qe-code-intelligence

name	qe-code-intelligence
description	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.
trust_tier	3
validation	{"schema_path":"schemas/output.json","validator_path":"scripts/validate-config.json","eval_path":"evals/qe-code-intelligence.yaml"}

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/

# Generate C4 architecture diagrams (Mermaid) with a confidence score
aqe code c4 .

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 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