agent-user-tools
Agent skill for user-tools - invoke with $agent-user-tools
Agent skill for user-tools - invoke with $agent-user-tools
Agent skill for v3-integration-architect - invoke with $agent-v3-integration-architect
Agent skill for v3-memory-specialist - invoke with $agent-v3-memory-specialist
Agent skill for v3-performance-engineer - invoke with $agent-v3-performance-engineer
Agent skill for v3-queen-coordinator - invoke with $agent-v3-queen-coordinator
Agent skill for v3-security-architect - invoke with $agent-v3-security-architect
Agent skill for worker-specialist - invoke with $agent-worker-specialist
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.
Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.
Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination
Advanced Hive Mind collective intelligence system for queen-led multi-agent coordination with consensus mechanisms and persistent memory
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.
Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.
Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination
Multi-repository coordination, synchronization, and architecture management with AI swarm orchestration
Advanced Hive Mind collective intelligence system for queen-led multi-agent coordination with consensus mechanisms and persistent memory
AgentDB memory system with HNSW vector search. Use when: need to store patterns, search for solutions, semantic lookup. Skip when: no learning needed, ephemeral tasks.
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.
Train and deploy neural networks in distributed E2B sandboxes with Flow Nexus
Neural pattern training with SONA (Self-Optimizing Neural Architecture), MoE (Mixture of Experts), and EWC++ for knowledge consolidation. Use when: pattern learning, model optimization, knowledge transfer, adaptive routing. Skip when: simple tasks, no learning required, one-off operations.
Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.
Implement adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement. Use when building self-learning agents, optimizing workflows, or implementing meta-cognitive systems.