data-summarizer

name

data-summarizer

description

Analyzes and summarizes datasets, producing statistical overviews, trend identification, and key insights. Use when the user asks to analyze data, find patterns, summarize a dataset, or generate descriptive statistics from CSV, JSON, or tabular data. Trigger phrases include "summarize this data", "what are the key trends", "analyze this spreadsheet", "give me stats on this dataset", "what does this data tell us", "break down these numbers", "find patterns in this data".

Data Summarizer

Quick Start

Analyze datasets to produce statistical summaries, identify trends, and extract key insights. Support CSV, JSON, and tabular data formats. Present findings in clear tables and bullet points.

Workflow

1. Load and inspect the dataset: format, row count, column types, missing values
2. Generate descriptive statistics: mean, median, min, max, std dev for numeric columns
3. Identify data quality issues: nulls, outliers, duplicates, inconsistent formats
4. Analyze distributions and trends for key metrics
5. Extract top insights and notable patterns
6. Present a structured summary with visualizable recommendations

Examples

Example 1: Sales Data Summary Input: "Analyze this Q1 sales data file" Output:

DATA SUMMARY
============
Dataset: Q1 Sales Data
Records: 2,847 rows × 12 columns
Period: 01/01/2026 – 03/31/2026

DATA QUALITY
-------------
| Check          | Result                    |
|----------------|---------------------------|
| Missing Values | 23 rows (0.8%) in 'region'|
| Duplicates     | 5 duplicate order IDs     |
| Outliers       | 3 orders > $25,000        |

DESCRIPTIVE STATISTICS
----------------------
| Metric        | Value              |
|---------------|--------------------|
| Total Revenue | $12.45M            |
| Avg Order     | $4,372             |
| Median Order  | $2,850             |
| Max Order     | $580,000           |
| Min Order     | $125               |

KEY INSIGHTS
------------
1. Revenue grew 18% month-over-month (Jan→Feb→Mar)
2. Top 3 products account for 45% of total revenue
3. New York metro area leads with 38% of orders
4. Weekend orders are 35% lower than weekday average
5. Average order value increased 12% compared to Q4 2025

RECOMMENDED VISUALIZATIONS:
- Line chart: monthly revenue trend
- Bar chart: revenue by region
- Pie chart: product category distribution

Example 2: Quick Stats Input: "Give me a quick summary of this CSV file" Output: A concise overview with row/column count, data types, missing values, and top-5 statistics.

Tools

• Use Read to load CSV, JSON, or data files
• Use Bash to run data processing commands (sort, count, aggregate)
• Use Write to save analysis reports
• Use Grep to search for specific patterns in data files

Error Handling

• If file format is unsupported → suggest converting to CSV or JSON
• If dataset is too large to process at once → sample or process in chunks
• If columns have mixed data types → flag and suggest cleanup
• If no numeric columns found → focus on categorical analysis (counts, modes, distributions)

Connectors (Optional)

This skill works standalone. When connected to external tools, it unlocks additional capabilities:

Connector	What it enables
~~database	Query live databases directly to pull datasets for analysis
~~BI tool	Push summaries and insights into dashboards and automated reports
~~spreadsheet	Read data from linked spreadsheets and write summary tables back
~~data warehouse	Access large-scale datasets with full schema and partition metadata

Rules

• Always start with data quality assessment before analysis
• Report exact numbers — never round without stating the original value
• Flag outliers but do not remove them without user confirmation
• Currency defaults to USD; adjust when data indicates otherwise
• Percentages should include the base number for context
• Suggest appropriate chart types for key findings
• Note any assumptions made during analysis

Output Template

DATA SUMMARY
============
Dataset: [Name/Description]
Records: [Rows] × [Columns]
Period: [Date range if applicable]

DATA QUALITY
-------------
| Check | Result |
|-------|--------|
| [Check type] | [Finding] |

DESCRIPTIVE STATISTICS
----------------------
| Metric | Value |
|--------|-------|
| [Stat] | [Value] |

KEY INSIGHTS
------------
1. [Insight with supporting data]

RECOMMENDED VISUALIZATIONS:
- [Chart type]: [What it shows]

Related Skills

• chart-generator -- turn key findings into polished visualizations and dashboard components
• kpi-tracker -- frame summary insights as trackable KPIs with targets and scorecards
• data-cleaner -- fix data quality issues discovered during the profiling step