When should I use Symbols instead of the standard Read tool?

Use Symbols when you need to find where something is defined or understand the structure of the codebase. Use the Read tool only once you've identified the specific file you need to modify.

How does the Symbols skill save tokens?

It queries a lightweight index of your code signatures and summaries rather than loading full source files, reducing token consumption by 60% to 95%.

Does this skill work with any programming language?

Currently, the skill includes specialized extraction tools for TypeScript, React, and Python codebases.

What are architectural layers in this context?

Layers are tags such as 'router', 'service', or 'repository' that allow you to filter code based on its function within your application's architecture.

How do I generate the symbol graph for my project?

You run the provided extraction scripts (like extract_symbols_typescript.py) which read your project configuration and output the necessary JSON symbol maps.

Intelligent Codebase Symbols

Name: Intelligent Codebase Symbols
Author: miethe

bymiethe

개발자 도구

Navigates and analyzes complex codebases using token-efficient symbol graphs and architectural layer filtering.

소개

The Symbols skill transforms how Claude interacts with large-scale projects by providing a high-performance, token-efficient navigation layer. Instead of consuming the context window with full file contents, this skill utilizes pre-generated symbol graphs to query specific functions, classes, and architectural layers. It is specifically designed to reduce token usage by up to 95%, allowing for deeper codebase exploration, pattern discovery, and structural analysis across frontend and backend domains without hitting context limits.

주요 기능

Domain-specific chunking for targeted loading of UI, API, or Shared code
Token-efficient querying with 60-95% reduction in context usage
0 GitHub stars
Architectural layer awareness for filtering by service, repository, or router
Cross-language support for TypeScript, React, and Python environments
Progressive loading strategy to fetch only necessary context

사용 사례

Navigating multi-layered backend architectures to understand data flow
Locating specific UI components or hooks across large frontend repositories
Identifying existing implementation patterns before building new features

소개

주요 기능

Domain-specific chunking for targeted loading of UI, API, or Shared code
Token-efficient querying with 60-95% reduction in context usage
0 GitHub stars
Architectural layer awareness for filtering by service, repository, or router
Cross-language support for TypeScript, React, and Python environments
Progressive loading strategy to fetch only necessary context

사용 사례

Navigating multi-layered backend architectures to understand data flow
Locating specific UI components or hooks across large frontend repositories
Identifying existing implementation patterns before building new features