By ruvnet
Orchestrate swarms of AI agents for complex multi-step tasks using SPARC methodology, swarm coordination, and GitHub automation, with WASM-accelerated local execution and a cloud-based orchestration platform providing 70+ tools.
Own this plugin?
Verify ownership to unlock analytics, metadata editing, and a verified badge. GitHub access is read-only (username + org membership).
Sign in to claimOwn this plugin?
Verify ownership to unlock analytics, metadata editing, and a verified badge. GitHub access is read-only (username + org membership).
Sign in to claimBased on adoption, maintenance, documentation, and repository signals. Not a security audit or endorsement.
npx claudepluginhub ruvnet/ruflo --plugin ruflo-arenaEnd-to-end RuView (WiFi-DensePose) toolkit for Claude Code: onboarding, ESP32 hardware setup, configuration, sensing applications, model training, advanced multistatic sensing, and witness verification — from practical to advanced.
Foundation plugin for AgentDB — pattern store, search, stats. Required by other agentdb-* plugins.
Episodic replay (Reflexion) + skill library for AgentDB. Adds /remember, /recall, and a curator agent for nightly consolidation.
Graph operations on AgentDB — Cypher execution, k-hop traversal, hyperedge search.
RL routing + Thompson Sampling bandit for AgentDB. 9 algorithms (Q-Learning, SARSA, DQN, PPO, Actor-Critic, Policy Gradient, Decision Transformer, MCTS, Model-Based RL); /learn-task, /route-task.
Multi-agent coordination with agent-swarm MCP
Task distribution, agent coordination, progress monitoring - executes plans via subagents. Requires AI Maestro for inter-agent messaging.
Multi-agent orchestration with AI SDK v5 - handoffs, routing, and coordination for any AI provider (OpenAI, Anthropic, Google)
Intelligent orchestration platform for AI coding tools — routes tasks to the best model, learns from outcomes, and enforces quality through multi-model consensus. 46 MCP tools for agent management, research, memory, consensus voting, codebase intelligence, and a full dev pipeline.
This skill should be used when the model's ROLE_TYPE is orchestrator and needs to delegate tasks to specialist sub-agents. Provides scientific delegation framework ensuring world-building context (WHERE, WHAT, WHY) while preserving agent autonomy in implementation decisions (HOW). Use when planning task delegation, structuring sub-agent prompts, or coordinating multi-agent workflows.
Repowire mesh usage skills for AI coding agents: cross-agent review and planning, delegate, usage patterns, and install/update. Backend-agnostic and parameterised on the agent you choose.