qe-code-intelligence — agenticqe qe-code-intelligence, agentic-qe, community, agenticqe, ide skills, agenticsfoundation, quality-engineering, Claude Code, Cursor, Windsurf

v1.0.0

이 스킬 정보

Perfect for Coding Agents needing advanced code intelligence and semantic search capabilities. Agentic QE Fleet is an open-source AI-powered QA/QE platform designed for use with Coding Agents (works best with Claude Code) featuring specialized agents and skills to support testing activities for a product at any stage of the SDLC. Free to use, fork, build, and contribute. Based on the Agentic QE Framework created by Dragan Spiridonov.

# Core Topics

proffesor-for-testing proffesor-for-testing
[276]
[57]
Updated: 3/26/2026

Killer-Skills Review

Decision support comes first. Repository text comes second.

Reference-Only Page Review Score: 7/11

This page remains useful for operators, but Killer-Skills treats it as reference material instead of a primary organic landing page.

Original recommendation layer Concrete use-case guidance Explicit limitations and caution
Review Score
7/11
Quality Score
49
Canonical Locale
en
Detected Body Locale
en

Perfect for Coding Agents needing advanced code intelligence and semantic search capabilities. Agentic QE Fleet is an open-source AI-powered QA/QE platform designed for use with Coding Agents (works best with Claude Code) featuring specialized agents and skills to support testing activities for a product at any stage of the SDLC. Free to use, fork, build, and contribute. Based on the Agentic QE Framework created by Dragan Spiridonov.

이 스킬을 사용하는 이유

Empowers agents to leverage knowledge graph construction, semantic code search, and dependency mapping using libraries like TypeScript, and protocols such as incremental indexing, providing significant token reduction and context-aware code understanding.

최적의 용도

Perfect for Coding Agents needing advanced code intelligence and semantic search capabilities.

실행 가능한 사용 사례 for qe-code-intelligence

Analyzing change impact on large codebases
Searching for code semantically with reduced context
Building knowledge graphs for complex projects
Mapping dependencies and identifying hotspots
Reducing context for AI operations with token reduction

! 보안 및 제한 사항

  • Requires TypeScript files and Agentic QE Framework
  • Limited to coding languages supported by the framework
  • Needs incremental indexing for optimal performance

Why this page is reference-only

  • - Current locale does not satisfy the locale-governance contract.
  • - The underlying skill quality score is below the review floor.

Source Boundary

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

After The Review

Decide The Next Action Before You Keep Reading Repository Material

Killer-Skills should not stop at opening repository instructions. It should help you decide whether to install this skill, when to cross-check against trusted collections, and when to move into workflow rollout.

Labs Demo

Browser Sandbox Environment

⚡️ Ready to unleash?

Experience this Agent in a zero-setup browser environment powered by WebContainers. No installation required.

Boot Container Sandbox

FAQ & Installation Steps

These questions and steps mirror the structured data on this page for better search understanding.

? Frequently Asked Questions

What is qe-code-intelligence?

Perfect for Coding Agents needing advanced code intelligence and semantic search capabilities. Agentic QE Fleet is an open-source AI-powered QA/QE platform designed for use with Coding Agents (works best with Claude Code) featuring specialized agents and skills to support testing activities for a product at any stage of the SDLC. Free to use, fork, build, and contribute. Based on the Agentic QE Framework created by Dragan Spiridonov.

How do I install qe-code-intelligence?

Run the command: npx killer-skills add proffesor-for-testing/agentic-qe/qe-code-intelligence. It works with Cursor, Windsurf, VS Code, Claude Code, and 19+ other IDEs.

What are the use cases for qe-code-intelligence?

Key use cases include: Analyzing change impact on large codebases, Searching for code semantically with reduced context, Building knowledge graphs for complex projects, Mapping dependencies and identifying hotspots, Reducing context for AI operations with token reduction.

Which IDEs are compatible with qe-code-intelligence?

This skill is compatible with Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer. Use the Killer-Skills CLI for universal one-command installation.

Are there any limitations for qe-code-intelligence?

Requires TypeScript files and Agentic QE Framework. Limited to coding languages supported by the framework. Needs incremental indexing for optimal performance.

How To Install

  1. 1. Open your terminal

    Open the terminal or command line in your project directory.

  2. 2. Run the install command

    Run: npx killer-skills add proffesor-for-testing/agentic-qe/qe-code-intelligence. The CLI will automatically detect your IDE or AI agent and configure the skill.

  3. 3. Start using the skill

    The skill is now active. Your AI agent can use qe-code-intelligence immediately in the current project.

! Reference-Only Mode

This page remains useful for installation and reference, but Killer-Skills no longer treats it as a primary indexable landing page. Read the review above before relying on the upstream repository instructions.

Upstream Repository Material

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

Upstream Source

qe-code-intelligence

Install qe-code-intelligence, an AI agent skill for AI agent workflows and automation. Works with Claude Code, Cursor, and Windsurf with one-command setup.

SKILL.md
Readonly
Upstream Repository Material
The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.
Supporting Evidence

QE Code Intelligence

Purpose

Guide the use of v3's code intelligence capabilities including knowledge graph construction, semantic code search, dependency mapping, and context-aware code understanding with significant token reduction.

