RRepoGEO

REPOGEO REPORT · LITE

Pangu-Immortal/MagicWX

Default branch Ai · commit 6add92fe · scanned 6/4/2026, 4:02:26 PM

GitHub: 834 stars · 362 forks

AI VISIBILITY SCORE
35 /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
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 Pangu-Immortal/MagicWX, 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
    Add a clear, concise project description immediately after the H1

    Why:

    CURRENT
    The current README starts with a star prompt and a quote before the project overview.
    COPY-PASTE FIX
    Move the sentence "MagicWX 是一款 **Android 端侧大模型推理应用**，支持 **10 个主流 LLM** 一键下载与本地推理，无需服务器，完全离线运行。" to be the first descriptive paragraph directly under the H1.
  • mediumhomepage#2
    Add the repository URL as the homepage

    Why:

    COPY-PASTE FIX
    https://github.com/Pangu-Immortal/MagicWX

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 Pangu-Immortal/MagicWX
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
MLC LLM
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. MLC LLM · recommended 2×
  2. MediaPipe · recommended 2×
  3. TensorFlow Lite · recommended 2×
  4. PyTorch Mobile · recommended 2×
  5. ONNX Runtime · recommended 1×
  • CATEGORY QUERY
    How can I run large language models directly on an Android device without internet?
    you: not recommended
    AI recommended (in order):
    1. MLC LLM
    2. MediaPipe
    3. ONNX Runtime
    4. TensorFlow Lite
    5. PyTorch Mobile

    AI recommended 5 alternatives but never named Pangu-Immortal/MagicWX. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools enable efficient on-device LLM inference for Android with quantization support?
    you: not recommended
    AI recommended (in order):
    1. MediaPipe
    2. TensorFlow Lite
    3. ONNX Runtime Mobile
    4. PyTorch Mobile
    5. MLC LLM
    6. Qualcomm AI Engine Direct (QNN)
    7. Huawei MindSpore Lite

    AI recommended 7 alternatives but never named Pangu-Immortal/MagicWX. 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 Pangu-Immortal/MagicWX?
    pass
    AI named Pangu-Immortal/MagicWX explicitly

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

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

    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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