Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination
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Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination
Tags: qe, quality-engineering
This skill requires the AQE MCP server. Ensure it is configured in .kiro/settings/mcp.json.
Installation
Basic Usage
Core Capabilities
Track operations, learn patterns, and get intelligent suggestions:
Automatically identify successful operation sequences:
Track improvement over time:
Fast integrity verification with quantum-resistant cryptography:
Automatic tracking of all operations:
Use AQE tools via the @agentic-qe MCP server:
@agentic-qe/fleet_init — Initialize the QE fleet@agentic-qe/test_generate_enhanced — Generate tests@agentic-qe/coverage_analyze_sublinear — Analyze coverage@agentic-qe/quality_assess — Assess quality gates@agentic-qe/memory_store — Store learned patterns@agentic-qe/memory_query — Query past patterns还没有评价。欢迎分享你的使用感受。
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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.
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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.
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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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