Spreadsheet

Read, write, and analyze tabular data with schema memory, format preservation, and multi-platform support.

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.

text

~/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:

bash

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