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-ip-enrichmentgit 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-ip-enrichment)<a href="https://agentmods.dev/skills/automateyournetwork/netclaw/gtrace-ip-enrichment"><img src="https://agentmods.dev/badge/skills/automateyournetwork/netclaw/gtrace-ip-enrichment/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-ip-enrichment"><img src="https://agentmods.dev/badge/skills/automateyournetwork/netclaw/gtrace-ip-enrichment.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 | $0.00064 | $0.01103 |
| Opus 5 | $0.00032 | $0.00551 |
| Sonnet 5 | $0.00013 | $0.00221 |
| Haiku 4.5 | $0.00006 | $0.00110 |
Grade A, and why
gtrace-ip-enrichment 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 — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
IP Address Enrichment with gtrace
How to Call the gtrace MCP Tools
python3 $MCP_CALL "gtrace mcp" TOOL_NAME '{"param":"value"}'
When to Use
- Identify who owns an IP address (ASN, organization name, network range)
- Determine the geographic location of an IP (city, region, country, coordinates)
- Resolve an IP address to its PTR/reverse DNS hostname
- Enrich traceroute hop data with ASN and geo context
- Investigate unknown IPs appearing in logs, flow data, or routing tables
- Map network paths to physical geography for latency analysis
Available Tools
| Tool | Purpose |
|---|---|
asn_lookup |
Look up ASN, organization, and network range for an IP |
geo_lookup |
Get geographic location (city, region, country, lat/lon) for an IP |
reverse_dns |
Resolve an IP to its PTR record (reverse DNS hostname) |
Workflow: IP Investigation
When asked "who owns this IP?" or "where is this IP?":
Step 1: ASN Lookup
Identify the Autonomous System and organization that owns the IP.
python3 $MCP_CALL "gtrace mcp" asn_lookup '{"ip":"8.8.8.8"}'
Returns: ASN number, organization name, network CIDR, registry (ARIN, RIPE, APNIC, etc.)
Step 2: Geolocation
Determine the physical location of the IP.
python3 $MCP_CALL "gtrace mcp" geo_lookup '{"ip":"8.8.8.8"}'
Returns: City, region/state, country, latitude/longitude, timezone
Step 3: Reverse DNS
Resolve the IP to its PTR record for hostname identification.
python3 $MCP_CALL "gtrace mcp" reverse_dns '{"ip":"8.8.8.8"}'
Returns: PTR hostname (e.g., dns.google)
Workflow: Traceroute Hop Enrichment
After running a traceroute (via gtrace-path-analysis skill), enrich each hop with ASN and geo data:
- Run
tracerouteto get the path with hop IPs - For each hop IP, run
asn_lookupto identify the network owner - For key hops (transit boundaries, high-latency hops), run
geo_lookupto map physical location - Use
reverse_dnson hops to identify router naming conventions (often reveals ISP, POP location, interface type)
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 · 120 lines · 64 tokens per session scan A 07fc2c00408d
gtrace-ip-enrichment is a skill published in the GitHub repository automateyournetwork/netclaw (657 stars, last pushed 6d ago), licensed Apache-2.0. It adds 64 tokens to every session and 1,103 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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