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Claroty Ot Topology

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automateyournetwork
claroty-ot-topology

Render Claroty xDome OT / IoT communication maps and zone segmentation as inline Canvas/A2UI topology, draw.io diagrams, and timeline summaries.

Overview

Publisherautomateyournetwork
Repositorynetclaw
Skill nameclaroty-ot-topology
Stars
680
Forks
189
Bundled files
Instructions only
LicenseApache-2.0
Links
Skill sourceRepository
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by automateyournetwork on GitHub. Read the source before you install it.

Installation

Install the Claroty Ot Topology AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/automateyournetwork/netclaw/tree/main/workspace/skills/claroty-ot-topology
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/automateyournetwork/netclaw.git /tmp/netclaw
mkdir -p .claude/skills
cp -r /tmp/netclaw/workspace/skills/claroty-ot-topology .claude/skills/claroty-ot-topology
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Claroty Ot Topology in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Claroty Ot Topology on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Claroty Ot Topology is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

ToolParametersWhat It Does
get_device_communication_mapdevice_id?, site_id?, limit?, offset?Device-to-device edges (src, dst, protocol, port, byte counts)
list_organization_zoneslimit?, offset?Network segmentation zones (id, name, device count)
list_ot_activity_eventsdevice_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.

Failure Behavior

  • On a tool error (timeout, unreachable host, malformed response), report the failure and its error message directly to the user rather than fabricating or guessing at results.
  • For a confirmed read-only call, check connectivity and retry once if appropriate. For any call that changes state or sends a message, a timeout does not prove the action failed: inspect current state or delivery status before retrying, preserve the required approval/change gates, and do not repeat an action whose outcome is unknown.

Frequently asked questions

What does the Claroty Ot Topology AI skill do?

Render Claroty xDome OT / IoT communication maps and zone segmentation as inline Canvas/A2UI topology, draw.io diagrams, and timeline summaries.

Why use Claroty Ot Topology on TypingMind?

Because you install it once and use it with any model. Claroty Ot Topology is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Claroty Ot Topology in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/automateyournetwork/netclaw/tree/main/workspace/skills/claroty-ot-topology. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Claroty Ot Topology?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Claroty Ot Topology?

As many as you like. As long as a model supports skills, you can use Claroty Ot Topology with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Claroty Ot Topology AI skill free?

Yes. It is published on GitHub by automateyournetwork under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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