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 26zl/cybersec-toolkit --skill building-adversary-infrastructure-tracking-systemgit clone --depth 1 https://github.com/26zl/cybersec-toolkitWrote 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/26zl/cybersec-toolkit/building-adversary-infrastructure-tracking-system)<a href="https://agentmods.dev/skills/26zl/cybersec-toolkit/building-adversary-infrastructure-tracking-system"><img src="https://agentmods.dev/badge/skills/26zl/cybersec-toolkit/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/26zl/cybersec-toolkit/building-adversary-infrastructure-tracking-system"><img src="https://agentmods.dev/badge/skills/26zl/cybersec-toolkit/building-adversary-infrastructure-tracking-system.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 5 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Data Exfiltration · line 88 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 109 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 124 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 279 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 282 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 | $0.00022 | $0.01524 |
| Sonnet 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 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.
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) Copies of this mod
1 near-identical copy found in the catalogue:
- building-adversary-infrastructure-tracking-system — 89% identical, 4 lines differ
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
3 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.
- 8d ago First seen · 348 lines · 44 tokens per session scan A c8cbb72ee7e8
building-adversary-infrastructure-tracking-system is a skill published in the GitHub repository 26zl/cybersec-toolkit (54 stars, last pushed yesterday), licensed MIT. It adds 44 tokens to every session and 3,049 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
building-adversary-infrastructure-tracking-system
Build an automated system to track adversary infrastructure using passive DNS, certificate transparency, WHOIS data, and IP enrichment to map and monitor threat actor command-and-control networks.
building-adversary-infrastructure-tracking-system
Build an automated adversary infrastructure tracking system in Python (dnspython, python-whois, shodan, networkx) that pivots across passive DNS, certificate transparency logs, WHOIS records, and IP enrichment to map threat-actor C2 networks and flag newly registered domains matching known patterns. Use when pivoting…
building-adversary-infrastructure-tracking-system
Build an automated system to track adversary infrastructure using passive DNS, certificate transparency, WHOIS data, and IP enrichment to map and monitor threat actor command-and-control networks.
building-adversary-infrastructure-tracking-system
Build an automated system to track adversary infrastructure using passive DNS, certificate transparency, WHOIS data, and IP enrichment to map and monitor threat actor command-and-control networks.
analyzing-apt-group-with-mitre-navigator
Query ATT&CK data with attackcti, mitreattack-python, and stix2, then build MITRE ATT&CK Navigator layers and multi-layer heatmap overlays mapping one or more APT groups' TTPs for detection-gap analysis. Use to compare threat-actor technique coverage, find gaps in detection engineering, or produce Navigator…
analyzing-malware-family-relationships-with-malpedia
Query the Malpedia API to look up malware family aliases and naming (platform.familyname), pull community/vendor YARA rules, link families to threat actors, and map family relationships such as loader-payload chains and shared authorship. Use when researching a malware family's aliases, lineage, or actor attribution…