菜单
Render Claroty xDome OT / IoT communication maps and zone segmentation as inline Canvas/A2UI topology, draw.io diagrams, and timeline summaries.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Federate your NetClaw with other NetClaw operators over the BGP mesh — exchange capability inventories and ask your claw what a peer can do. (US1; remote invocation and chat land in later phases.)
Catalyst Center device inventory and site management - list/filter devices by hostname, IP, platform, family, role, reachability; view site hierarchy; get interface details per device; device reachability monitoring; cross-reference with pyATS. Use when listing network devices, checking device reachability, auditing software versions, viewing site hierarchy, or finding a device by serial number.
Catalyst Center troubleshooting workflows - device unreachable investigation, client connectivity issues, interface down analysis, site-wide outage triage, wireless roaming problems, integration with pyATS for CLI-level diagnostics. Use when a device is unreachable, a user reports connectivity problems, an interface is down, a site has an outage, or wireless clients have roaming issues.
Human-in-the-loop escalation via HumanRail — route low-confidence agent decisions, pre-destructive operation approvals, and ambiguous incident tickets to real human engineers. Human answers are verified and returned as structured output. Workers are paid via Lightning Network. Use when the agent is uncertain, when a destructive change needs explicit human sign-off beyond a ServiceNow CR, or when an ambiguous ticket requires human triage before automated handling.
Generate and manage Visio network diagrams on SharePoint via Microsoft Graph API - create topology diagrams from CDP/LLDP discovery, update existing diagrams, export to PDF. Use when creating Visio topology diagrams, uploading network diagrams to SharePoint, or generating physical/logical topology views from discovery data
Reconcile NetBox source of truth against live network state - detect IP drift, missing interfaces, undocumented links, cable mismatches, VLAN mismatches, and ticket discrepancies in ServiceNow. Use when validating NetBox accuracy, checking for config drift, auditing network documentation, or reconciling source of truth after changes
| 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 |
| metadata | {"openclaw":{"requires":{"bins":["python3"],"env":["CLAROTY_API_URL","CLAROTY_API_TOKEN"]}}} |
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.
claroty-mcppython3 -u mcp-servers/claroty-mcp/claroty_mcp_server.py (stdio transport)CLAROTY_API_TOKEN| 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 renderingdrawio- skill for exportable .drawio / SVG diagramsuml- skill for nwdiag-style topology"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.canvas-network-viz to render the topology in chat with health-coloured nodes."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.
"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..drawio file."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.
This skill is intentionally a data + composition skill — it does not own the rendering layer. For visual output:
canvas-network-viz (topology map A2UI primitive)drawio- skill or uml- skill (nwdiag)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.