Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add automateyournetwork/netclaw --skill claroty-ot-topologygit clone --depth 1 https://github.com/automateyournetwork/netclawWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/automateyournetwork/netclaw/claroty-ot-topology)<a href="https://agentmods.dev/skills/automateyournetwork/netclaw/claroty-ot-topology"><img src="https://agentmods.dev/badge/skills/automateyournetwork/netclaw/claroty-ot-topology/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/automateyournetwork/netclaw/claroty-ot-topology"><img src="https://agentmods.dev/badge/skills/automateyournetwork/netclaw/claroty-ot-topology.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00038 | $0.00954 |
| Opus 5 | $0.00019 | $0.00477 |
| Sonnet 5 | $0.00008 | $0.00191 |
| Haiku 4.5 | $0.00004 | $0.00095 |
Grade A, and why
claroty-ot-topology scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 13d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 13d ago First seen · 94 lines · 38 tokens per session scan A a33b00106d91
claroty-ot-topology is a skill published in the GitHub repository automateyournetwork/netclaw (657 stars, last pushed 6d ago), licensed Apache-2.0. It adds 38 tokens to every session and 954 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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