femto/minion/data-analysis
Claude Code and agent skill by femto/minion
Trust tier 2
- community
Source check pending
Checking that the SKILL.md file is still in the repository.
Analyze datasets and create visualizations
Install
npx vskill@latest install femto/minion/data-analysisSKILL.md
Data Analysis Skill
Description
This skill helps analyze datasets and create meaningful visualizations. It can handle CSV files, perform statistical analysis, and generate various types of plots.
Usage Instructions
When a user requests data analysis:
- Load the dataset: Use pandas to read the data file
- Inspect the data: Check shape, columns, data types, and basic statistics
- Clean the data: Handle missing values and outliers if necessary
- Perform analysis: Calculate relevant statistics based on user's question
- Create visualizations: Generate appropriate plots (line, bar, scatter, etc.)
- Save results: Export results and visualizations
Available Resources
Scripts
- scripts/analyze.py: Core analysis functions
load_dataset(filepath): Load data from various formatsbasic_statistics(df): Calculate descriptive statisticsdetect_outliers(df, column): Identify outlierscorrelation_analysis(df): Compute correlations
- scripts/visualize.py: Visualization utilities
plot_distribution(df, column): Create distribution plotsplot_correlation_matrix(df): Visualize correlation heatmapplot_time_series(df, date_col, value_col): Time series plotssave_plot(fig, filename): Save figure to file
References
- references/examples.md: Usage examples and common patterns
- references/best_practices.md: Data analysis best practices
Example Prompts
- "Analyze this CSV file and show me the trends"
- "Create a visualization of the sales data by month"
- "Find correlations in this dataset"
- "Identify outliers in the price column"
- "Generate a statistical summary of the data"
Output Format
Analysis results should include:
- Data overview (shape, columns, types)
- Statistical summary
- Key insights and findings
- Visualizations (saved as PNG files)
- Recommendations or next steps
Notes
- Always inspect data before analysis
- Handle missing values appropriately
- Choose visualizations that match the data type
- Provide clear explanations of findings
- Save all outputs for user reference
Security
- Security scanPassed, 100 of 100Pattern checks on the skill's files.
- Model reviewPassed
Quality evaluation
- Task evaluationNot runNo evaluation yet. Run one in Skill Studio.
Source
- SKILL.md in the repositoryCheckingexamples/skills/data-analysis/SKILL.md
Popularity
- Repository stars
- 150
- Repository forks
- 22
- 7-day trend score
- 25
Stars and forks are for the whole GitHub repository, not this skill alone. Refreshed 18 Mar 2026, 7 months ago.
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