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 marysatasselshaped667/skills-collection-1 --skill analyzing-network-flow-data-with-netflowgit clone --depth 1 https://github.com/marysatasselshaped667/skills-collection-1Wrote 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/marysatasselshaped667/skills-collection-1/analyzing-network-flow-data-with-netflow)<a href="https://agentmods.dev/skills/marysatasselshaped667/skills-collection-1/analyzing-network-flow-data-with-netflow"><img src="https://agentmods.dev/badge/skills/marysatasselshaped667/skills-collection-1/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/marysatasselshaped667/skills-collection-1/analyzing-network-flow-data-with-netflow"><img src="https://agentmods.dev/badge/skills/marysatasselshaped667/skills-collection-1/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 | $0.00462 |
| Opus 5 | $0.00038 | $0.00231 |
| Sonnet 5 | $0.00015 | $0.00092 |
| Haiku 4.5 | $0.00008 | $0.00046 |
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 8d 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
91% identical to analyzing-network-flow-data-with-netflow — 28 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.
- 8d ago First seen · 59 lines · 76 tokens per session scan A b989859b4416
analyzing-network-flow-data-with-netflow is a skill published in the GitHub repository marysatasselshaped667/skills-collection-1 (1 stars, last pushed today), licensed MIT. It adds 76 tokens to every session and 462 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to analyzing-network-flow-data-with-netflow, differing in 28 lines, and is treated as a copy.
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