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 analyzing-dns-logs-for-exfiltrationgit 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/analyzing-dns-logs-for-exfiltration)<a href="https://agentmods.dev/skills/26zl/cybersec-toolkit/analyzing-dns-logs-for-exfiltration"><img src="https://agentmods.dev/badge/skills/26zl/cybersec-toolkit/analyzing-dns-logs-for-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/26zl/cybersec-toolkit/analyzing-dns-logs-for-exfiltration"><img src="https://agentmods.dev/badge/skills/26zl/cybersec-toolkit/analyzing-dns-logs-for-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 | $0.00073 | $0.03127 |
| Opus 5 | $0.00036 | $0.01563 |
| Sonnet 5 | $0.00015 | $0.00625 |
| Haiku 4.5 | $0.00007 | $0.00313 |
Grade A, and why
analyzing-dns-logs-for-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 11d 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.
This is a copy
100% identical to analyzing-dns-logs-for-exfiltration — 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 — 310 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyzing DNS Logs for Exfiltration
When to Use
Use this skill when:
- SOC teams suspect data exfiltration through DNS tunneling to bypass firewall/proxy controls
- Threat intelligence indicates adversaries using DNS-based C2 channels (e.g., Cobalt Strike DNS beacon)
- UEBA detects anomalous DNS query volumes from specific hosts
- Malware analysis reveals DNS-over-HTTPS (DoH) or DNS tunneling capabilities
Do not use for standard DNS troubleshooting or availability monitoring — this skill focuses on security-relevant DNS abuse detection.
Prerequisites
- DNS query logging enabled (Windows DNS Server, Bind, Infoblox, or Cisco Umbrella)
- DNS logs ingested into SIEM (Splunk with
Stream:DNS,dnssourcetype, or Zeek DNS logs) - Passive DNS data for historical domain resolution analysis
- Baseline of normal DNS behavior (query volume, domain distribution, TXT record frequency)
- Python with
mathandcollectionslibraries for entropy calculation
Workflow
Step 1: Detect DNS Tunneling via Subdomain Length Analysis
DNS tunneling encodes data in subdomain labels, creating unusually long queries:
index=dns sourcetype="stream:dns" query_type IN ("A", "AAAA", "TXT", "CNAME", "MX")
| eval domain_parts = split(query, ".")
| eval subdomain = mvindex(domain_parts, 0, mvcount(domain_parts)-3)
| eval subdomain_str = mvjoin(subdomain, ".")
| eval subdomain_len = len(subdomain_str)
| eval tld = mvindex(domain_parts, -1)
| eval registered_domain = mvindex(domain_parts, -2).".".tld
| where subdomain_len > 50
| stats count AS queries, dc(query) AS unique_queries,
avg(subdomain_len) AS avg_subdomain_len,
max(subdomain_len) AS max_subdomain_len,
values(src_ip) AS sources
by registered_domain
| where queries > 20
| sort - avg_subdomain_len
| table registered_domain, queries, unique_queries, avg_subdomain_len, max_subdomain_len, sources
Step 2: Detect High-Entropy Domain Queries (DGA Detection)
Domain Generation Algorithms produce random-looking domains:
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.
- 11d ago First seen · 310 lines · 73 tokens per session scan A f09f70a215a8
analyzing-dns-logs-for-exfiltration is a skill published in the GitHub repository 26zl/cybersec-toolkit (54 stars, last pushed today), licensed MIT. It adds 73 tokens to every session and 3,127 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to analyzing-dns-logs-for-exfiltration, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
analyzing-dns-logs-for-exfiltration
Analyzes DNS query logs to detect data exfiltration via DNS tunneling, DGA domain communication, and covert C2 channels using entropy analysis, query volume anomalies, and subdomain length detection in SIEM platforms. Use when SOC teams need to identify DNS-based threats that bypass traditional network security…
analyzing-dns-logs-for-exfiltration
Use when analyzes DNS query logs to detect data exfiltration via DNS tunneling, DGA domain communication, and covert C2 channels using entropy analysis, query volume anomalies, and subdomain length detection in SIEM platforms. Use when SOC teams need to identify DNS-based threats that bypass traditional network…
analyzing-dns-logs-for-exfiltration
A security-analysis workflow for finding suspicious use of DNS, the system that translates domain names into network addresses. It looks for signs of DNS tunnelling, where data is hidden in DNS requests, as well as algorithmically generated domains and hidden command channels.
analyzing-dns-logs-for-exfiltration
Analyzes DNS query logs to detect data exfiltration via DNS tunneling, DGA domain communication, and covert C2 channels using entropy analysis, query volume anomalies, and subdomain length detection in SIEM platforms. Use when SOC teams need to identify DNS-based threats that bypass traditional network security…
analyzing-dns-logs-for-exfiltration
Analyzes DNS query logs to detect data exfiltration via DNS tunneling, DGA domain communication, and covert C2 channels using entropy analysis, query volume anomalies, and subdomain length detection in SIEM platforms. Use when SOC teams need to identify DNS-based threats that bypass traditional network security…
analyzing-dns-logs-for-exfiltration
Analyzes DNS query logs to detect data exfiltration via DNS tunneling, DGA domain communication, and covert C2 channels using entropy analysis, query volume anomalies, and subdomain length detection in SIEM platforms. Use when SOC teams need to identify DNS-based threats that bypass traditional network security…