Use when running comprehensive WCAG accessibility audits with axe-core + pa11y + Lighthouse, generating context-aware remediation, or testing video accessibility. Supports 3-tier browser cascade with graceful degradation.
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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.
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实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
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.
# 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 .
// 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")
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
});
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']
}
});
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
}
}
});
// 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'
}
});
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[]>;
};
}
interface SearchResult {
entity: {
name: string;
type: string;
file: string;
line: number;
};
relevance: number;
snippet: string;
context: {
before: string[];
after: string[];
related: string[];
};
explanation: string;
}
# 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
aqe health to diagnose, or aqe init to re-initialize if you get initialization errorsPrimary 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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Use when running comprehensive WCAG accessibility audits with axe-core + pa11y + Lighthouse, generating context-aware remediation, or testing video accessibility. Supports 3-tier browser cascade with graceful degradation.
中文简介正在准备中。当前显示原文说明。
WCAG 2.2 compliance testing, screen reader validation, and inclusive design verification. Use when ensuring legal compliance (ADA, Section 508), testing for disabilities, or building accessible applications for 1 billion disabled users globally.
中文简介正在准备中。当前显示原文说明。
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
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Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
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Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
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Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
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