automateyournetwork/claroty-ot-topology
workspace/skills/claroty-ot-topology/SKILL.md
Render Claroty xDome OT / IoT communication maps and zone segmentation as inline Canvas/A2UI topology, draw.io diagrams, and timeline summaries.
$ install --global
skillsauthnpx skillsauth add automateyournetwork/netclaw claroty-ot-topologyInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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SKILL.md
- name:
- claroty-ot-topology
- description:
- Render Claroty xDome OT / IoT communication maps and zone segmentation as inline Canvas/A2UI topology, draw.io diagrams, and timeline summaries.
- license:
- Apache-2.0
- user-invocable:
- true
- bins:
- ["python3"]
- env:
- ["CLAROTY_API_URL", "CLAROTY_API_TOKEN"]
Claroty OT Topology
Visualise the OT / IoT communication fabric observed by Claroty xDome — device-to-device edges, organisation zones, and OT activity timelines — using Canvas / A2UI for inline chat rendering and draw.io for exportable diagrams.
When to Use
- Visualising how OT devices communicate (PLC ↔ HMI, RTU ↔ historian, etc.)
- Reviewing network segmentation zones and verifying Purdue-layer separation
- Producing an OT topology diagram for a CR or QBR deck
- Walking an activity timeline for a specific device during incident review
- Sanity-checking that newly deployed zones are reflected in observed traffic
MCP Server
- Server:
claroty-mcp - Command:
python3 -u mcp-servers/claroty-mcp/claroty_mcp_server.py(stdio transport) - Auth: Bearer token via
CLAROTY_API_TOKEN - ITSM: This is a read-only skill — no ITSM gate required.
Available Tools
| Tool | Parameters | What It Does |
|------|------------|--------------|
| get_device_communication_map | device_id?, site_id?, limit?, offset? | Device-to-device edges (src, dst, protocol, port, byte counts) |
| list_organization_zones | limit?, offset? | Network segmentation zones (id, name, device count) |
| list_ot_activity_events | device_id?, site_id?, event_type?, start?, end?, limit?, offset?, max_items? | OT activity / protocol observations |
| list_devices | (see claroty-asset-inventory) | Resolve device IDs ↔ human-friendly names for diagram labels |
Compose with:
canvas-network-vizskill for inline Canvas / A2UI topology renderingdrawio-skill for exportable.drawio/ SVG diagramsuml-skill for nwdiag-style topology
Workflow Examples
Inline topology for a device
"Show me the communication map for device 7a2c... as an inline topology"
get_device_communication_map(device_id="7a2c...")→ edges.list_devices(...)to resolve neighbour device IDs to names.- Hand-off to
canvas-network-vizto render the topology in chat with health-coloured nodes.
Zone segmentation audit
"List all xDome zones and show their device counts"
Calls list_organization_zones(). The agent then optionally calls list_devices per zone to surface devices that should be in a stricter zone but aren't.
Site-wide topology export
"Render a draw.io diagram of the OT topology at site warehouse-east"
list_devices(site_id="warehouse-east")→ nodes.get_device_communication_map(site_id="warehouse-east")→ edges.- Hand off to the draw.io skill to produce an exportable
.drawiofile.
OT activity timeline during incident review
"Show me OT activity events for device 7a2c between 09:00 and 11:00 today"
Calls list_ot_activity_events(device_id="7a2c...", start="2026-06-08T09:00:00Z", end="2026-06-08T11:00:00Z"). Optionally renders as a Canvas A2UI timeline.
Rendering hand-offs
This skill is intentionally a data + composition skill — it does not own the rendering layer. For visual output:
- Inline chat:
canvas-network-viz(topology map A2UI primitive) - Exportable diagram:
drawio-skill oruml-skill (nwdiag) - Timeline:
canvas-network-viz(timeline A2UI primitive)
This keeps Principle VII (Skill Modularity) intact — Claroty-specific knowledge stays here, generic rendering lives in the visualisation skills.
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