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Network topology discovery via CDP/LLDP neighbors, ARP tables, routing peers, and interface mapping to build complete network maps.
Install Pyats Topology
$ npx skills add automateyournetwork/netclaw --skill pyats-topology -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install automateyournetwork/netclaw pyats-topology --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/automateyournetwork/netclaw.git skills-src && mkdir -p .claude/skills && cp -r skills-src/workspace/skills/pyats-topology .claude/skills/pyats-topology && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "pyats-topology" agent skill from https://github.com/automateyournetwork/netclaw/tree/main/workspace/skills/pyats-topology into .claude/skills/pyats-topology/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pyats-topology", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/automateyournetwork/netclaw/tree/main/workspace/skills/pyats-topologyType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add automateyournetwork/netclaw --skill pyats-topology -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install automateyournetwork/netclaw pyats-topology --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/automateyournetwork/netclaw.git skills-src && mkdir -p .agents/skills && cp -r skills-src/workspace/skills/pyats-topology .agents/skills/pyats-topology && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "pyats-topology" agent skill from https://github.com/automateyournetwork/netclaw/tree/main/workspace/skills/pyats-topology into .agents/skills/pyats-topology/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pyats-topology", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add automateyournetwork/netclaw --skill pyats-topology -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install automateyournetwork/netclaw pyats-topology --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/automateyournetwork/netclaw.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/workspace/skills/pyats-topology .cursor/skills/pyats-topology && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "pyats-topology" agent skill from https://github.com/automateyournetwork/netclaw/tree/main/workspace/skills/pyats-topology into .cursor/skills/pyats-topology/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pyats-topology", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/automateyournetwork/netclaw.git --path workspace/skills/pyats-topology--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add automateyournetwork/netclaw --skill pyats-topology -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install automateyournetwork/netclaw pyats-topology --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/automateyournetwork/netclaw.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/workspace/skills/pyats-topology .gemini/skills/pyats-topology && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "pyats-topology" agent skill from https://github.com/automateyournetwork/netclaw/tree/main/workspace/skills/pyats-topology into .gemini/skills/pyats-topology/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pyats-topology", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install automateyournetwork/netclaw pyats-topologyInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add automateyournetwork/netclaw --skill pyats-topology -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/automateyournetwork/netclaw.git skills-src && mkdir -p .github/skills && cp -r skills-src/workspace/skills/pyats-topology .github/skills/pyats-topology && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "pyats-topology" agent skill from https://github.com/automateyournetwork/netclaw/tree/main/workspace/skills/pyats-topology into .github/skills/pyats-topology/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pyats-topology", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add automateyournetwork/netclaw --skill pyats-topology -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install automateyournetwork/netclaw pyats-topology --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/automateyournetwork/netclaw.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/workspace/skills/pyats-topology .opencode/skills/pyats-topology && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "pyats-topology" agent skill from https://github.com/automateyournetwork/netclaw/tree/main/workspace/skills/pyats-topology into .opencode/skills/pyats-topology/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pyats-topology", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
pyats-topology- GitHub stars
- 676
- Token cost
- ~2.1k tokens
- SKILL.md length
- 560 words
- Files
- 1
- Skills in repo
- 120
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Network topology discovery via CDP/LLDP neighbors, ARP tables, routing peers, and interface mapping to build complete network maps.
- Works in 7 steps: CDP Neighbors (Cisco-to-Cisco) → LLDP Neighbors (Multi-Vendor) → ARP Table (L3 Neighbor Discovery) → …
- Mapping the network
- SKILL.md covers When to Use, Discovery Procedure, Building the Topology Model and Integration with Diagram Tools, plus 3 more sections
- Calls python3
What it does
Pyats Topology is an agent skill from automateyournetwork/netclaw. Network topology discovery via CDP/LLDP neighbors, ARP tables, routing peers, and interface mapping to build complete network maps. Use when mapping the network, building a diagram, discovering what is connected to what, or documenting device neighbors and links.
Its SKILL.md is about 2.1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Business, Finance & HR, covering Diagrams. It works with draw.io. The repository describes itself as: An AI agent that claws through your network. The licence is Apache-2.0.
