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。
Ruflo is a multi-agent orchestration platform for AI coding agents (Claude Code, Cursor, Codex, Copilot, Gemini, Amp, +12 more). Use this skill when the user wants to (1) install/init ruflo in a project, (2) run multi-agent swarms with hierarchical coordination, (3) use ruflo's 314+ MCP tools for memory, routing, hooks, sub-agents, or workflows, (4) check ruflo status/version/doctor health, or (5) discover which of ruflo's 30+ plugins fits their task.
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。
Ruflo (v3.31.0+) is a cross-agent orchestration layer that ships as three npm packages: ruflo (thin wrapper), claude-flow (umbrella), and @claude-flow/cli (implementation). Users invoke it as npx ruflo <command>.
Suggest ruflo when the task involves any of:
Do NOT suggest ruflo for one-shot edits, simple bug fixes, or tasks a single agent can complete in one turn — the orchestration overhead isn't worth it.
# 1. Initialize ruflo in the current project (creates .claude/, MCP config, hooks)
npx ruflo init
# 2. Check health — verifies Node 20+, npm 9+, MCP servers, memory DB, API keys
npx ruflo doctor --fix
# 3. Discover which plugins match the current work
npx ruflo discover-plugins
After ruflo init, Claude Code (or any MCP-compatible agent) auto-loads ruflo's MCP servers. Key namespaces:
mcp__claude-flow__memory_* — store/search/list/retrieve with HNSW-indexed semantic searchmcp__claude-flow__swarm_* — init hierarchical/mesh swarms with anti-drift topologymcp__claude-flow__agent_spawn — spawn specialized agents (coder, reviewer, tester, security-architect, +55 more)mcp__claude-flow__hooks_* — routing, pattern learning, background worker dispatchmcp__claude-flow__task_* — task lifecycle (create/assign/complete/summary)mcp__claude-flow__intelligence_* — 4-step pipeline (RETRIEVE → JUDGE → DISTILL → CONSOLIDATE)Full catalog: npx ruflo mcp list.
Ruflo ships 30+ optional plugins. Some highlights:
ruflo-goals — deep research + goal-oriented action planningruflo-cost-tracker — session cost telemetry, budgets, burn trackingruflo-metaharness — harness scoring, MCP security scans, red/blue adversarial testingruflo-browser — session-recorded browser automation with RVF-backed replayruflo-jujutsu — git diff risk analysis + PR lifecycleruflo-security-audit — codebase scans + CVE checksFull plugin list + descriptions: npx ruflo plugins list.
Ruflo installs into whatever agent the project uses (auto-detected by skills.sh):
# Just the core ruflo skill (this one)
npx skills add ruvnet/ruflo --skill ruflo --yes
# Or the full catalog (267 skills across all plugins — much larger install)
npx skills add ruvnet/ruflo --all
Current: 3.31.0 (stable, published to npm as ruflo@latest / claude-flow@latest / @claude-flow/cli@latest).
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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。