Claude Code Plugin

# Add the Station marketplace
/plugin marketplace add cloudshipai/station

# Choose ONE of these plugins:
/plugin install station@cloudshipai-station        # Skills-based (lightweight)
/plugin install station-agent@cloudshipai-station  # Subagent-based (full integration)

# Skills-based plugin
/plugin install cloudshipai/station/claude-code-plugin

# Subagent-based plugin
/plugin install cloudshipai/station/claude-code-plugin-agent

# Skills-based plugin
/plugin install ./station/claude-code-plugin

# Subagent-based plugin
/plugin install ./station/claude-code-plugin-agent

{
  "mcpServers": {
    "station": {
      "command": "stn",
      "args": ["stdio"],
      "env": {
        "OTEL_EXPORTER_OTLP_ENDPOINT": "http://localhost:4318"
      }
    }
  }
}

# Verify installation
stn --version

# Initialize Station
stn init

# Create a simple agent
stn agent create --name "Hello Agent" --model gpt-4o-mini

# Load the core Station skill before working with agents
/skill station

# Now ask questions or give commands
Create an agent that monitors Kubernetes pods and alerts on failures

User: Create a Station agent that monitors Kubernetes pods

Claude: [Invokes station-operator subagent]
[Subagent creates agent using create_agent tool]
[Subagent returns result with agent details]

Claude: I've created your Kubernetes pod monitor agent...

Use the station-operator subagent to debug my last failed agent run

# Start Jaeger for distributed tracing
stn jaeger up

# View traces at http://localhost:16686

# Get Station overview
/station

# Create an agent
/station-agent

# Build a workflow
/station-workflow

# Package agents
/station-bundle

Create a Station agent that monitors Kubernetes pod health 
and alerts on failures. Use gpt-4o-mini model.

Run my "Pod Monitor" agent with the task "Check all pods in production namespace"

Create an incident response team with:
1. An "Incident Coordinator" that delegates to specialists
2. A "Kubernetes Expert" for K8s troubleshooting  
3. A "Log Analyzer" for log pattern analysis

Set up the coordinator to call the specialists as tools.

Create a workflow that:
1. Triages incoming alerts by severity
2. Investigates high-severity issues automatically
3. Requires human approval before remediation
4. Documents the resolution

---
metadata:
  name: "My Agent"
  description: "What this agent does"
model: gpt-4o-mini
max_steps: 8
tools:
  - "__tool_name"
---
{{role "system"}}
You are an expert assistant that...

{{role "user"}}
{{userInput}}

{
  "mcpServers": {
    "station": {
      "command": "stn",
      "args": ["stdio"]
    }
  }
}

{
  "mcpServers": {
    "station": {
      "command": "stn",
      "args": ["--config", "/path/to/config.yaml", "stdio"]
    }
  }
}

{
  "mcpServers": {
    "station": {
      "type": "http",
      "url": "http://localhost:8586/mcp"
    }
  }
}

{
  "mcpServers": {
    "station-dev": {
      "command": "stn",
      "args": ["--config", "~/.station/dev.yaml", "stdio"]
    },
    "station-prod": {
      "command": "stn",
      "args": ["--config", "~/.station/prod.yaml", "stdio"]
    }
  }
}

{
  "mcpServers": {
    "station": {
      "command": "stn",
      "args": ["stdio"],
      "env": {
        "OTEL_EXPORTER_OTLP_ENDPOINT": "http://localhost:4318"
      }
    }
  }
}

stn --version

echo '{"jsonrpc":"2.0","method":"tools/list","id":1}' | stn stdio

/plugin uninstall station
/plugin install cloudshipai/station

stn env list

stn provider status

stn mcp tools

stn jaeger up
# View traces at http://localhost:16686

# Good - Claude uses MCP tool with structured response
"Create an agent called 'Cost Analyzer' that..."

# Avoid - asking Claude to run CLI commands
"Run: stn agent create --name 'Cost Analyzer'..."

/station-workflow

Now create a workflow that handles deployment approvals...

{
  "mcpServers": {
    "station-dev": { "command": "stn", "args": ["--config", "~/.station/dev.yaml", "stdio"] },
    "station-prod": { "command": "stn", "args": ["--config", "~/.station/prod.yaml", "stdio"] }
  }
}