When your agent uses it
- Mapping the network
- Building a diagram
- Discovering what is connected to what
- Documenting device neighbors and links
Example prompts
- “/pyats-topology”
Requirements
- Python 3
Workflow steps
7 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit aa90e7d. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
python3
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Pyats Topology loads about 2.1k tokens when it runs. Until then it costs about 70 tokens; SKILL.md has 560 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from automateyournetwork/netclaw at commit aa90e7d, republished under its Apache-2.0 licence (© automateyournetwork). 560 words, ~2,118 tokens.
Download SKILL.mdSave it as
.claude/skills/pyats-topology/SKILL.md (or your agent's skills folder).- name
- pyats-topology
- description
- Network topology discovery via CDP/LLDP neighbors, ARP tables, routing peers, and interface mapping to build complete network maps. Use when mapping the network, building a diagram, discovering what is connected to what, or documenting device neighbors and links.
- license
- Apache-2.0
- user-invocable
- true
Topology Discovery
When to Use
- Building network diagrams from scratch (no documentation exists)
- Validating existing documentation matches reality
- Pre-change topology baseline
- Incident response — understanding blast radius
- New device onboarding — mapping where it connects
Discovery Procedure
Step 1: CDP Neighbors (Cisco-to-Cisco)
PYATS_TESTBED_PATH=$PYATS_TESTBED_PATH python3 $MCP_CALL "${PYATS_PYTHON:-python3} -u $PYATS_MCP_SCRIPT" pyats_run_show_command '{"device_name":"R1","command":"show cdp neighbors detail"}'Extract per neighbor:
- Device ID (hostname)
- Platform and model
- IP address (management address)
- Local interface → Remote interface (link mapping)
- Software version
- Native VLAN (on switch links)
- Duplex
Build adjacency table:
Local Device | Local Interface | Remote Device | Remote Interface | Remote Platform
R1 | Gi0/0/0 | SW1 | Gi1/0/1 | WS-C3850-24T
R1 | Gi0/0/1 | R2 | Gi0/0/0 | ISR4431Step 2: LLDP Neighbors (Multi-Vendor)
PYATS_TESTBED_PATH=$PYATS_TESTBED_PATH python3 $MCP_CALL "${PYATS_PYTHON:-python3} -u $PYATS_MCP_SCRIPT" pyats_run_show_command '{"device_name":"R1","command":"show lldp neighbors detail"}'LLDP is IEEE 802.1AB — works with non-Cisco devices (Arista, Juniper, Linux hosts, IP phones, APs). Same adjacency table format as CDP but may include additional TLVs.
Step 3: ARP Table (L3 Neighbor Discovery)
PYATS_TESTBED_PATH=$PYATS_TESTBED_PATH python3 $MCP_CALL "${PYATS_PYTHON:-python3} -u $PYATS_MCP_SCRIPT" pyats_run_show_command '{"device_name":"R1","command":"show arp"}'Analysis:
- Map IP addresses to MAC addresses on each interface
- Identify directly connected hosts (servers, endpoints, other routers)
- Look for multiple MAC addresses on the same interface (switch segment)
- Incomplete entries indicate devices that are configured but unreachable
Step 4: Routing Protocol Peers
OSPF neighbors = L3 adjacent routers:
PYATS_TESTBED_PATH=$PYATS_TESTBED_PATH python3 $MCP_CALL "${PYATS_PYTHON:-python3} -u $PYATS_MCP_SCRIPT" pyats_run_show_command '{"device_name":"R1","command":"show ip ospf neighbor"}'BGP peers = logical connections (may be multi-hop):
PYATS_TESTBED_PATH=$PYATS_TESTBED_PATH python3 $MCP_CALL "${PYATS_PYTHON:-python3} -u $PYATS_MCP_SCRIPT" pyats_run_show_command '{"device_name":"R1","command":"show ip bgp summary"}'EIGRP neighbors:
PYATS_TESTBED_PATH=$PYATS_TESTBED_PATH python3 $MCP_CALL "${PYATS_PYTHON:-python3} -u $PYATS_MCP_SCRIPT" pyats_run_show_command '{"device_name":"R1","command":"show ip eigrp neighbors"}'Step 5: Interface-to-Subnet Mapping
PYATS_TESTBED_PATH=$PYATS_TESTBED_PATH python3 $MCP_CALL "${PYATS_PYTHON:-python3} -u $PYATS_MCP_SCRIPT" pyats_run_show_command '{"device_name":"R1","command":"show ip interface brief"}'Build subnet map:
Interface | IP Address | Subnet | Connected Subnet
Gi0/0/0 | 10.1.1.1/30 | 10.1.1.0/30 | R1 <-> SW1 transit
Gi0/0/1 | 10.1.2.1/30 | 10.1.2.0/30 | R1 <-> R2 transit
Loopback0 | 1.1.1.1/32 | 1.1.1.1/32 | Router IDStep 6: VRF Topology
PYATS_TESTBED_PATH=$PYATS_TESTBED_PATH python3 $MCP_CALL "${PYATS_PYTHON:-python3} -u $PYATS_MCP_SCRIPT" pyats_run_show_command '{"device_name":"R1","command":"show vrf"}'For each VRF, identify:
- VRF name, RD, RT import/export
- Interfaces assigned to the VRF
- Routes in the VRF routing table
Step 7: FHRP Group Mapping
PYATS_TESTBED_PATH=$PYATS_TESTBED_PATH python3 $MCP_CALL "${PYATS_PYTHON:-python3} -u $PYATS_MCP_SCRIPT" pyats_run_show_command '{"device_name":"R1","command":"show standby brief"}'Map virtual IPs, active/standby roles, group numbers, and tracking objects.
