Add optional Ruflo development-time orchestration to an Agentic QE workflow. Use when a task benefits from coordinated swarms, persistent cross-session memory, routing, or Ruflo hooks. Do not use for a one-shot edit, and never add Ruflo to shipped AQE runtime dependencies.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
실행 점수 미검증
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Keep the ownership boundary explicit:
ruflo, claude-flow, or
@claude-flow/cli as an AQE runtime dependency.Before using Ruflo, confirm that multi-agent coordination is worth its overhead. Prefer a small, risk-selected QE fleet over activating every agent.
For projects that explicitly want Ruflo, initialize or inspect it through its external CLI:
npx ruflo doctor
npx ruflo discover-plugins
Use the installed Ruflo MCP tools when available. Treat their absence as graceful degradation: AQE quality workflows must continue to work without Ruflo installed.
아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.
아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.