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 periodic timing patterns.
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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionanalyzing-network-flow-data-with-netflowExecute the skills CLI command in your project's root directory to begin installation:
Fetches analyzing-network-flow-data-with-netflow from mukul975/Anthropic-Cybersecurity-Skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate analyzing-network-flow-data-with-netflow. Access via /analyzing-network-flow-data-with-netflow in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Quickly understand datasets, identify patterns, and generate insights
Example
Analyze CSV with 100K rows, identify outliers, visualize correlations, suggest hypotheses
Reduce EDA time from hours to minutes, uncover insights faster
Write scripts to clean messy data, handle missing values, normalize formats
Example
Generate Python/SQL to fix date formats, impute missing values, remove duplicates
Automate 80% of data preprocessing work
Perform hypothesis testing, regression, and statistical modeling
Example
Run A/B test analysis, calculate confidence intervals, interpret p-values
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| name | analyzing-network-flow-data-with-netflow |
| description | 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 periodic timing patterns. |
| domain | cybersecurity |
| subdomain | network-security |
| tags | - analyzing - network - flow - data |
| version | '1.0' |
| author | mahipal |
| license | Apache-2.0 |
| nist_csf | - PR.IR-01 - DE.CM-01 - ID.AM-03 - PR.DS-02 |
pip install netflowpython -m netflow.collector -p 9995netflow.parse_packet().python scripts/agent.py --flow-file captured_flows.json --output netflow_report.json
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)
Get statistically sound analysis without PhD in statistics
Create charts, dashboards, and visual reports
Example
Generate matplotlib/seaborn code for time series plots, distribution charts, heatmaps
Build presentation-ready visualizations 3x faster
Prerequisites
Time Estimate
20-40 minutes to set up and run first analysis
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for exploratory data analysis, data cleaning, statistical testing, visualization prototyping, and learning new analysis techniques. Best for initial exploration and rapid insights.
✗ Avoid when
Avoid for mission-critical financial analysis, medical research requiring regulatory compliance, production ML models, or when deep statistical expertise is required for nuanced interpretation.
mukul975/Anthropic-Cybersecurity-Skills
mukul975/Anthropic-Cybersecurity-Skills
mukul975/Anthropic-Cybersecurity-Skills
mukul975/Anthropic-Cybersecurity-Skills
mukul975/Anthropic-Cybersecurity-Skills
mukul975/Anthropic-Cybersecurity-Skills
analyzing-network-flow-data-with-netflow fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Solid pick for teams standardizing on skills: analyzing-network-flow-data-with-netflow is focused, and the summary matches what you get after install.
We added analyzing-network-flow-data-with-netflow from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Keeps context tight: analyzing-network-flow-data-with-netflow is the kind of skill you can hand to a new teammate without a long onboarding doc.
analyzing-network-flow-data-with-netflow fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
analyzing-network-flow-data-with-netflow is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Keeps context tight: analyzing-network-flow-data-with-netflow is the kind of skill you can hand to a new teammate without a long onboarding doc.
analyzing-network-flow-data-with-netflow is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
We added analyzing-network-flow-data-with-netflow from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Registry listing for analyzing-network-flow-data-with-netflow matched our evaluation — installs cleanly and behaves as described in the markdown.
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