RRepoGEO

REPOGEO REPORT · LITE

pengchengneo/Claude-Code

Default branch main · commit b78dd22a · scanned 5/23/2026, 2:08:04 AM

GitHub: 1,396 stars · 2,368 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
17 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 pengchengneo/Claude-Code, 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 README H1 and opening line to clarify project's unique purpose

    Why:

    CURRENT
    # Claude Code 源码还原
> 从 `@anthropic-ai/claude-code` npm 包的 source map 中还原的完整 TypeScript 源码，**可本地运行**
    COPY-PASTE FIX
    # Claude Code 源码还原：揭秘 Anthropic Claude 隐藏功能
> 从 `@anthropic-ai/claude-code` npm 包的 source map 中还原的完整 TypeScript 源码，**可本地运行**，旨在深入探索 Anthropic Claude 的内部机制与未公开功能。
  • hightopics#2
    Add relevant topics to the repository

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    anthropic, claude, llm, reverse-engineering, source-code, hidden-features, internal-mechanisms, typescript, source-map
  • highlicense#3
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected)
    COPY-PASTE FIX
    Create a LICENSE file in the repository root with the text of the MIT License.

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 pengchengneo/Claude-Code
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
GitHub
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. GitHub · recommended 1×
  2. huggingface/transformers · recommended 1×
  3. pytorch/pytorch · recommended 1×
  4. tensorflow/tensorflow · recommended 1×
  5. OpenAI Developer Documentation & Blog · recommended 1×
  • CATEGORY QUERY
    How to explore unreleased features or experimental functionalities within a large language model's codebase?
    you: not recommended
    AI recommended (in order):
    1. GitHub
    2. Hugging Face Transformers (huggingface/transformers)
    3. PyTorch (pytorch/pytorch)
    4. TensorFlow (tensorflow/tensorflow)
    5. OpenAI Developer Documentation & Blog
    6. Google AI Blog & Google Cloud AI Documentation
    7. Hugging Face Blog & Documentation
    8. Microsoft Azure AI Blog & Documentation
    9. Hugging Face Discord Server
    10. OpenAI Developer Forum
    11. Reddit
    12. arXiv.org
    13. Semantic Scholar
    14. Google Scholar
    15. OpenAI API Playground
    16. Google Cloud AI Platform
    17. Postman
    18. cURL (curl/curl)

    AI recommended 18 alternatives but never named pengchengneo/Claude-Code. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools allow local execution and deep inspection of a major AI assistant's internal mechanisms?
    you: not recommended
    AI recommended (in order):
    1. OpenAI API
    2. Hugging Face Transformers
    3. Llama.cpp
    4. LangChain
    5. LlamaIndex
    6. Weights & Biases (W&B)
    7. MLflow
    8. InterpretML
    9. LIME
    10. SHAP
    11. DeepSpeed
    12. Accelerate

    AI recommended 12 alternatives but never named pengchengneo/Claude-Code. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 pengchengneo/Claude-Code?
    pass
    AI did not name pengchengneo/Claude-Code — 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 pengchengneo/Claude-Code in production, what risks or prerequisites should they evaluate first?
    pass
    AI named pengchengneo/Claude-Code 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 pengchengneo/Claude-Code solve, and who is the primary audience?
    pass
    AI did not name pengchengneo/Claude-Code — 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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pengchengneo/Claude-Code — RepoGEO report