RRepoGEO

REPOGEO REPORT · LITE

he-yufeng/CoreCoder

Default branch main · commit 0cdebf91 · scanned 5/31/2026, 5:57:06 PM

GitHub: 1,043 stars · 257 forks

AI VISIBILITY SCORE
40 /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
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 he-yufeng/CoreCoder, 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
    Add a clear 'blueprint' statement immediately after the H1

    Why:

    CURRENT
    The README's first content after the H1 is a note about renaming, not the core value proposition.
    COPY-PASTE FIX
    Add the following sentence directly after the `# CoreCoder` heading: `CoreCoder is a minimal Python blueprint for AI coding agents, inspired by Claude Code. It's the nanoGPT for understanding agent design patterns.`
  • mediumtopics#2
    Add topics emphasizing CoreCoder's role as a blueprint/reference

    Why:

    CURRENT
    ai-agent, claude-code, cli, coding-agent, corecoder, deepseek, developer-tools, llm, openai, python
    COPY-PASTE FIX
    ai-agent, claude-code, cli, coding-agent, corecoder, deepseek, developer-tools, llm, openai, python, agent-design-patterns, reference-implementation, educational-codebase, nano-agent
  • lowabout#3
    Refine the repository description to explicitly state its 'blueprint' nature

    Why:

    CURRENT
    Minimal AI coding agent (~1,400 LoC Python) inspired by Claude Code. Works with any LLM. Think NanoGPT for coding agents. Formerly NanoCoder.
    COPY-PASTE FIX
    A minimal Python blueprint (~1,400 LoC) for AI coding agents, inspired by Claude Code. Works with any LLM. Think NanoGPT for understanding agent design patterns. Formerly NanoCoder.

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 he-yufeng/CoreCoder
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI
Recommended in 3 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI · recommended 3×
  2. LangChain · recommended 2×
  3. LlamaIndex · recommended 2×
  4. Anthropic · recommended 2×
  5. BabyAGI · recommended 1×
  • CATEGORY QUERY
    Seeking a minimal Python codebase to understand core AI coding agent design patterns.
    you: not recommended
    AI recommended (in order):
    1. BabyAGI
    2. MiniGPT4
    3. LangChain
    4. LlamaIndex
    5. CrewAI

    AI recommended 5 alternatives but never named he-yufeng/CoreCoder. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are simple ways to create a custom AI code generation assistant using any LLM?
    you: not recommended
    AI recommended (in order):
    1. ChatGPT Custom Instructions
    2. Custom GPTs
    3. OpenAI
    4. GPT-3.5
    5. GPT-4
    6. Google Gemini Advanced Custom Instructions
    7. Gemini Ultra
    8. LangChain
    9. OpenAI
    10. Anthropic
    11. Google LLMs
    12. PromptTemplate
    13. ChatOpenAI
    14. ChatAnthropic
    15. ChatGoogleGenerativeAI
    16. LlamaIndex
    17. OpenAI
    18. Anthropic
    19. Google
    20. OpenAI API
    21. Anthropic API
    22. Google AI Studio
    23. Hugging Face Transformers Library
    24. CodeLlama
    25. StarCoder
    26. deepseek-coder
    27. Phi-2

    AI recommended 27 alternatives but never named he-yufeng/CoreCoder. 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 he-yufeng/CoreCoder?
    pass
    AI named he-yufeng/CoreCoder explicitly

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

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

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

Embed your GEO score

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MARKDOWN (README)
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he-yufeng/CoreCoder — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
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