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 hunting-dns-tunneling-and-exfiltrationgit 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/hunting-dns-tunneling-and-exfiltration)<a href="https://agentmods.dev/skills/meltedinhex/analyst-ai-pack/hunting-dns-tunneling-and-exfiltration"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/hunting-dns-tunneling-and-exfiltration/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/hunting-dns-tunneling-and-exfiltration"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/hunting-dns-tunneling-and-exfiltration.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 | $0.00074 | $0.00788 |
| Opus 5 | $0.00037 | $0.00394 |
| Sonnet 5 | $0.00015 | $0.00158 |
| Haiku 4.5 | $0.00007 | $0.00079 |
Grade A, and why
hunting-dns-tunneling-and-exfiltration 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 — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hunting DNS Tunneling and Exfiltration
When to Use
- You have DNS query logs (resolver, Zeek
dns.log, Windows DNS) and want to find tunneling or exfiltration over DNS. - You are testing a hypothesis that an implant uses DNS as a covert channel.
- You need to rank domains by tunneling indicators across many queries.
Do not use query length alone as a verdict — CDNs, antivirus lookups, and some SaaS use long encoded labels legitimately; combine entropy, volume, and record-type signals.
Prerequisites
- DNS logs with query name, query type, source, and timestamp, over a meaningful window.
- A way to whitelist known-benign high-volume domains (security vendors, CDNs).
Workflow
Step 1: Aggregate by registered domain
Group queries under their registered/parent domain so per-domain volume and subdomain diversity are visible.
Step 2: Score tunneling indicators
Weight: high unique-subdomain count, long average label length, high subdomain entropy (encoded
data), heavy TXT/NULL/CNAME usage, and high query rate to one domain.
python scripts/analyst.py hunt dns.json
Step 3: Estimate exfiltration volume
Sum encoded bytes implied by query lengths per domain; sustained high volume to a single nameserver suggests data egress.
Step 4: Whitelist and pivot
Remove known-benign high-entropy domains; for survivors, check domain age/registration, the authoritative nameserver, and the originating host.
Step 5: Confirm and respond
Validate by decoding sampled labels where possible and correlating with host activity; escalate confirmed tunnels and write a detection.
Validation
- High-score domains combine multiple signals (entropy + volume + record type), not one alone.
- Benign high-volume domains are whitelisted, keeping the list reviewable.
- Confirmed tunnels tie to a specific host and a young/suspicious domain.
Pitfalls
- Flagging long labels from CDNs and AV telemetry as tunneling.
- Aggregating by full FQDN instead of registered domain, hiding subdomain diversity.
- Ignoring record type;
TXT/NULLcarry far more data thanArecords.
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 · 95 lines · 74 tokens per session scan A c23d70afef04
hunting-dns-tunneling-and-exfiltration is a skill published in the GitHub repository meltedinhex/analyst-ai-pack (22 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 74 tokens to every session and 788 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 2026-09-03.
Other skills, from other repositories
analyzing-cobalt-strike-beacon-configuration
Extract and analyze Cobalt Strike beacon configuration from PE files and memory dumps to identify C2 infrastructure, malleable profiles, and operator tradecraft.
analyzing-golang-malware-with-ghidra
Reverse engineer Go-compiled malware in Ghidra by parsing Go buildinfo and pclntab structures, recovering stripped/obfuscated function names (e.g. via GoResolver), and extracting embedded module/dependency strings and types from Go binaries. Use when analyzing a Go-language malware sample, deobfuscating a…
analyzing-malicious-pdf-with-peepdf
Perform static analysis of malicious PDF documents using peepdf, pdfid, and pdf-parser to extract embedded JavaScript, shellcode, and suspicious objects. Use when triaging a suspicious PDF attachment from a phishing email, analyzing a PDF-based exploit document, or building detection signatures for weaponized PDF…
dns-security
Performs a structured DNS security review against NIST SP 800-81 Rev 2 (Secure Domain Name System Deployment Guide) and CIS Controls v8 (Control 9.2 -- Use DNS Filtering Services). Auto-invoked when reviewing DNS configurations, DNSSEC deployment, or investigating DNS-based exfiltration and tunneling indicators.…
analyzing-malicious-pdf-with-peepdf
A Chinese-language skill for examining suspicious PDF files with peepdf, pdfid, and pdf-parser. It is intended for static malware analysis, which studies a file without running it.
yara-rule-writing-malware
Write custom YARA rules to identify and classify malware based on textual and binary patterns. This skill focuses on creating robust signatures using strings, regular expressions, and hexadecimal opcodes extracted during malware analysis for enterprise threat hunting.