Agentic-qe qe-code-intelligence
Builds semantic code indexes, maps dependency graphs, and performs intelligent code search across large codebases. Use when understanding unfamiliar code, tracing call chains, analyzing import dependencies, or reducing context window usage through targeted retrieval.
install
source · Clone the upstream repo
git clone https://github.com/proffesor-for-testing/agentic-qe
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/proffesor-for-testing/agentic-qe "$T" && mkdir -p ~/.claude/skills && cp -r "$T/.claude/skills/qe-code-intelligence" ~/.claude/skills/proffesor-for-testing-agentic-qe-qe-code-intelligence && rm -rf "$T"
manifest:
.claude/skills/qe-code-intelligence/SKILL.mdsource content
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
# Index codebase into knowledge graph aqe code index src/ --incremental # Semantic code search aqe code search "authentication middleware" # Analyze change impact aqe code impact src/services/UserService.ts --depth 3 # Map dependencies aqe code deps src/ # Analyze complexity and find hotspots aqe code complexity src/
Agent Workflow
// Build knowledge graph Task("Index codebase", ` Build knowledge graph for the project: - Parse all TypeScript files in src/ - Extract entities (classes, functions, types) - Map relationships (imports, calls, inheritance) - Generate embeddings for semantic search Store in AgentDB vector database. `, "qe-kg-builder") // Semantic search Task("Find relevant code", ` Search for code related to "user authentication flow": - Use semantic similarity (not just keyword) - Include related functions and types - Rank by relevance score - Return with minimal context (80% token reduction) `, "qe-code-intelligence")
Knowledge Graph Operations
1. Codebase Indexing
await knowledgeGraph.index({ source: 'src/**/*.ts', extraction: { entities: ['class', 'function', 'interface', 'type', 'variable'], relationships: ['imports', 'calls', 'extends', 'implements', 'uses'], metadata: ['jsdoc', 'complexity', 'lines'] }, embeddings: { model: 'code-embedding', dimensions: 384, normalize: true }, incremental: true // Only index changed files });
2. Semantic Search
await semanticSearcher.search({ query: 'payment processing with stripe', options: { similarity: 'cosine', threshold: 0.7, limit: 20, includeContext: true }, filters: { fileTypes: ['.ts', '.tsx'], excludePaths: ['node_modules', 'dist'] } });
3. Dependency Analysis
await dependencyMapper.analyze({ entry: 'src/services/OrderService.ts', depth: 3, direction: 'both', // imports and importedBy output: { graph: true, metrics: { afferentCoupling: true, efferentCoupling: true, instability: true } } });
Token Reduction Strategy
// Get context with 80% token reduction const context = await codeIntelligence.getOptimizedContext({ query: 'implement user registration', budget: 4000, // max tokens strategy: { relevanceRanking: true, summarization: true, codeCompression: true, deduplication: true }, include: { signatures: true, implementations: 'relevant-only', comments: 'essential', examples: 'top-3' } });
Knowledge Graph Schema
interface KnowledgeGraph { entities: { id: string; type: 'class' | 'function' | 'interface' | 'type' | 'file'; name: string; file: string; line: number; embedding: number[]; metadata: Record<string, any>; }[]; relationships: { source: string; target: string; type: 'imports' | 'calls' | 'extends' | 'implements' | 'uses'; weight: number; }[]; indexes: { byName: Map<string, string[]>; byFile: Map<string, string[]>; byType: Map<string, string[]>; }; }
Search Results
interface SearchResult { entity: { name: string; type: string; file: string; line: number; }; relevance: number; snippet: string; context: { before: string[]; after: string[]; related: string[]; }; explanation: string; }
CLI Examples
# Full reindex aqe code index src/ # Incremental index (changed files only) aqe code index src/ --incremental # Index only files changed since a git ref aqe code index . --git-since HEAD~5 # Semantic code search aqe code search "database connection" # Change impact analysis aqe code impact src/services/UserService.ts # Dependency mapping aqe code deps src/ --depth 5 # Complexity metrics and hotspots aqe 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
to diagnose, oraqe health
to re-initialize if you get initialization errorsaqe init
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