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 Mikaru0Mystic/sectinel --skill analyzing-network-flow-data-with-netflowgit clone --depth 1 https://github.com/Mikaru0Mystic/sectinelWrote 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/mikaru0mystic/sectinel/analyzing-network-flow-data-with-netflow)<a href="https://agentmods.dev/skills/mikaru0mystic/sectinel/analyzing-network-flow-data-with-netflow"><img src="https://agentmods.dev/badge/skills/mikaru0mystic/sectinel/analyzing-network-flow-data-with-netflow/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/mikaru0mystic/sectinel/analyzing-network-flow-data-with-netflow"><img src="https://agentmods.dev/badge/skills/mikaru0mystic/sectinel/analyzing-network-flow-data-with-netflow.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 | $0.00076 | $0.00489 |
| Opus 5 | $0.00038 | $0.00244 |
| Sonnet 5 | $0.00015 | $0.00098 |
| Haiku 4.5 | $0.00008 | $0.00049 |
Grade A, and why
analyzing-network-flow-data-with-netflow 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.
This is a copy
94% identical to analyzing-network-flow-data-with-netflow — 13 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.
What it actually says
Analyzing Network Flow Data with Netflow
When to Use
- When investigating security incidents that require analyzing network flow data with netflow
- When building detection rules or threat hunting queries for this domain
- When SOC analysts need structured procedures for this analysis type
- When validating security monitoring coverage for related attack techniques
Prerequisites
- Familiarity with network security concepts and tools
- Access to a test or lab environment for safe execution
- Python 3.8+ with required dependencies installed
- Appropriate authorization for any testing activities
Instructions
- Install dependencies:
pip install netflow - Collect NetFlow/IPFIX data from routers or use the built-in collector:
python -m netflow.collector -p 9995 - Parse captured flow data using
netflow.parse_packet(). - Analyze flows for:
- Port scanning: single source to many destinations on same port
- Data exfiltration: high byte-count outbound flows to unusual destinations
- C2 beaconing: periodic connections with consistent intervals
- Volumetric anomalies: traffic spikes beyond baseline thresholds
- Generate a prioritized findings report.
python scripts/agent.py --flow-file captured_flows.json --output netflow_report.json
Examples
Parse NetFlow v9 Packet
import netflow
data, _ = netflow.parse_packet(raw_bytes, templates={})
for flow in data.flows:
print(flow.IPV4_SRC_ADDR, flow.IPV4_DST_ADDR, flow.IN_BYTES)
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 · 66 lines · 76 tokens per session scan A 8043aa1a977a
analyzing-network-flow-data-with-netflow is a skill published in the GitHub repository Mikaru0Mystic/sectinel (11 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 76 tokens to every session and 489 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 94% identical to analyzing-network-flow-data-with-netflow, differing in 13 lines, and is treated as a copy.
Other skills, from other repositories
analyzing-network-flow-data-with-netflow
Parse NetFlow v9 and IPFIX records to detect volumetric anomalies, port scanning, data exfiltration, and C2 beaconing patterns. Uses the Python netflow library to decode flow records, builds traffic baselines, and applies statistical analysis to identify flows with abnormal byte counts, connection durations, and…
analyzing-network-flow-data-with-netflow
Parse NetFlow v9 and IPFIX records to detect volumetric anomalies, port scanning, data exfiltration, and C2 beaconing patterns. Uses the Python netflow library to decode flow records, builds traffic baselines, and applies statistical analysis to identify flows with abnormal byte counts, connection durations, and…
analyzing-network-flow-data-with-netflow
Parse NetFlow v9 and IPFIX records to detect volumetric anomalies, port scanning, data exfiltration, and C2 beaconing patterns. Uses the Python netflow library to decode flow records, builds traffic baselines, and applies statistical analysis to identify flows with abnormal byte counts, connection durations, and…
analyzing-network-flow-data-with-netflow
A guide to reading NetFlow v9 and IPFIX records, which summarise network connections without storing every packet. It uses Python analysis to identify unusual traffic patterns.
analyzing-network-flow-data-with-netflow
Parse NetFlow v9 and IPFIX records to detect volumetric anomalies, port scanning, data exfiltration, and C2 beaconing patterns. Uses the Python netflow library to decode flow records, builds traffic baselines, and applies statistical analysis to identify flows with abnormal byte counts, connection durations, and…
analyzing-network-flow-data-with-netflow
Parse NetFlow v9 and IPFIX records to detect volumetric anomalies, port scanning, data exfiltration, and C2 beaconing patterns. Uses the Python netflow library to decode flow records, builds traffic baselines, and applies statistical analysis to identify flows with abnormal byte counts, connection durations, and…