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 capturing-and-analyzing-malware-network-trafficgit 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/capturing-and-analyzing-malware-network-traffic)<a href="https://agentmods.dev/skills/meltedinhex/analyst-ai-pack/capturing-and-analyzing-malware-network-traffic"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/capturing-and-analyzing-malware-network-traffic/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/capturing-and-analyzing-malware-network-traffic"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/capturing-and-analyzing-malware-network-traffic.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 | $0.00078 | $0.00839 |
| Opus 5 | $0.00039 | $0.00419 |
| Sonnet 5 | $0.00016 | $0.00168 |
| Haiku 4.5 | $0.00008 | $0.00084 |
Grade A, and why
capturing-and-analyzing-malware-network-traffic 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 10d 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 — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Capturing and Analyzing Malware Network Traffic
When to Use
- You detonated a sample and captured a PCAP on the services guest, and need to extract its network behavior.
- You want C2 endpoints, HTTP/DNS patterns, TLS SNI/JA3, and beaconing intervals.
- You are turning traffic observations into network IOCs and detection signatures.
Do not use a single capture as the full C2 picture — staged samples reveal more on longer runs or with the right context. Combine with binary config extraction.
Prerequisites
- A PCAP captured from the isolated lab (capture on the analysis guest, not the victim).
tshark/Wireshark, or Zeek for connection logs, or the bundled PCAP summarizer.- The lab's simulated-internet IP so you can separate sample traffic from noise.
Safety & Handling
- Capture from the services/analysis guest so the victim never hosts a sniffer.
- Defang all extracted domains/IPs/URLs before sharing.
Workflow
Step 1: Get a connection overview
Summarize conversations and protocols to see who talks to whom and how often:
python scripts/analyst.py summarize capture.pcap
Step 2: Analyze DNS
List queried domains and resolved IPs. Look for DGA-like randomness, repeated NXDOMAIN, and long TXT records (possible tunneling).
Step 3: Inspect HTTP/HTTPS
For HTTP, examine URIs, methods, User-Agent (often unique/odd), and POST bodies. For HTTPS, extract SNI and JA3/JA3S fingerprints since payloads are encrypted.
Step 4: Detect beaconing
Compute inter-arrival times to the same destination. Regular intervals (with jitter) to one host indicate C2 check-ins.
Step 5: Build IOCs and detection
Produce a network IOC set (domains, IPs, URIs, JA3, User-Agent) and hand candidate signatures to the Suricata/Zeek detection workflow.
Validation
- Beaconing intervals are consistent and tied to a specific destination, not random browsing.
- Extracted SNI/JA3 reproduce across runs of the same sample.
- DNS and HTTP observations corroborate the C2 endpoints found in the binary's config.
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.
- 10d ago First seen · 102 lines · 78 tokens per session scan A bfc5c53e123c
capturing-and-analyzing-malware-network-traffic is a skill published in the GitHub repository meltedinhex/analyst-ai-pack (22 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 78 tokens to every session and 839 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 2026-08-30.
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