RRepoGEO

REPOGEO REPORT · LITE

FANzR-arch/Numerologist_skills

Default branch main · commit 847a38ed · scanned 5/30/2026, 9:43:01 PM

GitHub: 745 stars · 142 forks

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 FANzR-arch/Numerologist_skills, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Clarify README H1 and opening sentence to emphasize LLM hallucination prevention in traditional knowledge

    Why:

    CURRENT
    # Numerologist Skills (AI 术数工程化)

本项目把传统术数相关的 AI skill 拆成可审计、可复用、可逐步扩展的工程模块。目标不是把模型包装成“更会玄学”，而是尽量减少它在排盘、流派口径、步骤顺序和解释链路上的幻觉。
    COPY-PASTE FIX
    # Numerologist Skills: An Engineering Framework to Stop LLM Hallucinations in Traditional Knowledge Systems

本项目是一个工程化框架，旨在减少大型语言模型（LLM）在解释奇门遁甲、紫微斗数等传统术数时产生的幻觉。它将传统术数相关的AI技能拆解为可审计、可复用、可逐步扩展的工程模块，目标是固定排盘步骤、统一流派口径，并减少解释链路上的不确定性。
  • mediumlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root. A common choice for open-source projects is the MIT License, but choose one that aligns with your project's distribution goals.

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 FANzR-arch/Numerologist_skills
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 1×
  2. Pinecone · recommended 1×
  3. Weaviate · recommended 1×
  4. OpenAI API · recommended 1×
  5. Hugging Face Transformers · recommended 1×
  • CATEGORY QUERY
    How to prevent large language models from hallucinating when interpreting complex traditional knowledge systems?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. Pinecone
    3. Weaviate
    4. OpenAI API
    5. Hugging Face Transformers
    6. Llama 2
    7. Mistral
    8. Argilla
    9. Snorkel AI
    10. Anthropic Claude
    11. Google Gemini
    12. Neo4j
    13. Stardog

    AI recommended 13 alternatives but never named FANzR-arch/Numerologist_skills. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What frameworks help integrate external scripts and structured references for accurate LLM domain applications?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. LlamaIndex (run-llama/llama_index)
    3. Haystack (deepset-ai/haystack)
    4. OpenAI Functions
    5. DSPy (stanfordnlp/dspy)
    6. Mojo (modularml/mojo)
    7. Semantic Kernel (microsoft/semantic-kernel)

    AI recommended 7 alternatives but never named FANzR-arch/Numerologist_skills. 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 FANzR-arch/Numerologist_skills?
    pass
    AI did not name FANzR-arch/Numerologist_skills — 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 FANzR-arch/Numerologist_skills in production, what risks or prerequisites should they evaluate first?
    pass
    AI named FANzR-arch/Numerologist_skills 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 FANzR-arch/Numerologist_skills solve, and who is the primary audience?
    pass
    AI did not name FANzR-arch/Numerologist_skills — 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
  • Prioritized action items8 vs 3 in Lite