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 meltedinhex/analyst-ai-pack --skill tracking-adversary-infrastructuregit clone --depth 1 https://github.com/meltedinhex/analyst-ai-packWrote 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/meltedinhex/analyst-ai-pack/tracking-adversary-infrastructure)<a href="https://agentmods.dev/skills/meltedinhex/analyst-ai-pack/tracking-adversary-infrastructure"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/tracking-adversary-infrastructure/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/meltedinhex/analyst-ai-pack/tracking-adversary-infrastructure"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/tracking-adversary-infrastructure.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 | $0.00067 | $0.00627 |
| Opus 5 | $0.00034 | $0.00313 |
| Sonnet 5 | $0.00013 | $0.00125 |
| Haiku 4.5 | $0.00007 | $0.00063 |
Grade A, and why
tracking-adversary-infrastructure 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.
What it actually says
Tracking Adversary Infrastructure
When to Use
- You have a set of suspected C2/staging hosts with attributes (JARM/JA3S, TLS cert fields, favicon hash, ASN, ports, registrar) and want to cluster them to find related infrastructure.
- You are expanding from a few known indicators to the adversary's broader footprint.
Do not use clustering as confirmed attribution — shared hosting/attributes can be coincidental. Corroborate before attributing.
Prerequisites
- Host records (CSV/JSON) with shared-attribute fields to cluster on.
Workflow
Step 1: Cluster on shared attributes
python scripts/analyst.py cluster hosts.json --attrs jarm,cert_cn,favicon_hash,asn
Groups hosts that share one or more pivot attributes into clusters, defanging host indicators.
Step 2: Score cluster cohesion
Rank clusters by how many distinct attributes the members share (more shared attributes → stronger relationship).
Step 3: Expand and confirm
Use the strongest shared attributes (e.g., a unique self-signed cert CN or favicon hash) to pivot in internet-scan data for more hosts; corroborate.
Step 4: Document
Record clusters, the shared pivots, and confidence; feed confirmed indicators to detection.
Validation
- Clustering keys on explicit shared attributes, not loose similarity.
- Cluster strength reflects the count of distinct shared attributes.
- Host indicators are defanged in output.
Pitfalls
- Common CDN/cloud JARM/ASN values creating huge false clusters — exclude generic pivots.
- Default certificates shared by unrelated servers.
- Treating one weak shared attribute as a strong link.
References
- See
references/api-reference.mdfor the clustering tool. - ATT&CK T1583 and JARM references (linked in frontmatter).
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.
- 9d ago First seen · 85 lines · 67 tokens per session scan A ae631535915b
tracking-adversary-infrastructure is a skill published in the GitHub repository meltedinhex/analyst-ai-pack (22 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 67 tokens to every session and 627 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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