CSV Data Summarizer
This Skill analyzes CSV files and provides comprehensive summaries with statistical insights and visualizations.
When to Use This Skill
Claude should 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
⚠️ CRITICAL BEHAVIOR REQUIREMENT ⚠️
DO NOT ASK THE USER WHAT THEY WANT TO DO WITH THE DATA.
DO NOT OFFER OPTIONS OR CHOICES.
DO NOT SAY "What would you like me to help you with?"
DO NOT LIST POSSIBLE ANALYSES.
IMMEDIATELY AND AUTOMATICALLY:
- Run the comprehensive analysis
- Generate ALL relevant visualizations
- Present complete results
- NO questions, NO options, NO waiting for user input
THE USER WANTS A FULL ANALYSIS RIGHT AWAY - JUST DO IT.
Automatic Analysis Steps:
The skill intelligently adapts to different data types and industries by inspecting the data first, then determining what analyses are most relevant.
Load and inspect the CSV file into pandas DataFrame
Identify data structure - column types, date columns, numeric columns, categories
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
Only create visualizations that make sense for the specific dataset:
- Time-series plots ONLY if date/timestamp columns exist
- Correlation heatmaps ONLY if multiple numeric columns exist
- Category distributions ONLY if categorical columns exist
- Histograms for numeric distributions when relevant
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
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
✅ CORRECT APPROACH - SAY THIS:
- "I'll analyze this data comprehensively right now."
- "Here's the complete analysis with visualizations:"
- "I've identified this as [type] data and generated relevant insights:"
- Then IMMEDIATELY show the full analysis
✅ DO:
- Immediately run the analysis script
- Generate ALL relevant charts automatically
- Provide complete insights without being asked
- Be thorough and complete in first response
- Act decisively without asking permission
❌ NEVER SAY THESE PHRASES:
- "What would you like to do with this data?"
- "What would you like me to help you with?"
- "Here are some common options:"
- "Let me know what you'd like help with"
- "I can create a comprehensive analysis if you'd like!"
- Any sentence ending with "?" asking for user direction
- Any list of options or choices
- Any conditional "I can do X if you want"
❌ FORBIDDEN BEHAVIORS:
- Asking what the user wants
- Listing options for the user to choose from
- Waiting for user direction before analyzing
- Providing partial analysis that requires follow-up
- Describing what you COULD do instead of DOING it
Usage
The Skill provides a Python function summarize_csv(file_path) that:
- Accepts a path to a CSV file
- Returns a comprehensive text summary with statistics
- Generates multiple visualizations automatically based on data structure
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)
Files
analyze.py - Core analysis logicrequirements.txt - Python dependenciesresources/sample.csv - Example dataset for testingresources/README.md - Additional documentation
Notes
- Automatically detects date columns (columns containing 'date' in name)
- Handles missing data gracefully
- Generates visualizations only when date columns are present
- All numeric columns are included in statistical summary
1---2name: csv-data-summarizer3description: Analyzes CSV files, generates summary stats, and plots quick visualizations using Python and pandas.4---5
6# CSV Data Summarizer
7
8This Skill analyzes CSV files and provides comprehensive summaries with statistical insights and visualizations.
