Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination
日本語の概要は準備中です。原文の説明を表示しています。
実行スコア 未検証
インストール方法を見るインストールする前に、エージェントに与えられる指示の中身を確認できます。
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まだレビューはありません。使ってみた感想をお寄せください。
ログインなしで書き込めます。使ってみた感想、質問、動かなかった環境などを気軽にどうぞ。
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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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。