RRepoGEO

REPOGEO REPORT · LITE

he-yufeng/CoreCoder

Default branch main · commit 61fc02c0 · scanned 6/30/2026, 6:01:44 PM

GitHub: 1,425 stars · 314 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
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
    Reposition README's opening sentence to explicitly state Python AI agent for learning

    Why:

    CURRENT
    **The nanoGPT of coding agents. 1,081 lines of pure Python — understand how a coding agent actually works, then fork your own.learn from it · fork it · ship something better*
    COPY-PASTE FIX
    **CoreCoder is a minimal Python AI coding agent (~1,000 lines) designed for learning and forking. Inspired by Claude Code, it helps you understand how a coding agent actually works.**
  • highreadme#2
    Add a dedicated 'About' section to the README for clarity

    Why:

    COPY-PASTE FIX
    ## About CoreCoder
CoreCoder is the "nanoGPT" for AI coding agents. It's a highly readable, ~1,000-line Python implementation of a coding agent, designed to be easily understood, hacked, and forked. Unlike complex production systems, CoreCoder focuses on the minimal core, making it an ideal educational tool to learn the architecture of LLM-powered coding assistants.
  • mediumtopics#3
    Expand GitHub topics with educational and learning-focused keywords

    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, educational, learning, minimal-example, llm-agent-architecture

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
CrewAI
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. CrewAI · recommended 2×
  2. LangChain · recommended 1×
  3. OpenAI's GPT-3.5/GPT-4 · recommended 1×
  4. LlamaIndex · recommended 1×
  5. Auto-GPT · recommended 1×
  • CATEGORY QUERY
    Looking for a minimal Python AI agent example to learn coding assistant architecture.
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. OpenAI's GPT-3.5/GPT-4
    3. CrewAI
    4. LlamaIndex
    5. Auto-GPT
    6. Haystack

    AI recommended 6 alternatives but never named he-yufeng/CoreCoder. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are some lightweight, hackable AI coding agents that support various LLMs?
    you: not recommended
    AI recommended (in order):
    1. AutoGPT
    2. BabyAGI
    3. CrewAI
    4. OpenDevin
    5. LangChain Agents
    6. LlamaIndex Agents

    AI recommended 6 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?

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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
  • Prioritized action items8 vs 3 in Lite