sheetsmith

تایید شده

Pandas-powered CSV & Excel management for quick previews, summaries, filtering, transforming, and format conversions. Use this skill whenever you need to inspect spreadsheet files, compute column-level summaries, apply queries or expressions, or export cleansed data to a new CSV/TSV/XLSX output without rewriting pandas every time.

@openclaw
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نصب مهارت

مهارت‌ها کدهای شخص ثالث از مخازن عمومی GitHub هستند. SkillHub الگوهای مخرب شناخته‌شده را اسکن می‌کند اما نمی‌تواند امنیت را تضمین کند. قبل از نصب، کد منبع را بررسی کنید.

نصب سراسری (سطح کاربر):

npx skillhub install openclaw/skills/sheetsmith

نصب در پروژه فعلی:

npx skillhub install openclaw/skills/sheetsmith --project

مسیر پیشنهادی: ~/.claude/skills/sheetsmith/

محتوای SKILL.md

---
name: sheetsmith
description: Pandas-powered CSV & Excel management for quick previews, summaries, filtering, transforming, and format conversions. Use this skill whenever you need to inspect spreadsheet files, compute column-level summaries, apply queries or expressions, or export cleansed data to a new CSV/TSV/XLSX output without rewriting pandas every time.
---

# Sheetsmith

## Overview
Sheetsmith is a lightweight pandas wrapper that keeps the focus on working with CSV/Excel files: previewing, describing, filtering, transforming, and converting them in one place. The CLI lives at `skills/sheetsmith/scripts/sheetsmith.py`, and it automatically loads any CSV/TSV/Excel file, reports structural metadata, runs pandas expressions, and writes the results back safely.

## Quick start
1. Place the spreadsheet (CSV, TSV, or XLS/XLSX) inside the workspace or reference it via a full path.
2. Run `python3 skills/sheetsmith/scripts/sheetsmith.py <command> <path>` with the command described below.
3. When you modify data, either provide `--output new-file` to save a copy or pass `--inplace` to overwrite the source file.
4. Check `references/usage.md` for extra sample commands and tips.

## Commands
### summary
Prints row/column counts, dtype breakdowns, columns with missing data, and head/tail previews. Use `--rows` to control how many rows are shown after the summary and `--tail` to preview the tail instead of the head.

### describe
Runs `pandas.DataFrame.describe(include='all')` (customizable with `--include`) so you instantly see numeric statistics, cardinality, and frequency information. Supply `--percentiles` to add additional percentile lines.

### preview
Shows a quick tabulated peek at the first (`--rows`) or last (`--tail`) rows so you can sanity-check column order or formatting before taking actions.

### filter
Enter a pandas query string via `--query` (e.g., `state == 'CA' and population > 1e6`). The command can either print the filtered rows or, when you also pass `--output`, write the filtered table to a new CSV/TSV/XLSX file. Add `--sample` to inspect a random subset instead of the entire result.

### transform
Compose new columns, rename or drop existing ones, and immediately inspect the resulting table. Provide one or more `--expr` expressions such as `total = quantity * price`. Use `--rename old:new` and `--drop column` to reshape the table, and persist changes via `--output` or `--inplace`. The preview version (without writing) reuses the same `--rows`/`--tail` flags as the other commands.

### convert
Convert between supported formats (CSV/TSV/Excel). Always specify `--output` with the desired extension, and the helper will detect the proper writer (Excel uses `openpyxl`, CSV preserves the comma separator by default, TSV uses tabs). This is the simplest way to normalize data before running other commands.

## Workflow rules
- Always keep a copy of the raw file or write to a new path; the script will only overwrite the original when you explicitly demand `--inplace`.
- Use the same CLI for both exploration (`summary`, `preview`, `describe`) and editing (`filter`, `transform`). The `--output` flag works for filter/transform so you can easily branch results.
- Behind the scenes, the script relies on pandas + `tabulate` for Markdown previews and supports Excel/CSV/TSV, so ensure those dependencies are present (pandas, openpyxl, xlrd, tabulate are installed via apt on this system).
- Use `references/usage.md` for extended examples (multi-step cleaning, dataset comparison, expression tips) when the basic command descriptions above are not enough.

## References
- **Usage guidelines:** `references/usage.md` (contains ready-to-copy commands, expression patterns, and dataset cleanup recipes).

## Resources

- **GitHub:** https://github.com/CrimsonDevil333333/sheetsmith
- **ClawHub:** https://www.clawhub.ai/skills/sheetsmith