QE Code Intelligence

Construct knowledge graphs and map dependencies from codebases.

2|2|Updated Aug 23, 2025
One-click install
npx skills add https://github.com/summarybotng/summarybot-ng --skill qe-code-intelligence
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: QE Code Intelligence
Source: https://github.com/summarybotng/summarybot-ng/tree/main/.claude/skills/qe-code-intelligence
Command: npx skills add https://github.com/summarybotng/summarybot-ng --skill qe-code-intelligence

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill streamlines complex code understanding by transforming raw code into actionable insights through knowledge graphs, semantic search, and optimized context retrieval.

Core Features & Use Cases

  • Knowledge Graph Construction: Builds a semantic map of your codebase, detailing entities and relationships.
  • Semantic Code Search: Finds code based on meaning, not just keywords, with high relevance.
  • Dependency Analysis: Maps intricate dependencies and calculates coupling metrics.
  • Token Reduction: Delivers concise, relevant code context, drastically reducing AI processing costs.
  • Use Case: A developer needs to understand how a specific feature is implemented across a large, unfamiliar codebase. This Skill can build a knowledge graph, semantically search for relevant functions, map their dependencies, and provide a highly condensed context for AI analysis.

Quick Start

Use the qe-code-intelligence skill to index your project's source code into a knowledge graph.

Frequently Asked Questions about QE Code Intelligence

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

FAQPage Schema
How do I build a knowledge graph from my source code to map dependencies?

You can build a knowledge graph by indexing your project's source code, which extracts entities, relationships, and complexity metrics to map intricate dependencies and calculate coupling metrics.

What is semantic code search and how does it find relevant functions?

Semantic code search finds code based on meaning rather than exact keywords, delivering highly relevant functions and context from your codebase to help understand feature implementation.

How can I reduce token usage when providing code context to AI for analysis?

You can reduce token usage through intelligent summarization and filtering, which delivers concise, relevant code context to drastically lower AI processing costs during context retrieval.

Does this code intelligence approach work for large unfamiliar codebases?

Yes, it streamlines complex code understanding for large unfamiliar codebases by transforming raw code into actionable insights through knowledge graphs and optimized semantic search.

What's the best way to analyze code complexity and coupling metrics across a project?

The best way to analyze code complexity is by constructing a semantic map of your codebase that details entities and relationships while calculating intricate dependency coupling metrics.

Can I extract entity relationships and complexity metrics without manual code review?

Yes, the system operates directly on codebases to automatically extract entities, relationships, and complexity metrics, transforming raw code into actionable insights without manual review.