performing-network-traffic-analysis-with-tshark▌
mukul975/Anthropic-Cybersecurity-Skills · updated May 25, 2026
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Automate network traffic analysis using tshark and pyshark for protocol statistics, suspicious flow detection, DNS anomaly identification, and IOC extraction from PCAP files
| name | performing-network-traffic-analysis-with-tshark |
| description | Automate network traffic analysis using tshark and pyshark for protocol statistics, suspicious flow detection, DNS anomaly identification, and IOC extraction from PCAP files |
| domain | cybersecurity |
| subdomain | network-security |
| tags | - tshark - pyshark - pcap - packet-analysis - network-forensics - wireshark - traffic-analysis |
| version | '1.0' |
| author | mahipal |
| license | Apache-2.0 |
| nist_csf | - PR.IR-01 - DE.CM-01 - ID.AM-03 - PR.DS-02 |
Performing Network Traffic Analysis with TShark
Overview
This skill automates packet capture analysis using tshark (Wireshark CLI) and pyshark (Python wrapper). It extracts protocol distribution statistics, identifies suspicious network flows (port scans, beaconing, data exfiltration), extracts IOCs (IPs, domains, URLs), and detects DNS tunneling patterns from PCAP files.
When to Use
- When conducting security assessments that involve performing network traffic analysis with tshark
- When following incident response procedures for related security events
- When performing scheduled security testing or auditing activities
- When validating security controls through hands-on testing
Prerequisites
- tshark (Wireshark CLI) installed and in PATH
- Python 3.8+ with pyshark library
- PCAP or PCAPNG capture file for analysis
Steps
- Extract Protocol Statistics — Generate protocol hierarchy and conversation statistics from the capture
- Identify Top Talkers — Rank source/destination IPs by volume and connection count
- Detect Suspicious Flows — Flag port scanning patterns, unusual port usage, and high-frequency connections
- Extract Network IOCs — Pull unique IPs, domains from DNS queries, and URLs from HTTP traffic
- Analyze DNS Traffic — Detect DNS tunneling via high-entropy subdomain queries and excessive TXT records
- Generate Analysis Report — Produce structured report with flow summaries and threat indicators
Expected Output
- JSON report with protocol statistics and top talkers
- Suspicious flow detections with severity ratings
- Extracted IOCs (IPs, domains, URLs)
- DNS anomaly analysis results
How to use performing-network-traffic-analysis-with-tshark on Cursor
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Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add performing-network-traffic-analysis-with-tshark
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches performing-network-traffic-analysis-with-tshark from GitHub repository mukul975/Anthropic-Cybersecurity-Skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate performing-network-traffic-analysis-with-tshark. Access the skill through slash commands (e.g., /performing-network-traffic-analysis-with-tshark) or your agent's skill management interface.
Security & Verification Notice
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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
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Use Cases▌
Exploratory Data Analysis
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
Data Cleaning & Transformation
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
Statistical Analysis
Perform hypothesis testing, regression, and statistical modeling
Example
Run A/B test analysis, calculate confidence intervals, interpret p-values
Get statistically sound analysis without PhD in statistics
Data Visualization
Create charts, dashboards, and visual reports
Example
Generate matplotlib/seaborn code for time series plots, distribution charts, heatmaps
Build presentation-ready visualizations 3x faster
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Python environment (pandas, numpy, matplotlib) or SQL database access
- ›Basic understanding of data analysis concepts
- ›Sample datasets for testing skill capabilities
Time Estimate
20-40 minutes to set up and run first analysis
Installation Steps
- 1.Install data analysis skill using provided command
- 2.Prepare a sample dataset (CSV, JSON, or database connection)
- 3.Start with descriptive statistics: 'Summarize this dataset'
- 4.Progress to visualization: 'Create a scatter plot of X vs Y'
- 5.Advanced analysis: 'Run linear regression and interpret results'
- 6.Validate outputs: check calculations, verify visualizations make sense
- 7.Document analysis workflow for reproducibility
Common Pitfalls
- ⚠Not validating statistical assumptions before applying tests
- ⚠Accepting visualizations without checking data accuracy
- ⚠Overlooking data quality issues (missing values, outliers)
- ⚠Misinterpreting correlation as causation
- ⚠Using wrong statistical test for data distribution
- ⚠Not considering sample size and statistical power
Best Practices▌
✓ Do
- +Always validate data quality before analysis
- +Check statistical assumptions (normality, independence, etc.)
- +Visualize data before running statistical tests
- +Document analysis steps for reproducibility
- +Cross-validate findings with domain experts
- +Use skill for initial exploration, then dive deeper manually
- +Save generated code for reuse on similar datasets
✗ Don't
- −Don't trust analysis without verifying data quality
- −Don't apply statistical tests without checking assumptions
- −Don't make business decisions solely on AI-generated analysis
- −Don't ignore outliers without investigating cause
- −Don't skip data validation and sanity checks
- −Don't use for mission-critical financial or medical analysis without expert review
💡 Pro Tips
- ★Describe data context: 'This is user behavior data from e-commerce site'
- ★Ask for interpretation: 'What does this correlation mean for business?'
- ★Request multiple approaches: 'Show 3 ways to handle missing data'
- ★Combine AI analysis with domain expertise for best insights
- ★Use for rapid prototyping, then refine analysis manually
When to Use This▌
✓ 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.
Learning Path▌
- 1Basic: descriptive statistics, data cleaning, simple visualizations
- 2Intermediate: hypothesis testing, regression, correlation analysis
- 3Advanced: time series analysis, clustering, predictive modeling
- 4Expert: causal inference, experimental design, advanced statistical methods
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.8★★★★★30 reviews- ★★★★★Shikha Mishra· Dec 28, 2024
Useful defaults in performing-network-traffic-analysis-with-tshark — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Noah Diallo· Dec 28, 2024
I recommend performing-network-traffic-analysis-with-tshark for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Ganesh Mohane· Dec 8, 2024
performing-network-traffic-analysis-with-tshark fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Harper Sethi· Dec 4, 2024
Keeps context tight: performing-network-traffic-analysis-with-tshark is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Michael Bansal· Nov 23, 2024
Registry listing for performing-network-traffic-analysis-with-tshark matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Yash Thakker· Nov 19, 2024
performing-network-traffic-analysis-with-tshark has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Li Smith· Nov 19, 2024
Solid pick for teams standardizing on skills: performing-network-traffic-analysis-with-tshark is focused, and the summary matches what you get after install.
- ★★★★★Hiroshi Li· Oct 14, 2024
performing-network-traffic-analysis-with-tshark reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Dhruvi Jain· Oct 10, 2024
Solid pick for teams standardizing on skills: performing-network-traffic-analysis-with-tshark is focused, and the summary matches what you get after install.
- ★★★★★Li Martinez· Oct 10, 2024
performing-network-traffic-analysis-with-tshark has been reliable in day-to-day use. Documentation quality is above average for community skills.
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