data-analysis
Load, analyze, and visualize datasets using pandas with AG Grid display
Best use case
data-analysis is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Load, analyze, and visualize datasets using pandas with AG Grid display
Teams using data-analysis should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/prismer-data-analysis/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How data-analysis Compares
| Feature / Agent | data-analysis | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Load, analyze, and visualize datasets using pandas with AG Grid display
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Data Analysis Skill
## Description
Load data files (CSV, XLSX, JSON, Parquet) into the AG Grid viewer, run pandas queries, save results, and generate visualizations.
## Tools Used
### Primary (Data Grid workflow)
- `data_list` - List available data files in /workspace/data/
- `data_load` - Load a data file into AG Grid (returns markdown preview for context)
- `data_query` - Execute pandas operations on loaded data (filter, aggregate, transform)
- `data_save` - Save the current DataFrame to a file
### Secondary (Jupyter workflow for visualization)
- `jupyter_execute` - Execute Python code in Jupyter kernel (for plots and complex analysis)
- `update_notebook` - Add cells to Jupyter notebook
- `update_gallery` - Display generated plots in the gallery
## Workflow
### Recommended: Data Grid Workflow
For tabular data exploration, use the data tools which provide a spreadsheet-like experience:
1. **List files**: `data_list` to see what's in /workspace/data/
2. **Load data**: `data_load` to read a file and display in AG Grid
- You'll receive a markdown preview to understand columns and types
3. **Query/Filter**: `data_query` to run pandas operations
- The `df` variable contains the loaded data
- Set `result = ...` to define output
4. **Save results**: `data_save` to export to CSV/XLSX
### Alternative: Jupyter Workflow
For visualization, statistical analysis, or ML, use Jupyter tools:
1. Load data with `jupyter_execute` running pandas code
2. Create visualizations with matplotlib/seaborn
3. Display plots with `update_gallery`
## Usage Patterns
### Load and Explore Data
When user says: "Analyze this dataset" or "Show me the data"
1. `data_list` to find available files
2. `data_load` with the target file
3. Review the markdown preview to understand structure
4. `data_query` with `result = df.describe()` for statistics
5. Offer filtering, sorting, or visualization
### Filter and Transform
When user says: "Show only rows where X > Y" or "Group by category"
1. `data_query` with pandas filter/groupby code
2. Grid updates automatically with filtered results
3. Inform user of result count and preview
### Save Processed Data
When user says: "Export this" or "Save as Excel"
1. `data_save` with desired filename and format
2. Report file location and size
### Visualize Data
When user says: "Create a chart" or "Plot the distribution"
1. Use `jupyter_execute` with matplotlib/seaborn code
2. Save plot and display via `update_gallery`
## Code Snippets for data_query
### Filter rows
```python
result = df[df['score'] > 90]
```
### Group and aggregate
```python
result = df.groupby('category').agg({'value': ['mean', 'sum', 'count']}).reset_index()
```
### Sort by column
```python
result = df.sort_values('date', ascending=False)
```
### Add computed column
```python
df['ratio'] = df['value_a'] / df['value_b']
result = df
```
### Summary statistics
```python
result = df.describe()
```
### Handle missing values
```python
result = df.dropna(subset=['important_column'])
```
## Best Practices
1. **Start with data_list**: Always check what files are available first
2. **Use data_load first**: Load data to get markdown preview before querying
3. **Keep queries simple**: One operation per data_query call for clarity
4. **Save intermediate results**: Use data_save for important filtered datasets
5. **Switch to Jupyter for plots**: AG Grid is for tabular data, use Jupyter for visualizationsRelated Skills
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