prompts-royale explicitly

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

  • If a team adopts meistrari/prompts-royale in production, what risks or prerequisites should they evaluate first?
    pass
    AI named meistrari/prompts-royale 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 meistrari/prompts-royale solve, and who is the primary audience?
    pass
    AI did not name meistrari/prompts-royale — 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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  • Brand-free category queries5 vs 2 in Lite
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