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.
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See how to installReview the instructions the agent will receive before you install.
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.
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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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