Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/trilwu/secskillsnpx agentmods add skills/trilwu/secskills/analyzing-network-trafficWrote 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/trilwu/secskills/analyzing-network-traffic)<a href="https://agentmods.dev/skills/trilwu/secskills/analyzing-network-traffic"><img src="https://agentmods.dev/badge/skills/trilwu/secskills/analyzing-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/trilwu/secskills/analyzing-network-traffic"><img src="https://agentmods.dev/badge/skills/trilwu/secskills/analyzing-network-traffic.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Prompt Injection · line 402 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
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 | $0.00146 | $0.05651 |
| Opus 5 | $0.00073 | $0.02825 |
| Sonnet 5 | $0.00029 | $0.01130 |
| Haiku 4.5 | $0.00015 | $0.00565 |
Grade A, and why
analyzing-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 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- analyzing-network-traffic — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 444 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyzing Network Traffic
Packet capture is ground truth the endpoint can lie about but the wire cannot: every connection, DNS lookup, and byte transferred is recorded, whether or not the host's logs survived. The analysis is turning a flat capture into a story — who talked to whom, over what protocol, whether the pattern was human or automated, and what left the network. You are reconstructing intent from frames, not reading a verdict off a tool.
When to Use
- A
.pcap/.pcapngcapture needs forensic review for intrusion evidence - A suspected C2 beacon must be confirmed and its interval, jitter, and channel characterized
- Data exfiltration is suspected and you need to size it, time it, and name the destination
- Malware network behaviour must be documented from the traffic it actually emitted
- A Zeek
notice.logor Suricataeve.jsonalert needs to be run down to a verdict - DNS tunneling, a DGA, or anomalous TLS (odd certs, rare JA3, SNI mismatch) is suspected
When NOT to Use
- Proactively sweeping endpoint, network, cloud, and identity telemetry for
undetected compromise — use
hunting-threats; this skill dissects one capture, a hunt spans data sources - Running the wider incident — use
responding-to-incidents; traffic analysis is one evidence stream feeding that process - Detonating a sample to produce the traffic — use
analyzing-malware; come here to analyze the pcap it emitted, not to run the binary - The evidence is cloud control-plane activity, not packets (CloudTrail,
VPC flow gaps, API calls) — use
investigating-aws-incidents - Actively testing a live application rather than analyzing a capture of
it — use
testing-web-applications
Capture and Handling
Get the capture right or every later step inherits the gap. A truncated snaplen or a dropped-packet capture cannot be fixed after the fact.
# Full-frame capture, no name resolution, write to disk (never parse live)
tcpdump -i eth0 -nn -s 0 -w case.pcap
# -s 0 takes full frames; a default snaplen truncates payloads and breaks carving
# Ring buffer for long-running capture: 20 files of 200 MB, oldest recycled
tcpdump -i eth0 -nn -s 0 -w case-%Y%m%d-%H%M%S.pcap -G 3600 -C 200 -W 20
# Capture without dropping under load: raise the kernel buffer, filter tightly
tcpdump -i eth0 -nn -s 0 -B 4096 'not port 22' -w case.pcap
# Confirm drops after: the summary line reports "packets dropped by kernel"
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 · 444 lines · 146 tokens per session scan A c4c0992885b5
analyzing-network-traffic is a skill published in the GitHub repository trilwu/secskills (137 stars, last pushed 4d ago), licensed MIT. It adds 146 tokens to every session and 5,651 once invoked, about $0.0007 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.
Other skills, from other repositories
claude-md-improver
Audit and improve CLAUDE.md files in repositories. Use when user asks to check, audit, update, improve, or fix CLAUDE.md files. Scans for all CLAUDE.md files, evaluates quality against templates, outputs quality report, then makes targeted updates. Also use when the user mentions "CLAUDE.md maintenance" or "project…
agent-platform-rag-engine-management
Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google…
gke-workload-security
Audits, configures, and hardens workload-level security controls for Google Kubernetes Engine (GKE) applications and namespaces. Covers running cluster security audits (auditcluster.sh), configuring Workload Identity Federation (impersonation, KSA/GSA binding, and pod setup), enforcing Network Policies (default-deny…
gke-reliability
Improves GKE workload reliability, using PDBs, health probes, and topology spread constraints. Use when configuring GKE workload reliability, setting up PDBs, or configuring GKE health probes (liveness, readiness, startup). Don't use for disaster recovery setup or full cluster backups (use gke-backup-dr instead).
agent-platform-model-registry
Agent Platform Model Registry Management. Use when you need to upload, list, describe, update, or delete machine learning models (and their versions) in the Agent Platform Model Registry. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform models.
google-cloud-solution-agentic-analytics-spark-knowledge-catalog
Discovers requirements and generates guidance to design and deploy a governed, secure agentic-analytics solution for data that's distributed across Google Cloud, other cloud providers, or on-premises. Data that's outside Google Cloud (such as data from Databricks, Snowflake, Salesforce, SAP, or Oracle systems) is…