Transform code, documents, and images into a persistent knowledge graph.
npx skills add https://github.com/tiroq/praxis --skill graphify-tiroq
Please help me install this Agent Skill. Skill: graphify Source: https://github.com/tiroq/praxis/tree/main/.copilot/skills/graphify Command: npx skills add https://github.com/tiroq/praxis --skill graphify-tiroq
This Skill transforms diverse inputs like code, documents, and images into a navigable knowledge graph, enabling deeper understanding and efficient exploration of relationships and connections.
Run the 'graphify' command on your codebase directory or specific path to generate a knowledge graph.
High-intent search queries and answers about installing and using this skill.
To create a knowledge graph from a codebase or documents, you can use graphify to transform diverse inputs like code, text, and images into a navigable structure of nodes and edges. Run the 'graphify' command on your target directory to generate the graph.
Community detection in a knowledge graph identifies clusters of related concepts or entities within your codebase. It helps code analysis by grouping interconnected components, allowing you to quickly understand the architecture and explore dependencies between different parts.
Yes, graphify supports semantic extraction from PDFs and images to build a persistent knowledge graph. It utilizes dependencies like pdfplumber, pypdf, and opencv-python to process documents and multimedia inputs into interconnected nodes.
You can query the knowledge graph using built-in query tools to retrieve information and explore specific paths. This allows you to navigate the generated graph to find relationships between different parts of your code and understand structural dependencies.
Yes, you need Python installed along with the graphify library and various subagents or tools for full functionality. The environment requires dependencies like networkx for graph operations and google-api-python-client for processing.
The best way to understand relationships in a large codebase is converting it into a navigable knowledge graph. This approach enables deeper understanding and efficient exploration of connections, allowing you to quickly find dependencies and analyze the overall architecture.
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