Building the Topology Model
Combine all discovery data into a unified model:
Topology: NetClaw Discovery - YYYY-MM-DD
Devices:
R1 (C8000V, IOS-XE 17.x.x)
Loopback0: 1.1.1.1/32 (Router ID)
Gi1: 10.1.1.1/30 → R2:Gi1 (OSPF Area 0, cost 1)
Gi2: 10.1.2.1/24 → SW1:Gi0/1 (Access VLAN 10)
R2 (ISR4431, IOS-XE 17.x.x) [discovered via CDP]
Gi1: 10.1.1.2/30 → R1:Gi1
Gi2: 10.2.1.1/24 → SW2:Gi0/1
Subnets:
10.1.1.0/30 - R1-R2 transit (OSPF Area 0)
10.1.2.0/24 - R1 LAN segment (VLAN 10)
10.2.1.0/24 - R2 LAN segment (VLAN 20)
Routing Adjacencies:
R1 <-> R2: OSPF (Area 0, FULL)
R1 <-> ISP: BGP (AS 65001 <-> AS 65000, Established)
FHRP:
VLAN 10: HSRP Group 10, VIP 10.1.2.254, Active=R1, Standby=R3Integration with Diagram Tools
After discovery, use this data to generate:
- Draw.io diagrams (via drawio-diagram skill) — for formal network documentation
- Markmap mind maps (via markmap-viz skill) — for hierarchical protocol views
- NVD CVE audit (via nvd-cve skill) — using discovered software versions
NetBox Cable Reconciliation (MISSION02 Enhancement)
When NetBox is available ($NETBOX_MCP_SCRIPT is set), reconcile discovered topology against the source of truth:
Pull NetBox Cables
python3 $MCP_CALL "python3 -u $NETBOX_MCP_SCRIPT" netbox_get_objects '{"object_type":"dcim.cables","filters":{},"limit":200}'Pull NetBox Devices
python3 $MCP_CALL "python3 -u $NETBOX_MCP_SCRIPT" netbox_get_objects '{"object_type":"dcim.devices","filters":{},"brief":true}'Pull NetBox Interfaces
python3 $MCP_CALL "python3 -u $NETBOX_MCP_SCRIPT" netbox_get_objects '{"object_type":"dcim.interfaces","filters":{"device":"R1"}}'Reconciliation Categories
Compare CDP/LLDP discovered neighbors against NetBox cables:
| Category | Meaning | Action |
|---|---|---|
| DOCUMENTED | Link exists in both discovery and NetBox | No action |
| UNDOCUMENTED | Link found by CDP/LLDP but not in NetBox | Open ServiceNow incident to update NetBox |
| MISSING | Cable in NetBox but not seen by CDP/LLDP | Investigate — may be physical disconnect |
| MISMATCH | Endpoints differ between discovery and NetBox | Investigate — possible re-patching |
Show full SKILL.md (207 more words)Show less
Color-Coded Draw.io Diagram
Generate a Draw.io topology diagram with links color-coded by reconciliation status:
- Green: DOCUMENTED
- Yellow: UNDOCUMENTED
- Red: MISSING
- Orange: MISMATCH
Fleet-Wide Discovery (pCall)
Run CDP/LLDP/ARP/routing peer collection across ALL devices simultaneously using multiple exec commands. Merge results to build the complete topology graph.
