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 pyats-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/pyats-topology)<a href="https://agentmods.dev/skills/automateyournetwork/netclaw/pyats-topology"><img src="https://agentmods.dev/badge/skills/automateyournetwork/netclaw/pyats-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/pyats-topology"><img src="https://agentmods.dev/badge/skills/automateyournetwork/netclaw/pyats-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 | $0.00056 | $0.02176 |
| Opus 5 | $0.00028 | $0.01088 |
| Sonnet 5 | $0.00011 | $0.00435 |
| Haiku 4.5 | $0.00006 | $0.00218 |
Grade A, and why
pyats-topology scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 203 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Topology Discovery
When to Use
- Building network diagrams from scratch (no documentation exists)
- Validating existing documentation matches reality
- Pre-change topology baseline
- Incident response — understanding blast radius
- New device onboarding — mapping where it connects
Discovery Procedure
Step 1: CDP Neighbors (Cisco-to-Cisco)
PYATS_TESTBED_PATH=$PYATS_TESTBED_PATH python3 $MCP_CALL "${PYATS_PYTHON:-python3} -u $PYATS_MCP_SCRIPT" pyats_run_show_command '{"device_name":"R1","command":"show cdp neighbors detail"}'
Extract per neighbor:
- Device ID (hostname)
- Platform and model
- IP address (management address)
- Local interface → Remote interface (link mapping)
- Software version
- Native VLAN (on switch links)
- Duplex
Build adjacency table:
Local Device | Local Interface | Remote Device | Remote Interface | Remote Platform
R1 | Gi0/0/0 | SW1 | Gi1/0/1 | WS-C3850-24T
R1 | Gi0/0/1 | R2 | Gi0/0/0 | ISR4431
Step 2: LLDP Neighbors (Multi-Vendor)
PYATS_TESTBED_PATH=$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
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.
- 9d ago First seen · 203 lines · 56 tokens per session scan A de3c8444c5e8
pyats-topology is a skill published in the GitHub repository automateyournetwork/netclaw (657 stars, last pushed 6d ago), licensed Apache-2.0. It adds 56 tokens to every session and 2,176 once invoked, about $0.0003 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 2026-09-03.
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