9
10## When to Use This Skill
11
12Claude should use this Skill whenever the user:
13- Uploads or references a CSV file
14- Asks to summarize, analyze, or visualize tabular data
15- Requests insights from CSV data
16- Wants to understand data structure and quality
17
18## How It Works
19
20## ⚠️ CRITICAL BEHAVIOR REQUIREMENT ⚠️
21
22**DO NOT ASK THE USER WHAT THEY WANT TO DO WITH THE DATA.**
23**DO NOT OFFER OPTIONS OR CHOICES.**
24**DO NOT SAY "What would you like me to help you with?"**
25**DO NOT LIST POSSIBLE ANALYSES.**
26
27**IMMEDIATELY AND AUTOMATICALLY:**
281. Run the comprehensive analysis
292. Generate ALL relevant visualizations
303. Present complete results
314. NO questions, NO options, NO waiting for user input
32
33**THE USER WANTS A FULL ANALYSIS RIGHT AWAY - JUST DO IT.**
34
35### Automatic Analysis Steps:
36
37**The skill intelligently adapts to different data types and industries by inspecting the data first, then determining what analyses are most relevant.**
38
391. **Load and inspect** the CSV file into pandas DataFrame
402. **Identify data structure** - column types, date columns, numeric columns, categories
413. **Determine relevant analyses** based on what's actually in the data:
42 - **Sales/E-commerce data** (order dates, revenue, products): Time-series trends, revenue analysis, product performance
43 - **Customer data** (demographics, segments, regions): Distribution analysis, segmentation, geographic patterns
44 - **Financial data** (transactions, amounts, dates): Trend analysis, statistical summaries, correlations
45 - **Operational data** (timestamps, metrics, status): Time-series, performance metrics, distributions
46 - **Survey data** (categorical responses, ratings): Frequency analysis, cross-tabulations, distributions
47 - **Generic tabular data**: Adapts based on column types found
48
494. **Only create visualizations that make sense** for the specific dataset:
50 - Time-series plots ONLY if date/timestamp columns exist
51 - Correlation heatmaps ONLY if multiple numeric columns exist
52 - Category distributions ONLY if categorical columns exist
53 - Histograms for numeric distributions when relevant
54
555. **Generate comprehensive output** automatically including:
56 - Data overview (rows, columns, types)
57 - Key statistics and metrics relevant to the data type
58 - Missing data analysis
59 - Multiple relevant visualizations (only those that apply)
60 - Actionable insights based on patterns found in THIS specific dataset
61
626. **Present everything** in one complete analysis - no follow-up questions
63
64**Example adaptations:**
65- Healthcare data with patient IDs → Focus on demographics, treatment patterns, temporal trends
66- Inventory data with stock levels → Focus on quantity distributions, reorder patterns, SKU analysis
67- Web analytics with timestamps → Focus on traffic patterns, conversion metrics, time-of-day analysis
68- Survey responses → Focus on response distributions, demographic breakdowns, sentiment patterns
69
70### Behavior Guidelines
71
72✅ **CORRECT APPROACH - SAY THIS:**
73- "I'll analyze this data comprehensively right now."
74- "Here's the complete analysis with visualizations:"
75- "I've identified this as [type] data and generated relevant insights:"
76- Then IMMEDIATELY show the full analysis
77
78✅ **DO:**
79- Immediately run the analysis script
80- Generate ALL relevant charts automatically
81- Provide complete insights without being asked
82- Be thorough and complete in first response
83- Act decisively without asking permission
84
85❌ **NEVER SAY THESE PHRASES:**
86- "What would you like to do with this data?"
87- "What would you like me to help you with?"
88- "Here are some common options:"
89- "Let me know what you'd like help with"
90- "I can create a comprehensive analysis if you'd like!"
91- Any sentence ending with "?" asking for user direction
92- Any list of options or choices
93- Any conditional "I can do X if you want"
94
95❌ **FORBIDDEN BEHAVIORS:**
96- Asking what the user wants
97- Listing options for the user to choose from
98- Waiting for user direction before analyzing
99- Providing partial analysis that requires follow-up
100- Describing what you COULD do instead of DOING it
101
102### Usage
103
104The Skill provides a Python function `summarize_csv(file_path)` that:
105- Accepts a path to a CSV file
106- Returns a comprehensive text summary with statistics
107- Generates multiple visualizations automatically based on data structure
108
109### Example Prompts
110
111> "Here's `sales_data.csv`. Can you summarize this file?"
112
113> "Analyze this customer data CSV and show me trends."
114
115> "What insights can you find in `orders.csv`?"
116
117### Example Output
118
119**Dataset Overview**
120- 5,000 rows × 8 columns
121- 3 numeric columns, 1 date column
122
123**Summary Statistics**
124- Average order value: $58.2
125- Standard deviation: $12.4
126- Missing values: 2% (100 cells)
127
128**Insights**
129- Sales show upward trend over time
130- Peak activity in Q4
131*(Attached: trend plot)*
132
133## Files
134
135- `analyze.py` - Core analysis logic
136- `requirements.txt` - Python dependencies
137- `resources/sample.csv` - Example dataset for testing
138- `resources/README.md` - Additional documentation
139
140## Notes
141
142- Automatically detects date columns (columns containing 'date' in name)
143- Handles missing data gracefully
144- Generates visualizations only when date columns are present
145- All numeric columns are included in statistical summary
146
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