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Spreadsheet

面向 Google Sheets、Excel 与 CSV 的表格读写与分析技能：首次访问记录列结构到 ~/spreadsheet/ 记忆，保留原有格式与公式，按行数分级加载，并支持按项目保存 schema 与模板。

ivangdavila
ivangdavila
热度 80.71
最近更新 2026-05-17 12:15:13
分类：数据依赖：代码执行 / 读写文件 / 第三方 API应用场景：数据处理 / 报告撰写 / 工作流 / 模板

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add spreadsheet
Metadata
name
Spreadsheet
slug
spreadsheet
version
1.0.0
description
Read, write, and analyze tabular data with schema memory, format preservation, and multi-platform support.
metadata
{"clawdbot":{"emoji":"📊","requires":{"bins":[]},"os":["linux","darwin","win32"]}}

When to Use

User needs spreadsheet operations: reading data, writing cells, analyzing tables, generating reports, or tracking structured information across Google Sheets, Excel, or CSV files.

Architecture

Memory lives in ~/spreadsheet/. See memory-template.md for setup.

~/spreadsheet/
  memory.md           # Preferences, recent sheets, format rules
  projects/           # Per-project schemas and configs
    {name}.md         # Sheet IDs, columns, formulas
  templates/          # Reusable structures
  exports/            # Generated files

Quick Reference

Topic File
Memory setup memory-template.md
Google Sheets API google-sheets.md
Excel operations excel.md
CSV handling csv.md

Scope

This skill ONLY:

  • Reads/writes spreadsheets user explicitly requests
  • Stores schemas and preferences in ~/spreadsheet/
  • Processes files user provides

This skill NEVER:

  • Accesses sheets without user request
  • Stores passwords, API keys, or sensitive financial data
  • Modifies files outside ~/spreadsheet/ or user paths

Data Storage

All data stored in ~/spreadsheet/. Create on first use:

mkdir -p ~/spreadsheet/{projects,templates,exports}

Self-Modification

This skill NEVER modifies its own SKILL.md. All user data stored in ~/spreadsheet/ only.

Core Rules

1. Schema First

On first access to any sheet:

  1. Document columns (name, type, sample)
  2. Save to projects/{name}.md
  3. Reference schema in future ops

2. Format Preservation

Situation Action
Updating cells Preserve existing format
Writing numbers Match user's locale (1,000.00 vs 1.000,00)
Writing dates Use user's preferred format
Writing formulas Never overwrite unless asked

3. Large Data Strategy

Row Count Approach
<1000 Load fully
1000-10000 Sample + targeted queries
>10000 Paginate, warn before loading

4. Integration Priority

  1. Google Sheets - if API configured
  2. Excel (.xlsx) - local files, use openpyxl
  3. CSV - universal fallback

5. Memory Updates

Event Action
New sheet accessed Add ID + schema to memory
User corrects format Save preference
Column renamed Update project schema

Common Traps

  • Truncating without warning - Always confirm before loading >1000 rows
  • Losing formulas - Use data_only=False in openpyxl, read formulas separately
  • Schema drift - Re-verify if last access >7 days
  • Rate limits - Batch Google Sheets requests, max 100/100s
  • Encoding - Default UTF-8, check for BOM on European files
  • Empty cells - Google API omits them; pandas fills with NaN