agentdb-advanced
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems…
Create and adapt Dynamic Agentic Architecture agents that learn and evolve
$ npx -y skills add ruvnet/claude-flow --skill daa-agent --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/daa-agentContext preview
The summary Claude sees to decide when to auto-load this skill.
Create and adapt Dynamic Agentic Architecture agents that learn and evolve
name: daa-agent description: Create and adapt Dynamic Agentic Architecture agents that learn and evolve argument-hint: "<create|adapt|status> [options]" allowed-tools: mcp__plugin_ruflo-core_ruflo__daa_agent_create mcp__plugin_ruflo-core_ruflo__daa_agent_adapt mcp__plugin_ruflo-core_ruflo__daa_learning_status mcp__plugin_ruflo-core_ruflo__daa_performance_metrics mcp__plugin_ruflo-core_ruflo__daa_knowledge_share Bash
Create agents with Dynamic Agentic Architecture that adapt and learn over time.
When you need agents that go beyond static configurations — agents that adapt their behavior based on performance metrics, learn from interactions, and share knowledge with other agents.
1. **Create agent** — call `mcp__plugin_ruflo-core_ruflo__daa_agent_create` with initial configuration and learning parameters 2. **Monitor learning** — call `mcp__plugin_ruflo-core_ruflo__daa_learning_status` to see adaptation progress 3. **Check performance** — call `mcp__plugin_ruflo-core_ruflo__daa_performance_metrics` for efficiency and accuracy metrics 4. **Adapt** — call `mcp__plugin_ruflo-core_ruflo__daa_agent_adapt` to trigger manual adaptation based on feedback 5. **Share knowledge** — call `mcp__plugin_ruflo-core_ruflo__daa_knowledge_share` to propagate learnings to other agents
| Aspect | Static Agent | DAA Agent | |--------|-------------|-----------| | Behavior | Fixed configuration | Adapts over time | | Learning | None | Continuous from interactions | | Knowledge | Isolated | Shared across agents | | Performance | Constant | Improves with use |
An agent meta-harness for Claude Code and Codex. 📖 RuFlo Explained — Build an AI Team That Plans, Remembers, Tests, and Improves A 14-chapter guide: from the basic idea to a first useful task, then memory, agent teams, plugins, cost and verification.
Repo: ruvnet/claude-flow
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems…
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and…
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use…
Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing…
Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG…
Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination