multi-agent-analysislisted
Install: claude install-skill aiskillstore/marketplace
# Multi-Agent Analysis
Analyzes coordination patterns in multi-agent systems.
## Process
1. **Identify coordination model** — Supervisor, peer-to-peer, pipeline
2. **Document handoffs** — How control transfers between agents
3. **Classify state sharing** — Blackboard vs message passing
4. **Trace communication** — Protocol and data flow
## Coordination Models
### Supervisor (Hierarchical)
```
┌─────────────┐
│ Supervisor │
│ (Router) │
└──────┬──────┘
│
┌──────────┼──────────┐
│ │ │
▼ ▼ ▼
┌───────┐ ┌───────┐ ┌───────┐
│Worker1│ │Worker2│ │Worker3│
│(Search)│ │(Code) │ │(Write)│
└───────┘ └───────┘ └───────┘
```
```python
class Supervisor:
def route(self, task: str) -> Agent:
"""Decide which worker handles the task"""
if "search" in task:
return self.search_agent
elif "code" in task:
return self.code_agent
else:
return self.general_agent
def run(self, input: str):
while not self.is_done():
agent = self.route(self.current_task)
result = agent.run(self.current_task)
self.update_state(result)
```
**Characteristics**:
- Central control point
- Clear routing logic
- Single point of failure
- Easy to understand
### Peer-to-Peer
```
┌───────┐ ┌───────┐
│Agent A│◄───►│Agent B│
└───┬───┘ └───┬───┘
│ │
│ ┌───────┐ │