Comprehensive performance analysis, bottleneck detection, and optimization recommendations for Claude Flow swarms
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。
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Comprehensive performance analysis, bottleneck detection, and optimization recommendations for Claude Flow swarms
Tags: performance, bottleneck, optimization, profiling, metrics, analysis, qe, quality-engineering, monitoring
This skill requires the AQE MCP server. Ensure it is configured in .kiro/settings/mcp.json.
This skill consolidates all performance analysis capabilities: - Bottleneck Detection: Identify performance bottlenecks across communication, processing, memory, and network - Performance Profiling: Real-time monitoring and historical analysis of swarm operations - Report Generation: Cre
Basic Bottleneck Detection
Generate Performance Report
Analyze and Auto-Fix
Core Capabilities
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。
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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。