GAIT Audit Trail
Record the topology discovery in GAIT:
python3 $MCP_CALL "python3 -u $GAIT_MCP_SCRIPT" gait_record_turn '{"user_text":"Example only: replace with the actual authorized request.","assistant_text":"Topology discovery completed: 5 devices, 12 links. NetBox reconciliation: 10 documented, 1 undocumented, 1 missing.","artifacts":[]}'Failure Behavior
- If a tool call fails with an authentication or connection error, check that
GAIT_MCP_SCRIPT,NETBOX_MCP_SCRIPT,PYATS_MCP_SCRIPT,PYATS_TESTBED_PATHare set and valid before assuming a data or device problem. - 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.
Audit examples are illustrative. Replace request, outcomes, identifiers and counts
with observed session evidence; do not record these example results as facts.
Inspect MCP isError, returned ok, and the recorded turn with gait_show when
validating a new client/schema. Follow gait-session-tracking for branch checkout.
© automateyournetwork, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in workspace/skills/pyats-topology of automateyournetwork/netclaw.
Open the folder on GitHubat commit aa90e7d
Compare with similar skills
Pyats Topology next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Pyats Topology this skillautomateyournetwork/netclaw | 676 | — | ~2.1k | Automated safety check: Pass | Apache-2.0 | |
| Documd Visualsmarkdown-viewer/skills | 3.4k | — | ~3.3k | Automated safety check: Pass | CC-BY-4.0 | |
| Audit Swimlane Flowchart Generatornigo81/nigo-skills | 133 | — | ~2.1k | Automated safety check: Pass | MIT | |
| Diagram Designcathrynlavery/diagram-design | 49k | 1 repos | ~7.6k | Automated safety check: Pass | MIT | |
| Draw.io Diagram StudioAgents365-ai/drawio-skill | 10k | — | ~2.4k | Automated safety check: Notes | MIT | |
| Draw.io Diagram ReconstructionHKUSTDial/Supervisor-Skills | 8.8k | — | ~5.4k | Automated safety check: Pass | MIT |
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Questions about Pyats Topology
What does Pyats Topology do?
Network topology discovery via CDP/LLDP neighbors, ARP tables, routing peers, and interface mapping to build complete network maps. Pyats Topology is an agent skill from automateyournetwork/netclaw. Network topology discovery via CDP/LLDP neighbors, ARP tables, routing peers, and interface mapping to build complete network maps.
When should I use Pyats Topology?
Pyats Topology fits situations like: mapping the network; building a diagram; discovering what is connected to what; documenting device neighbors and links.
How do I install Pyats Topology in Claude Code?
Run `npx skills add automateyournetwork/netclaw --skill pyats-topology -a claude-code`. Or copy the skill folder (workspace/skills/pyats-topology in automateyournetwork/netclaw) into .claude/skills/pyats-topology in your project. Claude Code loads it when a task matches its description.
How do I install Pyats Topology in Codex?
Run `npx skills add automateyournetwork/netclaw --skill pyats-topology -a codex`. Or copy the skill folder (workspace/skills/pyats-topology in automateyournetwork/netclaw) into .agents/skills/pyats-topology in your project. Codex loads it when a task matches its description.
Can I use Pyats Topology in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add automateyournetwork/netclaw --skill pyats-topology -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/pyats-topology, .gemini/skills/pyats-topology, .github/skills/pyats-topology and .opencode/skills/pyats-topology in your project.
What does Pyats Topology need to run?
Going by SKILL.md and its folder, Pyats Topology needs the command-line tools its instructions call (python3). Our summary lists: Python 3.
Does Pyats Topology access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Pyats Topology safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Pyats Topology use?
Pyats Topology is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Pyats Topology use?
About 2.1k tokens (SKILL.md is roughly 8.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Pyats Topology?
Skills that share tags, products or a category with Pyats Topology: Documd Visuals (markdown-viewer/skills, 3.4k stars), Audit Swimlane Flowchart Generator (nigo81/nigo-skills, 133 stars), Diagram Design (cathrynlavery/diagram-design, 49k stars) and Draw.io Diagram Studio (Agents365-ai/drawio-skill, 10k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Pyats Topology?
automateyournetwork (a GitHub user) maintains it in automateyournetwork/netclaw, which has 676 GitHub stars. The repository holds 120 skills in this directory. The repository was last updated on October 9, 2026.
Source: automateyournetwork/netclaw on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.