data-analysis
Use this skill when the user uploads Excel (.xlsx/.xls) or CSV files and wants to perform data analysis, generate statistics, create summaries, pivot tables, SQL queries, or any form of structured data exploration. Supports multi-sheet Excel workbooks, aggregation, filtering, joins, and exporting results to CSV/JSON/Markdown.
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- 82 902
- Лиценз
- MIT
- Последна проверка на записа
- 19.09.2026 г.
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Data Analysis Skill
Overview
This skill analyzes user-uploaded Excel/CSV files using DuckDB — an in-process analytical SQL engine. It supports schema inspection, SQL-based querying, statistical summaries, and result export, all through a single Python script.
Core Capabilities
- Inspect Excel/CSV file structure (sheets, columns, types, row counts)
- Execute arbitrary SQL queries against uploaded data
- Generate statistical summaries (mean, median, stddev, percentiles, nulls)
- Support multi-sheet Excel workbooks (each sheet becomes a table)
- Export query results to CSV, JSON, or Markdown
- Handle large files efficiently with DuckDB's columnar engine
Workflow
Step 1: Understand Requirements
When a user uploads data files and requests analysis, identify:
- File location: Path(s) to uploaded Excel/CSV files under
/mnt/user-data/uploads/ - Analysis goal: What insights the user wants (summary, filtering, aggregation, comparison, etc.)
- Output format: How results should be presented (table, CSV export, JSON, etc.)
- You don't need to check the folder under
/mnt/user-data
Step 2: Inspect File Structure
First, inspect the uploaded file to understand its schema:
python /mnt/skills/public/data-analysis/scripts/analyze.py \
--files /mnt/user-data/uploads/data.xlsx \
--action inspect
This returns:
- Sheet names (for Excel) or filename (for CSV)
- Column names, data types, and non-null counts
- Row count per sheet/file
- Sample data (first 5 rows)
Step 3: Perform Analysis
Based on the schema, construct SQL queries to answer the user's questions.
Run SQL Query
python /mnt/skills/public/data-analysis/scripts/analyze.py \
--files /mnt/user-data/uploads/data.xlsx \
--action query \
--sql "SELECT category, COUNT(*) as count, AVG(amount) as avg_amount FROM Sheet1 GROUP BY category ORDER BY count DESC"
Generate Statistical Summary
python /mnt/skills/public/data-analysis/scripts/analyze.py \
--files /mnt/user-data/uploads/data.xlsx \
--action summary \
--table Sheet1
This returns for each numeric column: count, mean, std, min, 25%, 50%, 75%, max, null_count. For string columns: count, unique, top value, frequency, null_count.