ck:gkg

Index and query code symbols across multi-language repositories via REST/HTTP and CLI.

Updated Aug 27, 2026
One-click install
npx skills add https://github.com/johngrandson/orkestry --skill ck-gkg-johngrandson
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: ck:gkg
Source: https://github.com/johngrandson/orkestry/tree/main/.claude/skills/gkg
Command: npx skills add https://github.com/johngrandson/orkestry --skill ck-gkg-johngrandson

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Navigate complex codebases by discovering symbol definitions, usages, and architecture relationships with a semantic knowledge graph.

Core Features & Use Cases

  • Go-to-definition, find-usages, and impact analysis across Ruby, Java, Kotlin, Python, and TypeScript/JavaScript projects.
  • Architecture visualization and cross-file references to understand dependencies.
  • MCP-enabled AI integration for programmatic code understanding.

Quick Start

Ask the AI to analyze a repository and generate a graph-ready summary of definitions and references.

Frequently Asked Questions about ck:gkg

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I find usages and go-to-definition across multi-language repositories?

To find usages and go-to-definition across multi-language repositories, this Skill indexes code symbols into a semantic knowledge graph. It enables rapid navigation and impact analysis for Ruby, Java, Kotlin, Python, and TypeScript/JavaScript projects via REST/HTTP and CLI tooling.

Can I use a semantic knowledge graph for code navigation in Python and Java projects?

Yes, you can use this semantic knowledge graph for code navigation in Python and Java projects. It supports multi-language repositories by indexing symbols to enable cross-file reference tracking and architecture visualization.

What do I need to index code symbols for impact analysis?

To index code symbols for impact analysis, you need initialized Git repositories and a server running with MCP endpoints. This environment exposes the necessary REST/HTTP and CLI tooling required to search and read definitions across your codebase.

How does MCP-enabled AI integration help with understanding complex codebases?

MCP-enabled AI integration helps with understanding complex codebases by allowing programmatic access to the semantic knowledge graph. You can ask the AI to analyze a repository and generate a graph-ready summary of definitions and references for rapid code navigation.

Does this code navigation tool support cross-file references for Ruby and Kotlin?

Yes, this code navigation tool supports cross-file references for Ruby and Kotlin. It indexes symbols across multi-language repositories to provide architecture visualization and track dependency relationships within your project.

What is the best way to visualize architecture dependencies across a multi-language codebase?

The best way to visualize architecture dependencies across a multi-language codebase is by querying a semantic knowledge graph. This Skill indexes symbols to map cross-file references, enabling you to understand structural relationships in Ruby, Java, Kotlin, Python, and TypeScript/JavaScript projects.