update-logic-index

Name: update-logic-index
Availability: InStock
Author: Till-Crazy-Tears-Us-Apart

Generate semantic summaries of Python code using AST and LLM analysis.

27|1|Updated Jan 9, 2026

Search Tags:#code analysis #data flow #python #llm #ast #semantic indexing

Ready to run

npx skills add https://github.com/Till-Crazy-Tears-Us-Apart/Claude-Code-Engineering-Suite --skill update-logic-index

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: update-logic-index
Source: https://github.com/Till-Crazy-Tears-Us-Apart/Claude-Code-Engineering-Suite/tree/main/skills/update-logic-index
Command: npx skills add https://github.com/Till-Crazy-Tears-Us-Apart/Claude-Code-Engineering-Suite --skill update-logic-index

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill helps you quickly grasp the logic and data flow within a Python codebase by generating concise semantic summaries, eliminating the need to read through extensive source code.

Core Features & Use Cases

AST-based Analysis: Parses Python code to understand classes, functions, and their relationships.
Cross-File Context: Tracks imports and injects summaries of dependent modules to provide a holistic view.
Data Flow Tagging: Identifies data sources [Source] and sinks [Sink] to visualize data movement.
Use Case: When onboarding to a new project, use this Skill to generate a high-level understanding of key functions and how data flows between them, significantly speeding up your learning curve.

Quick Start

Use the update-logic-index skill to refresh the codebase's semantic understanding.

Frequently Asked Questions about update-logic-index

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

FAQPage Schema

How do I understand Python code logic without reading the source files?▼

You can understand Python code logic without reading source by generating semantic summaries using AST parsing and LLM analysis. This approach extracts classes, functions, and relationships to provide a high-level view of project logic for efficient codebase comprehension.

What is the best way to track data flow across multiple Python files?▼

The best way to track data flow across Python files is using semantic indexing with cross-file context tracking. It monitors imports, injects summaries of dependent modules, and tags data sources and sinks to visualize how data moves between functions.

How does AST analysis help with LLM code comprehension in large projects?▼

AST analysis helps LLM code comprehension by parsing Python code structures into abstract syntax trees. This allows the LLM to understand function relationships and generate semantic summaries, enabling incremental updates and dependency tracking for large projects.

Can I use semantic code indexing for incremental updates in a complex Python codebase?▼

Yes, semantic code indexing supports incremental updates in complex Python codebases. It parses AST structures and uses LLM analysis to refresh semantic understanding, tracking dependencies and identifying data sources and sinks efficiently.

Does this semantic code analysis tool work with data source and sink identification?▼

Yes, this semantic code analysis tool works with data source and sink identification. It uses AST parsing and LLM analysis to tag data sources and sinks, visualizing data movement and helping you understand data flow without reading source code.