RRepoGEO

REPOGEO REPORT · LITE

alejandrobalderas/claude-code-from-source

Default branch main · commit a6d5e452 · scanned 6/30/2026, 4:07:54 AM

GitHub: 2,358 stars · 672 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
  • highabout#1
    Clarify the repository description to emphasize 'educational analysis'

    Why:

    CURRENT
    Architecture, patterns & internals of Anthropic's AI coding agent — reverse-engineered from source maps
    COPY-PASTE FIX
    An in-depth educational analysis of the architecture, patterns & internals of Anthropic's AI coding agent, reverse-engineered from source maps.
  • mediumlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the root of the repository with the text of a suitable open-source license (e.g., MIT or Apache-2.0) that aligns with the educational nature of the content.

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
LangChain
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 2×
  2. AutoGen · recommended 2×
  3. Django · recommended 1×
  4. FastAPI · recommended 1×
  5. Express.js · recommended 1×
  • CATEGORY QUERY
    What are the architectural patterns for building sophisticated AI coding assistants?
    you: not recommended
    AI recommended (in order):
    1. Django
    2. FastAPI
    3. Express.js
    4. Kubernetes
    5. NVIDIA Triton Inference Server
    6. Ray Serve
    7. Rust
    8. Go
    9. Language Server Protocol (LSP)
    10. rust-analyzer
    11. pyright
    12. Apache Kafka
    13. RabbitMQ
    14. Redis Pub/Sub
    15. Apache Flink
    16. Apache Spark Streaming
    17. MediatR
    18. NestJS CQRS
    19. PostgreSQL
    20. Apache Cassandra
    21. EventStoreDB
    22. RxPy
    23. RxJS
    24. Project Reactor
    25. Akka
    26. Socket.IO
    27. ASGI
    28. Django Channels
    29. VS Code Extension API
    30. JetBrains Plugin SDK
    31. ONNX Runtime
    32. TensorFlow Lite
    33. PyTorch Mobile
    34. Google Cloud Vertex AI
    35. AWS SageMaker
    36. Azure Machine Learning
    37. LangChain
    38. AutoGen
    39. CrewAI

    AI recommended 39 alternatives but never named alejandrobalderas/claude-code-from-source. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find resources to understand the design of production-grade AI agents?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. AutoGPT
    3. BabyAGI
    4. AutoGen
    5. Hugging Face Transformers Agents

    AI recommended 5 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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  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite