How does Kosmos X-Ray save tokens?

It uses AST parsing to extract only the structure (classes, methods, signatures) of a file, omitting the internal implementation code which often accounts for the majority of a file's token count.

Does it support visual diagrams?

Yes, it can generate Mermaid-formatted output for its dependency graph tool, which can be rendered in Markdown viewers to visualize system architecture.

Can it handle Pydantic models?

Yes, the enhanced skeleton tool explicitly captures Pydantic and dataclass fields, ensuring data structures are visible even when the logic implementation is hidden.

What is a 'context hazard'?

A context hazard refers to exceptionally large files that could consume an entire AI context window; the mapper tool identifies these early so you can use skeleton views instead.

Kosmos X-Ray

Name: Kosmos X-Ray
Author: jimmc414

byjimmc414

•

324

データサイエンスとML

Explores and maps complex codebases using AST analysis to reduce token usage by 95% while maintaining structural visibility.

概要

Kosmos X-Ray is a specialized diagnostic tool designed for high-efficiency codebase exploration within limited context windows. By leveraging Abstract Syntax Tree (AST) parsing, it extracts critical structural elements—such as Pydantic fields, decorators, and class hierarchies—without loading full implementation details. This skill is essential for developers working with large-scale projects like the Kosmos AI Scientist, enabling rapid onboarding, architectural visualization via Mermaid diagrams, and precise context management for AI-driven coding tasks.

主な機能

Directory mapping with token budget estimates and hazard detection
324 GitHub stars
Automated Mermaid diagram generation for architectural dependency graphs
Priority-based filtering to focus on critical modules and core interfaces
Support for Pydantic/dataclass field visibility and decorator mapping
AST-based skeleton extraction for Python files with ~95% token savings

ユースケース

Rapidly onboarding to complex AI research codebases without exceeding context limits
Generating architectural documentation and dependency maps for large Python projects
Identifying large files and context hazards before performing deep-dive code analysis

概要

主な機能

Directory mapping with token budget estimates and hazard detection
324 GitHub stars
Automated Mermaid diagram generation for architectural dependency graphs
Priority-based filtering to focus on critical modules and core interfaces
Support for Pydantic/dataclass field visibility and decorator mapping
AST-based skeleton extraction for Python files with ~95% token savings

ユースケース

Rapidly onboarding to complex AI research codebases without exceeding context limits
Generating architectural documentation and dependency maps for large Python projects
Identifying large files and context hazards before performing deep-dive code analysis