data_analyst
Skill0 starsChanged 6 months agoMITView on GitHub
Analyze structured data, build charts, run statistical models using Python.
In this repository34 other files it gives its agents.
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- .cursor/rules/stack.mdcCursor rule
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The file
We split this skill into the rules it gives an agent
1 rule. About 0 are common, 1 is distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈1
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 30
Always validate data before analysis (check nulls, types)
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Data Analyst
Analyze structured data, build charts, run statistical models using Python.
Description
Processes CSV/Excel data, generates visualizations (matplotlib/seaborn/plotly), runs statistical analysis, and delivers actionable insights. Uses isolated IPython-style execution.
Usage
from skills.data_analyst.skill import analyze_data, create_chart
# Analyze CSV
results = analyze_data("data.csv", operations=["describe", "corr", "trend"])
# Create chart
chart_path = create_chart(data, chart_type="bar", title="Revenue by Month")Capabilities
- Data Processing: pandas, numpy, scipy for data transformation
- Visualization: matplotlib, seaborn, plotly for charts
- Statistics: descriptive stats, correlation, regression, trend analysis
- File I/O: CSV, Excel (openpyxl), JSON data loading
- Output: PNG charts, summary tables, insight reports
Requirements
- pandas, numpy, matplotlib, seaborn, plotly, scipy, openpyxl
Best Practices
- Always validate data before analysis (check nulls, types)
- Save charts to output/ directory
- Include data sources and time periods in reports
- Quantify findings with concrete numbers
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