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 andycungkrinx91/konoha --skill building-adversary-infrastructure-tracking-systemgit clone --depth 1 https://github.com/andycungkrinx91/konohaWrote 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/andycungkrinx91/konoha/building-adversary-infrastructure-tracking-system)<a href="https://agentmods.dev/skills/andycungkrinx91/konoha/building-adversary-infrastructure-tracking-system"><img src="https://agentmods.dev/badge/skills/andycungkrinx91/konoha/building-adversary-infrastructure-tracking-system/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/andycungkrinx91/konoha/building-adversary-infrastructure-tracking-system"><img src="https://agentmods.dev/badge/skills/andycungkrinx91/konoha/building-adversary-infrastructure-tracking-system.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00044 | $0.03049 |
| Opus 5.5 | $0.00018 | $0.01220 |
| Sonnet 5.5 | $0.00009 | $0.00610 |
| Haiku 4.5 | $0.00004 | $0.00305 |
Grade A, and why
building-adversary-infrastructure-tracking-system scanned grade A with 1 finding 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 today.
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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
resp = requests.get(url, headers=headers, timeout=30) This is a copy
100% identical to building-adversary-infrastructure-tracking-system — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 348 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Building Adversary Infrastructure Tracking System
Overview
Adversary infrastructure tracking uses passive DNS records, certificate transparency logs, WHOIS registration data, and IP enrichment to discover, map, and monitor threat actor command-and-control (C2) networks. Attackers frequently reuse hosting providers, registrars, SSL certificates, and naming patterns across campaigns, enabling analysts to pivot from known indicators to discover new infrastructure. This skill covers building an automated tracking system that identifies infrastructure relationships, detects newly registered domains matching adversary patterns, and maintains a continuously updated map of threat actor networks.
When to Use
- When deploying or configuring building adversary infrastructure tracking system capabilities in your environment
- When establishing security controls aligned to compliance requirements
- When building or improving security architecture for this domain
- When conducting security assessments that require this implementation
Prerequisites
- Python 3.9+ with
requests,dnspython,python-whois,shodan,networkxlibraries - API keys: SecurityTrails, PassiveTotal/RiskIQ, Shodan, VirusTotal
- Access to passive DNS data sources
- Understanding of DNS infrastructure, hosting, and domain registration
- Graph database (Neo4j) or NetworkX for relationship visualization
Key Concepts
Passive DNS
Passive DNS captures historical DNS resolution data, recording which domains resolved to which IPs and when. Unlike active DNS queries, passive DNS preserves historical relationships even after records change, enabling analysts to track infrastructure changes, identify shared hosting patterns, and discover related domains that resolved to the same IP addresses over time.
Infrastructure Pivoting
Pivoting identifies related infrastructure by following connections: IP pivot (find all domains on an IP), domain pivot (find all IPs a domain resolved to), WHOIS pivot (find domains with same registrant), certificate pivot (find hosts sharing SSL certificates), and NS/MX pivot (find domains using same name servers or mail servers).
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- today First seen · 348 lines · 44 tokens per session scan A c8cbb72ee7e8
building-adversary-infrastructure-tracking-system is a skill published in the GitHub repository andycungkrinx91/konoha (12 stars, last pushed today), licensed MIT. It adds 44 tokens to every session and 3,049 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). It is 100% identical to building-adversary-infrastructure-tracking-system, differing in 0 lines, and is treated as a copy.
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