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 gtrace-path-analysisgit 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/gtrace-path-analysis)<a href="https://agentmods.dev/skills/automateyournetwork/netclaw/gtrace-path-analysis"><img src="https://agentmods.dev/badge/skills/automateyournetwork/netclaw/gtrace-path-analysis/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/gtrace-path-analysis"><img src="https://agentmods.dev/badge/skills/automateyournetwork/netclaw/gtrace-path-analysis.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 | $0.00080 | $0.01094 |
| Opus 5 | $0.00040 | $0.00547 |
| Sonnet 5 | $0.00016 | $0.00219 |
| Haiku 4.5 | $0.00008 | $0.00109 |
Grade A, and why
gtrace-path-analysis 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 8d 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 — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Network Path Analysis with gtrace
How to Call the gtrace MCP Tools
python3 $MCP_CALL "gtrace mcp" TOOL_NAME '{"param":"value"}'
When to Use
- Trace the path between two endpoints and identify each hop (router, latency, loss)
- Detect MPLS labels, ECMP load balancing, and NAT translation points along the path
- Monitor a path continuously with MTR to identify intermittent packet loss or latency spikes
- Run distributed traceroutes from 500+ GlobalPing probe locations worldwide to compare paths from different vantage points
- Verify transit providers, peering, and routing policy by inspecting AS-level path data
- Troubleshoot asymmetric routing, suboptimal paths, or blackholes
Available Tools
| Tool | Purpose | Requirements |
|---|---|---|
traceroute |
Advanced traceroute with MPLS, ECMP, and NAT detection | cap_net_raw |
mtr |
Continuous MTR monitoring with packet loss and jitter stats | cap_net_raw |
globalping |
Distributed traceroute/ping from 500+ worldwide probe locations | Internet access (optional GLOBALPING_API_KEY) |
Workflow: Path Troubleshooting
When asked "why is traffic slow to X?" or "trace the path to X":
Step 1: Traceroute
Run an advanced traceroute to see every hop, latency, and any MPLS/ECMP/NAT indicators.
python3 $MCP_CALL "gtrace mcp" traceroute '{"target":"8.8.8.8"}'
For IPv6:
python3 $MCP_CALL "gtrace mcp" traceroute '{"target":"2001:4860:4860::8888"}'
Step 2: Continuous Monitoring with MTR
If the traceroute shows packet loss or high latency at a specific hop, run MTR to monitor continuously and confirm the problem is persistent.
python3 $MCP_CALL "gtrace mcp" mtr '{"target":"8.8.8.8","count":100}'
Step 3: Global Perspective
Compare paths from multiple worldwide locations to determine if the issue is local or global.
python3 $MCP_CALL "gtrace mcp" globalping '{"target":"8.8.8.8","from":"US,EU,Asia"}'
Workflow: MPLS Path Verification
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.
- 8d ago First seen · 117 lines · 80 tokens per session scan A 0cadc138b3b0
gtrace-path-analysis is a skill published in the GitHub repository automateyournetwork/netclaw (655 stars, last pushed 5d ago), licensed Apache-2.0. It adds 80 tokens to every session and 1,094 once invoked, about $0.0004 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.
Other skills, from other repositories
gke-ai-troubleshooting-tpu-dynamic-slices-monitoring
Monitors, troubleshoots, and manages GKE TPU Dynamic Slices custom resources. Use when checking TPU slice lifecycle states, troubleshooting slice provisioning failures, validating single-slice or multi-slice (JobSet) workload manifests, or safely patching stuck finalizers and disabling the slice controller. Don't use…
gke-ai-troubleshooting-tpu-vbar-oom
Diagnoses and prevents vbarcontrolagent segfaults, out-of-memory (OOM) errors, and TPU device initialization failures on TPU v6e nodes in GKE caused by race conditions during TPU device resets or high-frequency metrics polling. Use when troubleshooting vbarcontrolagent crashes, memory cgroup OOMs in serial console…
competition-firmware-layout
Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for firmware images, partition tables, boot chains, update packages, extracted filesystems, embedded configs, and device-facing trust boundaries. Use when the user asks to unpack firmware, map partition layout, inspect bootloader or init…
doca-flow
Build and debug DOCA Flow applications on supported NVIDIA NICs/DPUs: define match/action pipes, initialize ports and representors, choose forwarding targets, validate pipes before hardware programming, read counters, match the Flow version to the installed DOCA release, and diagnose Flow API errors. Trigger on DOCA…
diagnose-driver-install
Diagnose NVIDIA driver installation failures on DeepOps-managed nodes — nvidia-smi errors, "No devices were found", DKMS build failures, or GPU pods crash-looping. Use before reinstalling anything.
eide
A build tool for EIDE projects, an embedded-development extension for Visual Studio Code. It finds EIDE project settings and builds firmware using ARM CC or GCC.