data-analysis
Data Analysis lets you query uploaded Excel and CSV files using SQL, supporting multi-sheet workbooks, aggregation, filtering, and cross-file joins. Run statistical summaries, inspect schemas, and export results in multiple formats. Built on DuckDB for efficient columnar analysis.
Data Analysis performs SQL-based exploration of uploaded Excel and CSV files, including aggregation, filtering, and statistical summaries.
AI-generated summary based on this skill's SKILL.md
Install
bytedance/deer-flow/data-analysis · repository language: Python
git clone https://github.com/bytedance/deer-flow
cp -r deer-flow/skills/public/data-analysis ~/.claude/skills/data-analysisFrequently asked questions
AI-generated answers based on this skill's SKILL.md and metadata
How do I analyze my Excel file with data-analysis?
Data Analysis lets you upload Excel or CSV files and query them directly using SQL. Simply upload your spreadsheet, and you can run SQL queries to filter, aggregate, and analyze your data. The skill supports multi-sheet workbooks, so you can work with complex Excel files containing multiple sheets. Results can be exported to CSV or JSON formats for further use.
Can data-analysis generate statistics from CSV files?
Yes, data-analysis excels at generating statistical summaries and descriptive statistics from CSV data. You can run SQL queries to compute counts, averages, sums, and other aggregate functions. The skill provides built-in support for calculating summary statistics, making it easy to explore and understand your dataset without manual calculations.
What's the best way to query an Excel spreadsheet with SQL?
Data Analysis uses DuckDB, a powerful columnar database engine, to enable efficient SQL queries on your uploaded Excel spreadsheets. You write standard SQL syntax to select, filter, and transform your data. The skill handles schema inspection automatically, so you can reference columns and sheets directly in your queries without setup.
Does data-analysis support joining data from multiple files?
Data Analysis supports joining and correlating data across multiple uploaded files. You can upload several Excel or CSV files and write SQL queries that join tables from different sources. This enables cross-file analysis and data enrichment workflows, making it simple to combine related datasets for deeper insights.
Can I create pivot tables and aggregate data by groups?
Data Analysis supports pivot table creation and group-by aggregation through SQL queries. You can use GROUP BY clauses to aggregate data by one or more columns, calculate metrics per group, and reshape your results. The skill makes it straightforward to summarize large datasets and identify patterns across categories.
How do I export filtered or transformed results from data-analysis?
Data Analysis lets you export your query results to CSV or JSON formats. After running your SQL analysis, filtering, or transformation queries, you can download the results in your preferred format for use in other tools, reports, or downstream processes.
SKILL.md
rendered from the published skill — quoted content, verbatim
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
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skills/public/data-analysis/SKILL.md
skills/public/data-analysis/scripts/analyze.py