How do I install the Claroty Ot Topology skill?

Run npx skillmds@latest add gabrielmoreira/claroty-ot-topology in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Claroty Ot Topology safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: reads secrets. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Claroty Ot Topology?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Claroty Ot Topology free to use?

Yes. Installing skills from SkillMD is free. This skill is licensed under Apache-2.

Who published Claroty Ot Topology?

gabrielmoreira (@gabrielmoreira) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Claroty Ot Topology by gabrielmoreira

Name: Claroty Ot Topology
Rating: 4.2 (5 reviews)
Author: gabrielmoreira

File contents

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-viz skill for inline Canvas / A2UI topology rendering
drawio- skill for exportable .drawio / SVG diagrams
uml- 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-viz to 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 .drawio file.

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 or uml- 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.

---name: claroty-ot-topologydescription: 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--- # 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-viz` skill for inline Canvas / A2UI topology rendering - `drawio-` skill for exportable `.drawio` / SVG diagrams - `uml-` skill for nwdiag-style topology ## Workflow Examples ### Inline topology for a device ``` "Show me the communication map for device 7a2c... as an inline topology" ``` 1. `get_device_communication_map(device_id="7a2c...")` → edges. 2. `list_devices(...)` to resolve neighbour device IDs to names. 3. Hand-off to `canvas-network-viz` to 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" ``` 1. `list_devices(site_id="warehouse-east")` → nodes. 2. `get_device_communication_map(site_id="warehouse-east")` → edges. 3. Hand off to the draw.io skill to produce an exportable `.drawio` file. ### 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 or `uml-` 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.

Frequently asked questions

Claroty Ot Topology