Agent skill · data analytics · bytedance
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.
Why this skill is useful
Adds SQL querying capabilities and statistical analysis functions for structured data exploration that the AI wouldn't reliably generate on its own.
What it needs
Requires duckdb installed locally. About 5k tokens when loaded. Last updated 2026-08-07. 79,444 stars on the source repository.
What this skill does
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: 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 Generate Statistical Summary This returns for each numeric column: count, mean, std, min, 25%, 50%, 75%, max, nullcount. For string columns: count, unique, top value, frequency, nullcount. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills bytedance/data-analysis