RRepoGEO

REPOGEO REPORT · LITE

alejandrobalderas/claude-code-from-source

Default branch main · commit a6d5e452 · scanned 5/18/2026, 9:18:56 PM

GitHub: 1,939 stars · 586 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
10 /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
0 / 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 alejandrobalderas/claude-code-from-source, 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
    Reposition README's opening to clarify educational purpose

    Why:

    CURRENT
    # Claude Code from Source

**Architecture, Patterns & Internals of Anthropic's AI Coding Agent**
    COPY-PASTE FIX
    # Claude Code from Source: An Educational Deep Dive into AI Agent Architecture

**This repository is an educational resource and technical deep dive into the architecture, patterns, and internals of Anthropic's AI coding agent, reverse-engineered from source maps.**
  • mediumhomepage#2
    Add the project homepage to repository metadata

    Why:

    COPY-PASTE FIX
    https://claude-code-from-source.com

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 alejandrobalderas/claude-code-from-source
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Django
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Django · recommended 1×
  2. Ruby on Rails · recommended 1×
  3. ASP.NET Core · recommended 1×
  4. Spring Boot · recommended 1×
  5. Kubernetes · recommended 1×
  • CATEGORY QUERY
    What are common architectural patterns for building an AI coding assistant?
    you: not recommended
    AI recommended (in order):
    1. Django
    2. Ruby on Rails
    3. ASP.NET Core
    4. Spring Boot
    5. Kubernetes
    6. Docker
    7. gRPC
    8. FastAPI
    9. AWS Lambda
    10. Google Cloud Functions
    11. Azure Functions
    12. Apache Kafka
    13. RabbitMQ
    14. AWS SQS
    15. Google Cloud Pub/Sub
    16. Azure Service Bus
    17. Celery
    18. TensorFlow.js
    19. ONNX Runtime
    20. WebAssembly (Wasm)
    21. Hugging Face Transformers
    22. PyTorch
    23. TensorFlow
    24. NVIDIA Triton Inference Server
    25. OpenAI API
    26. Anthropic Claude API
    27. Google Cloud Vertex AI
    28. AWS SageMaker
    29. LangChain
    30. LlamaIndex

    AI recommended 30 alternatives but never named alejandrobalderas/claude-code-from-source. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How do large-scale AI code generation systems structure their components internally?
    you: not recommended
    AI recommended (in order):
    1. GitHub Copilot
    2. Amazon CodeWhisperer
    3. VS Code (microsoft/vscode)
    4. IntelliJ IDEA
    5. OpenAI's GPT-4
    6. Google's PaLM 2
    7. Meta's LLaMA 2
    8. OpenAI's Codex
    9. Salesforce's CodeT5
    10. PEP 8
    11. ESLint (eslint/eslint)
    12. Prettier (prettier/prettier)
    13. Black (psf/black)
    14. Snyk
    15. Checkmarx
    16. ChatGPT
    17. Copilot

    AI recommended 17 alternatives but never named alejandrobalderas/claude-code-from-source. 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 alejandrobalderas/claude-code-from-source?
    pass
    AI did not name alejandrobalderas/claude-code-from-source — 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 alejandrobalderas/claude-code-from-source in production, what risks or prerequisites should they evaluate first?
    pass
    AI did not name alejandrobalderas/claude-code-from-source — 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?

  • In one sentence, what problem does the repo alejandrobalderas/claude-code-from-source solve, and who is the primary audience?
    pass
    AI did not name alejandrobalderas/claude-code-from-source — 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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alejandrobalderas/claude-code-from-source — 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