Activation

  • When understanding unfamiliar code
  • When searching for code semantically
  • When analyzing dependencies
  • When building code knowledge graphs
  • When reducing context for AI operations

Quick Start

bash
1# Index codebase into knowledge graph 2aqe code index src/ --incremental 3 4# Semantic code search 5aqe code search "authentication middleware" 6 7# Analyze change impact 8aqe code impact src/services/UserService.ts --depth 3 9 10# Map dependencies 11aqe code deps src/ 12 13# Analyze complexity and find hotspots 14aqe code complexity src/

Agent Workflow

typescript
1// Build knowledge graph 2Task("Index codebase", ` 3 Build knowledge graph for the project: 4 - Parse all TypeScript files in src/ 5 - Extract entities (classes, functions, types) 6 - Map relationships (imports, calls, inheritance) 7 - Generate embeddings for semantic search 8 Store in AgentDB vector database. 9`, "qe-kg-builder") 10 11// Semantic search 12Task("Find relevant code", ` 13 Search for code related to "user authentication flow": 14 - Use semantic similarity (not just keyword) 15 - Include related functions and types 16 - Rank by relevance score 17 - Return with minimal context (80% token reduction) 18`, "qe-code-intelligence")

Knowledge Graph Operations

1. Codebase Indexing

typescript
1await knowledgeGraph.index({ 2 source: 'src/**/*.ts', 3 extraction: { 4 entities: ['class', 'function', 'interface', 'type', 'variable'], 5 relationships: ['imports', 'calls', 'extends', 'implements', 'uses'], 6 metadata: ['jsdoc', 'complexity', 'lines'] 7 }, 8 embeddings: { 9 model: 'code-embedding', 10 dimensions: 384, 11 normalize: true 12 }, 13 incremental: true // Only index changed files 14});
typescript
1await semanticSearcher.search({ 2 query: 'payment processing with stripe', 3 options: { 4 similarity: 'cosine', 5 threshold: 0.7, 6 limit: 20, 7 includeContext: true 8 }, 9 filters: { 10 fileTypes: ['.ts', '.tsx'], 11 excludePaths: ['node_modules', 'dist'] 12 } 13});

3. Dependency Analysis

typescript
1await dependencyMapper.analyze({ 2 entry: 'src/services/OrderService.ts', 3 depth: 3, 4 direction: 'both', // imports and importedBy 5 output: { 6 graph: true, 7 metrics: { 8 afferentCoupling: true, 9 efferentCoupling: true, 10 instability: true 11 } 12 } 13});

Token Reduction Strategy

typescript
1// Get context with 80% token reduction 2const context = await codeIntelligence.getOptimizedContext({ 3 query: 'implement user registration', 4 budget: 4000, // max tokens 5 strategy: { 6 relevanceRanking: true, 7 summarization: true, 8 codeCompression: true, 9 deduplication: true 10 }, 11 include: { 12 signatures: true, 13 implementations: 'relevant-only', 14 comments: 'essential', 15 examples: 'top-3' 16 } 17});

Knowledge Graph Schema

typescript
1interface KnowledgeGraph { 2 entities: { 3 id: string; 4 type: 'class' | 'function' | 'interface' | 'type' | 'file'; 5 name: string; 6 file: string; 7 line: number; 8 embedding: number[]; 9 metadata: Record<string, any>; 10 }[]; 11 relationships: { 12 source: string; 13 target: string; 14 type: 'imports' | 'calls' | 'extends' | 'implements' | 'uses'; 15 weight: number; 16 }[]; 17 indexes: { 18 byName: Map<string, string[]>; 19 byFile: Map<string, string[]>; 20 byType: Map<string, string[]>; 21 }; 22}

Search Results

typescript
1interface SearchResult { 2 entity: { 3 name: string; 4 type: string; 5 file: string; 6 line: number; 7 }; 8 relevance: number; 9 snippet: string; 10 context: { 11 before: string[]; 12 after: string[]; 13 related: string[]; 14 }; 15 explanation: string; 16}

CLI Examples

bash
1# Full reindex 2aqe code index src/ 3 4# Incremental index (changed files only) 5aqe code index src/ --incremental 6 7# Index only files changed since a git ref 8aqe code index . --git-since HEAD~5 9 10# Semantic code search 11aqe code search "database connection" 12 13# Change impact analysis 14aqe code impact src/services/UserService.ts 15 16# Dependency mapping 17aqe code deps src/ --depth 5 18 19# Complexity metrics and hotspots 20aqe code complexity src/ --format json

Gotchas

  • WARNING: code-intelligence domain has 18% success rate — prefer direct grep/glob over agent-based code search for simple queries
  • Knowledge graph construction fails on repos >50K LOC — scope to specific modules
  • Semantic search returns irrelevant results without domain-specific embeddings — always verify search results manually
  • Agent claims "80% token reduction" but may skip critical context — verify key files are included in results
  • Fleet must be initialized before using: run aqe health to diagnose, or aqe init to re-initialize if you get initialization errors

Coordination

Primary Agents: qe-kg-builder, qe-dependency-mapper, qe-impact-analyzer, qe-code-complexity Coordinator: qe-code-intelligence Related Skills: qe-test-generation, qe-defect-intelligence

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