SuperAPI AI Directory

canvas-network-viz

Deployment & operations

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Updated 10-15 17:21

About this skill

Canvas Network Visualization

Overview

This skill renders inline network visualizations in the OpenClaw chat interface using the Canvas/A2UI framework. It does NOT establish direct device connections or create new MCP servers. All data is sourced from existing MCP tools.

Visualization Types

Type Component Data Sources Trigger
Topology Map topology-map pyATS CDP/LLDP, health metrics "show network topology"
Dashboard Panel dashboard-panel pyATS, Grafana, Prometheus "show health dashboard for {device}"
Alert Card alert-card Grafana alerts, Prometheus alertmanager "show current alerts"
Change Timeline change-timeline ServiceNow "show change request {CR} status"
Diff View diff-view pyATS config/route/ACL "show config diff for {device}"
Path Trace path-trace pyATS, SuzieQ "trace path from {src} to {dst}"
Health Scorecard health-scorecard pyATS, Grafana, Prometheus "show health scorecard for {site}"

How It Works

  1. Operator requests a visualization via natural language
  2. Skill identifies visualization type from request pattern
  3. Data is fetched from relevant MCP servers via existing tools
  4. Data is transformed into the visualization-specific content model
  5. A2UI JSON envelope is built with component type, props, and content
  6. Canvas framework renders the visualization inline in chat
  7. GAIT audit log entry is emitted for the visualization event

Output Format

All visualizations produce A2UI JSON:

{
  "a2ui": {
    "version": "1.0",
    "type": "canvas",
    "component": "<visualization-type>",
    "props": {
      "id": "<uuid>",
      "title": "<title>",
      "timestamp": "<ISO 8601>",
      "dataSources": [...],
      "scope": {...},
      "warnings": [...],
      "content": {...}
    }
  }
}

Graceful Degradation

  • If a data source is unavailable, partial data is rendered with warnings
  • If no data is available at all, an error-card is returned with guidance
  • Health status defaults to "unknown" (gray) when metrics are missing
  • Large topologies (200+ nodes) are automatically clustered by site

Integration with Other Skills

Skill Integration
gait-session-tracking All visualization events are GAIT-logged
pyats-topology CDP/LLDP discovery data for topology maps
pyats-health-check Health metrics for node coloring and dashboards
grafana-observability Prometheus metrics, alerts, dashboard data
prometheus-monitoring Direct PromQL queries for metrics
servicenow-change-workflow Change request lifecycle data

Important Rules

  • Read-only: This skill never modifies network devices or MCP server state
  • No new credentials: Uses existing MCP server connections only
  • Complements Three.js HUD: Does not replace ui/netclaw-visual/
  • GAIT mandatory: Every visualization generation is audit-logged
  • WCAG 2.1 AA: Color scales meet accessibility contrast requirements