data-analysis

name	data-analysis
description	Analyze data with pandas, generate visualizations with matplotlib, and return results as downloadable files
enabled	true

Instructions

When the user provides data (inline, as a file, or asks you to generate sample data) and wants analysis, follow this workflow:

1. Data Ingestion

• If the user provides a CSV, JSON, or other structured data inline, write it to /tmp first using code_interpreter.
• If the user references a file already in /tmp, read it directly.
• If no data is available, offer to generate realistic sample data for demonstration.

2. Exploratory Analysis

Before diving into specific questions, give the user a quick overview:

import pandas as pd

df = pd.read_csv("/tmp/data.csv")
print(f"Shape: {df.shape}")
print(f"\nColumns: {list(df.columns)}")
print(f"\nData types:\n{df.dtypes}")
print(f"\nFirst 5 rows:\n{df.head()}")
print(f"\nSummary statistics:\n{df.describe()}")
print(f"\nMissing values:\n{df.isnull().sum()}")

3. Analysis Patterns

Use the code_interpreter skill with pandas for:

• Aggregations: groupby, pivot tables, rolling windows
• Filtering: conditional selection, top-N, outlier detection
• Transformations: calculated columns, date parsing, normalization
• Statistical tests: correlation, distribution analysis

4. Visualizations

Generate charts with matplotlib and save to /tmp:

import matplotlib
matplotlib.use("Agg")  # Non-interactive backend — required
import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=(10, 6))
# ... plot logic ...
plt.tight_layout()
plt.savefig("/tmp/chart.png", dpi=150)
plt.close()
print("Chart saved: /tmp/chart.png")

Always:

• Use matplotlib.use("Agg") before importing pyplot (headless environment).
• Save to /tmp/ with a descriptive name (e.g. /tmp/sales_by_region.png).
• Call plt.close() after saving to free memory.
• Include a print() with the file path so file-sharing can pick it up.

Common chart types:

• Bar charts for category comparisons
• Line charts for time series / trends
• Scatter plots for correlations
• Histograms for distributions
• Heatmaps for correlation matrices
• Pie charts only when there are ≤6 categories

5. Output

• Print key findings as text in code_interpreter stdout.
• Save any generated files (charts, processed CSVs) to /tmp.
• Reference file paths in your response so the user can download them via the file-sharing capability.
• If the analysis produces a transformed dataset, save it as /tmp/<descriptive_name>.csv.

6. Chaining

This skill works best when combined with:

• code_interpreter — runs the actual Python code
• file-sharing — delivers charts and processed files to the user
• rag_search — retrieves internal data or context before analysis

Constraints

• Max execution time: 30 seconds per code block
• Pre-installed libraries: pandas, numpy, matplotlib. Additional libraries can be installed at runtime via pip.
• Files must be written to /tmp
• Keep DataFrames under ~1M rows for responsive performance

Wealth Management Analysis Patterns

When analyzing financial or portfolio data, consider these domain-specific techniques.

Note: For client-specific portfolio reviews (where the user references a client by name or ID), prefer the portfolio-review skill which integrates directly with the CRM and provides a structured review format. Use data-analysis for:

• General-purpose financial calculations not tied to a specific CRM client
• Ad-hoc quantitative analysis requested by the user
• Data the user provides directly (CSV, inline, uploaded file)
• Advanced statistical techniques beyond what portfolio-review covers

Techniques:

• Risk-adjusted returns: Sharpe ratio = (portfolio_return - risk_free_rate) / portfolio_std_dev
• Alpha / Beta: Compute portfolio beta against benchmark; alpha = actual_return - (risk_free + beta × (benchmark_return - risk_free))
• Sector/asset class attribution: Break down returns by sector contribution
• Correlation matrix: Cross-asset correlations using a heatmap
• Drawdown analysis: Maximum peak-to-trough decline
• Monte Carlo simulation: Use numpy.random for forward-looking return distributions (always disclaim as illustrative)
• Stress testing: Apply historical scenarios (e.g., 2008 GFC, 2020 COVID) to current holdings

Example

User: "Analyze this sales data and show me a monthly trend chart"

Steps:

1. Use code_interpreter to load the data with pandas
2. Compute monthly aggregations
3. Generate a line chart with matplotlib, save to /tmp/monthly_sales_trend.png
4. Print summary statistics
5. Reply with findings and the chart file path