/daa-agent
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.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/daa-agent
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The summary Claude sees to decide when to auto-load this skill.
Create and adapt Dynamic Agentic Architecture agents that learn and evolve
SKILL.md
daa-agent.SKILL.mdname: 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
DAA Agent
Create agents with Dynamic Agentic Architecture that adapt and learn over time.
When to use
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.
Steps
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
DAA vs static 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 |
Read more
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
DAA Agent
Create agents with Dynamic Agentic Architecture that adapt and learn over time.
When to use
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.
Steps
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
DAA vs static 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. Agent = Model + Harness. The model writes; the harness gives it tools, memory, loops, sandboxes, and controls so it can actually work.
Repo: ruvnet/claude-flow
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