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-c2-beaconing-with-frequency-analysisgit 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-c2-beaconing-with-frequency-analysis)<a href="https://agentmods.dev/skills/meltedinhex/analyst-ai-pack/hunting-c2-beaconing-with-frequency-analysis"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/hunting-c2-beaconing-with-frequency-analysis/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-c2-beaconing-with-frequency-analysis"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/hunting-c2-beaconing-with-frequency-analysis.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 | $0.00766 |
| Opus 5 | $0.00036 | $0.00383 |
| Sonnet 5 | $0.00015 | $0.00153 |
| Haiku 4.5 | $0.00007 | $0.00077 |
Grade A, and why
hunting-c2-beaconing-with-frequency-analysis 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 — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hunting C2 Beaconing with Frequency Analysis
When to Use
- You have connection logs (proxy, firewall, Zeek/
conn.log, NetFlow) and want to find implants that call home on a schedule. - You are testing a hypothesis that a beacon is hiding in normal-looking web traffic.
- You need to rank source→destination pairs by timing regularity, allowing for jitter.
Do not use pure interval regularity as a verdict — software update checks, telemetry, and keep-alives also beacon; combine timing with destination reputation and data volume.
Prerequisites
- Connection records with timestamp, source, destination (IP/domain), and ideally bytes, over a window long enough to capture many callbacks (hours to days).
- A way to enrich destinations (reputation, age, rarity) for follow-up.
Workflow
Step 1: Group and order by pair
Bucket connections by (source, destination) and sort timestamps. Require a minimum count so the interval statistics are meaningful.
Step 2: Compute inter-arrival intervals
Derive deltas between consecutive connections per pair; the interval distribution reveals periodicity.
Step 3: Score regularity with jitter tolerance
A low coefficient of variation (std/mean) of intervals indicates a steady beacon; modern beacons add jitter, so score on tolerance rather than requiring identical intervals.
python scripts/analyst.py beacon conn.json --min-events 8
Step 4: Reduce false positives
Down-rank known update/telemetry destinations and CDNs; up-rank rare/young domains, small fixed payload sizes, and odd ports.
Step 5: Triage and confirm
For top pairs, pull payloads/JA3, destination intel, and host context; confirm via the C2/beacon config skills and escalate.
Validation
- Top candidates show consistently spaced callbacks (low CV) over many events, not a handful.
- Known-benign periodic services are filtered or explained.
- Confirmed beacons corroborate with destination reputation or payload analysis.
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 · 96 lines · 73 tokens per session scan A affefba8b85f
hunting-c2-beaconing-with-frequency-analysis is a skill published in the GitHub repository meltedinhex/analyst-ai-pack (22 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 73 tokens to every session and 766 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…
hunting-for-beaconing-with-frequency-analysis
Identify command-and-control beaconing patterns in network traffic by applying statistical frequency analysis, jitter calculation, and coefficient of variation scoring to detect periodic callbacks from compromised endpoints.
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.