Can it handle Pydantic models and dataclasses?

Yes, the enhanced skeleton tool specifically captures Pydantic fields, decorators, and constants that standard parsers often miss.

Does it support visual documentation?

Yes, the dependency graph tool can generate Mermaid diagrams directly from your import structure for use in READMEs or documentation.

How does Kosmos X-Ray save tokens?

It uses AST parsing to extract only the structure (classes, signatures, fields) of your code, ignoring implementation logic to reduce data size by up to 95%.

When should I use this instead of reading a full file?

Use it when you need to understand the interface or data structure of a module without needing to see the internal implementation details, especially in large files.

Kosmos X-Ray Code Explorer

Name: Kosmos X-Ray Code Explorer
Author: jimmc414

byjimmc414

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324

생산성 및 워크플로

Explores and maps complex codebases using AST analysis to provide high-level structural insights while minimizing token consumption.

소개

Kosmos X-Ray is a specialized context-management tool designed to help developers and AI agents navigate large Python codebases with surgical precision. By utilizing AST (Abstract Syntax Tree) parsing, it extracts the skeleton of a project—including Pydantic models, class signatures, and decorators—without loading full implementation details, resulting in a 95% reduction in token usage. This is particularly valuable for understanding the architecture of the Kosmos AI Scientist framework, generating onboarding documentation, and managing limited context windows during complex development tasks.

주요 기능

324 GitHub stars
Automated dependency graph generation with Mermaid diagram support
Configurable priority filters to focus on critical architectural components
Line-number mapping for direct navigation to specific definitions within files
AST-based skeleton extraction for Python classes, methods, and Pydantic fields
Directory structure mapping with integrated token budget and hazard estimation

사용 사례

Managing large files by viewing interface skeletons instead of full implementation
Rapid onboarding for AI programmers to understand large-scale system architectures
Generating architectural documentation and visual dependency diagrams for research projects

소개

주요 기능

324 GitHub stars
Automated dependency graph generation with Mermaid diagram support
Configurable priority filters to focus on critical architectural components
Line-number mapping for direct navigation to specific definitions within files
AST-based skeleton extraction for Python classes, methods, and Pydantic fields
Directory structure mapping with integrated token budget and hazard estimation

사용 사례

Managing large files by viewing interface skeletons instead of full implementation
Rapid onboarding for AI programmers to understand large-scale system architectures
Generating architectural documentation and visual dependency diagrams for research projects