csv-analyzer

name	csv-analyzer
description	USE WHEN a user shares or references a CSV file and wants it summarized, analyzed, or visualized — produces data overview, summary statistics, missing-data report, and relevant charts using built-in tools (Read + Bash running Python/pandas).

CSV Analyzer

This Skill analyzes CSV files and provides comprehensive summaries with statistical insights and visualizations, using your built-in tools — no bundled scripts required.

When to Use This Skill

Use this Skill whenever the user:

• Uploads or references a CSV file
• Asks to summarize, analyze, or visualize tabular data
• Requests insights from CSV data
• Wants to understand data structure and quality

How It Works

Default behavior

By default, run the analysis directly — don't make the user pick from a menu of options first. Read the file, inspect its structure, and present a complete summary with relevant charts.

The one exception: if the data is genuinely ambiguous (e.g., unclear what a column means, multiple plausible date formats, or it's unclear whether a value is the metric of interest), ask a single short clarifying question, then proceed. This keeps the skill fast while staying consistent with muselab's "ask when ambiguous" stance.

How to run the analysis

Use the built-in tools directly — there is no separate script to call:

• Use Read to peek at the first rows and understand the columns.
• Use Bash to run inline Python (pandas, and matplotlib/seaborn if available) for stats and charts.

A typical inline analysis looks like:

python3 - <<'PY'
import pandas as pd
df = pd.read_csv("data.csv")
print(df.shape)
print(df.dtypes)
print(df.describe(include="all"))
print(df.isna().sum())
PY

For charts, save figures to PNG files (e.g. plt.savefig("trend.png")) and reference them in your summary.

Analysis steps

1. Load and inspect the CSV file into a pandas DataFrame

2. Identify data structure - column types, date columns, numeric columns, categories

3. Determine relevant analyses based on what's actually in the data:

   • Sales/E-commerce data (order dates, revenue, products): Time-series trends, revenue analysis, product performance
   • Customer data (demographics, segments, regions): Distribution analysis, segmentation, geographic patterns
   • Financial data (transactions, amounts, dates): Trend analysis, statistical summaries, correlations
   • Operational data (timestamps, metrics, status): Time-series, performance metrics, distributions
   • Survey data (categorical responses, ratings): Frequency analysis, cross-tabulations, distributions
   • Generic tabular data: Adapts based on column types found

4. Only create visualizations that make sense for the specific dataset:

   • Time-series plots only if a date/timestamp column exists
   • Correlation heatmaps only if multiple numeric columns exist
   • Category distributions only if categorical columns exist
   • Histograms for numeric distributions when relevant

5. Generate comprehensive output automatically including:

   • Data overview (rows, columns, types)
   • Key statistics and metrics relevant to the data type
   • Missing data analysis
   • Multiple relevant visualizations (only those that apply)
   • Actionable insights based on patterns found in THIS specific dataset

6. Present everything in one complete analysis - no follow-up questions

Example adaptations:

• Healthcare data with patient IDs → Focus on demographics, treatment patterns, temporal trends
• Inventory data with stock levels → Focus on quantity distributions, reorder patterns, SKU analysis
• Web analytics with timestamps → Focus on traffic patterns, conversion metrics, time-of-day analysis
• Survey responses → Focus on response distributions, demographic breakdowns, sentiment patterns

Behavior Guidelines

Do:

• Lead with the analysis itself rather than a menu of options
• Generate the relevant charts automatically
• Be thorough and complete in the first response
• Save charts to files and reference them in the summary

Avoid:

• Stalling with "What would you like me to do?" when the intent is clearly "analyze this"
• Listing options for the user to choose from instead of just analyzing
• Providing a partial analysis that forces a follow-up

Ask first only when: the data is genuinely ambiguous (unclear column meaning, ambiguous date format, or unclear target metric) — then ask one short question and proceed.

Example Prompts

"Here's sales_data.csv. Can you summarize this file?"

"Analyze this customer data CSV and show me trends."

"What insights can you find in orders.csv?"

Example Output

Dataset Overview

• 5,000 rows × 8 columns
• 3 numeric columns, 1 date column

Summary Statistics

• Average order value: $58.2
• Standard deviation: $12.4
• Missing values: 2% (100 cells)

Insights

• Sales show upward trend over time
• Peak activity in Q4 (Attached: trend plot)

Dependencies

Uses your built-in Read and Bash tools. The inline Python relies on:

• pandas (required) — pip install pandas
• matplotlib / seaborn (optional, for charts) — pip install matplotlib seaborn

If a charting library is missing, still produce the statistical summary and note that charts were skipped.

Notes

• Detects date columns by dtype and by name (columns containing 'date'/'time'); parse with pd.to_datetime when needed
• Handles missing data gracefully and reports it explicitly
• Produces a chart only when the data supports it — time-series needs a date column, correlation heatmaps need multiple numeric columns, etc.
• All numeric columns are included in the statistical summary