RRepoGEO

REPOGEO REPORT · LITE

Windy3f3f3f3f/claude-code-from-scratch

Default branch main · commit 5e674776 · scanned 6/30/2026, 12:42:00 AM

GitHub: 1,472 stars · 400 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
27 /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
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 Windy3f3f3f3f/claude-code-from-scratch, 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 the README's opening to clarify it's a tutorial/implementation of a coding agent

    Why:

    CURRENT
    # Claude Code From Scratch

**一步一步，从零造一个 Claude Code**
    COPY-PASTE FIX
    # Claude Code From Scratch: A Step-by-Step Tutorial to Build Your Own AI Coding Agent

**一步一步，从零造一个 Claude Code**
  • mediumtopics#2
    Add more specific topics to emphasize the 'tutorial' and 'implementation' aspects

    Why:

    CURRENT
    ai, ai-agent, anthropic, build-from-scratch, claude, claude-code, coding-agent, llm, tutorial, typescript
    COPY-PASTE FIX
    ai, ai-agent, anthropic, build-from-scratch, claude, claude-code, coding-agent, llm, tutorial, typescript, llm-agent-tutorial, coding-agent-implementation, from-scratch-tutorial
  • mediumreadme#3
    Add a concise 'What problem does this solve?' statement near the top of the README

    Why:

    COPY-PASTE FIX
    This project addresses the challenge of understanding complex AI coding agents by providing a simplified, step-by-step implementation and tutorial, making advanced LLM architecture accessible to developers without needing to parse hundreds of thousands of lines of code.

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 Windy3f3f3f3f/claude-code-from-scratch
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI GPT-4
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI GPT-4 · recommended 1×
  2. GPT-3.5 Turbo · recommended 1×
  3. Anthropic Claude 3 · recommended 1×
  4. Google Gemini · recommended 1×
  5. Meta Llama 3 · recommended 1×
  • CATEGORY QUERY
    How to implement a sophisticated AI coding agent from the ground up?
    you: not recommended
    AI recommended (in order):
    1. OpenAI GPT-4
    2. GPT-3.5 Turbo
    3. Anthropic Claude 3
    4. Google Gemini
    5. Meta Llama 3
    6. Mistral AI
    7. LangChain
    8. LlamaIndex
    9. AutoGen
    10. Docker
    11. Jupyter Kernel Gateway
    12. Papermill
    13. Piston API
    14. Judge0
    15. GitPython
    16. PyGithub
    17. pytest
    18. JUnit
    19. NUnit
    20. Jest
    21. python-lsp-server
    22. PostgreSQL
    23. SQLite
    24. Chroma
    25. Pinecone
    26. Weaviate

    AI recommended 26 alternatives but never named Windy3f3f3f3f/claude-code-from-scratch. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a tutorial to understand the core architecture of advanced LLM-powered coding assistants in TypeScript.
    you: not recommended
    AI recommended (in order):
    1. OpenAI API (TypeScript SDK) (openai/openai-node)
    2. LangChain.js (langchain-ai/langchainjs)
    3. TypeScript Compiler API (microsoft/TypeScript)
    4. ts-morph (dsherret/ts-morph)
    5. Language Server Protocol (LSP) (microsoft/language-server-protocol)
    6. vscode-languageserver (microsoft/vscode-languageserver-node)
    7. VS Code Extension API (microsoft/vscode)

    AI recommended 7 alternatives but never named Windy3f3f3f3f/claude-code-from-scratch. 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 Windy3f3f3f3f/claude-code-from-scratch?
    pass
    AI did not name Windy3f3f3f3f/claude-code-from-scratch — 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 Windy3f3f3f3f/claude-code-from-scratch in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Windy3f3f3f3f/claude-code-from-scratch 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 Windy3f3f3f3f/claude-code-from-scratch solve, and who is the primary audience?
    pass
    AI did not name Windy3f3f3f3f/claude-code-from-scratch — 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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Windy3f3f3f3f/claude-code-from-scratch — 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