RRepoGEO

REPOGEO REPORT · LITE

Turing-Project/AntiFraudChatBot

Default branch main · commit a912198e · scanned 6/26/2026, 7:52:59 PM

GitHub: 2,230 stars · 420 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)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
28 /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
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 Turing-Project/AntiFraudChatBot, 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
    Reposition the README's opening sentence to highlight anti-fraud and WeChat integration

    Why:

    CURRENT
    A simple prompt-chatting AI based on wechaty and fintuned NLP model
    COPY-PASTE FIX
    An open-source anti-fraud chatbot framework for WeChat, powered by a large Chinese pre-trained language model (YUAN1.0) and Wechaty.
  • mediumhomepage#2
    Add a project homepage URL

    Why:

    COPY-PASTE FIX
    https://turing-project.github.io/AntiFraudChatBot (or a similar dedicated project page)

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 Turing-Project/AntiFraudChatBot
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Rasa
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Rasa · recommended 1×
  2. DeepPavlov · recommended 1×
  3. OpenNMT · recommended 1×
  4. spaCy · recommended 1×
  5. Hugging Face Transformers · recommended 1×
  • CATEGORY QUERY
    What are the best open-source AI frameworks for developing anti-fraud chatbots on messaging apps?
    you: not recommended
    AI recommended (in order):
    1. Rasa
    2. DeepPavlov
    3. OpenNMT
    4. spaCy
    5. Hugging Face Transformers
    6. NLTK

    AI recommended 6 alternatives but never named Turing-Project/AntiFraudChatBot. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to integrate large Chinese language models into a real-time, modular chatbot system?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. FastAPI (tiangolo/fastapi)
    3. Flask (pallets/flask)
    4. PyTorch (pytorch/pytorch)
    5. TensorFlow (tensorflow/tensorflow)
    6. OpenNMT-py (OpenNMT/OpenNMT-py)
    7. WeLM
    8. ERNIE Bot
    9. Tongyi Qianwen
    10. NVIDIA Triton Inference Server (triton-inference-server/server)
    11. ONNX Runtime (microsoft/onnxruntime)
    12. Rasa Open Source (RasaHQ/rasa)

    AI recommended 12 alternatives but never named Turing-Project/AntiFraudChatBot. 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 Turing-Project/AntiFraudChatBot?
    pass
    AI named Turing-Project/AntiFraudChatBot explicitly

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

  • If a team adopts Turing-Project/AntiFraudChatBot in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Turing-Project/AntiFraudChatBot 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 Turing-Project/AntiFraudChatBot solve, and who is the primary audience?
    pass
    AI did not name Turing-Project/AntiFraudChatBot — 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?

Embed your GEO score

Drop this badge into the README of Turing-Project/AntiFraudChatBot. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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Turing-Project/AntiFraudChatBot — 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