Convert Spreadsheet Rows

name: convert-spreadsheet-rows
description: >
Use when (1) Convert spreadsheet rows into structured task objects for Jira. Markdown. or JSON formats.
license: MIT
metadata:
version: "1.0"
category: productivity
author: wangjipeng
sources:

• https://github.com/MiniMax-AI/skills

Core Position

This skill transforms input data from one format into a target format, preserving structure and fidelity. It is NOT a simple copy-paste — it handles formatting, schema mapping, and edge cases.

Key responsibilities:

• Parse the input format (JSON/CSV/code/etc.) and validate structure before transforming
• Apply formatting rules specific to the target format (indentation, escaping, etc.)
• Handle edge cases: missing fields, unusual characters, nested structures
• Provide a clear mapping summary so the user understands how input maps to output

Modes

/convert-spreadsheet-rows --pretty

Formatted output. Applies proper indentation, spacing, and style conventions.

/convert-spreadsheet-rows --strict

Strict mode. Fails on any deviation from expected structure rather than guessing.

Execution Steps

1. Parse input — Read and parse the input; detect format (JSON/CSV/XML/code/etc.)

• If parsing fails, report: "Failed to parse input as [format] — error at line [N]: [detail]"

1. Validate structure — Check required fields/columns are present

• If missing required field X, stop and report: "Missing required field: [X]"

1. Transform — Convert input to target format, applying format-specific rules

• Preserve all data — do not silently drop fields
• Apply proper escaping for special characters (quotes, newlines, etc.)

1. Validate output — Run the target format parser on the result to confirm it's valid

• If output is invalid, revert to previous version and report what went wrong

1. Deliver — Return the converted output with a brief mapping summary

Mandatory Rules

Do not

• Do not silently drop fields or data — if a field cannot be mapped, report it
• Do not guess at missing data — if a field is absent, leave it null/empty and flag it
• Do not apply formatting that destroys the semantic meaning of the data
• Do not produce output that fails the target format validator
• Do not convert binary data as if it were text — detect and handle binary separately

Do

• Report the complete field mapping: [source] -> [target] for every field
• Validate input and output formats before and after transformation
• Preserve character encoding (UTF-8) throughout the conversion process
• Handle large inputs in chunks if needed to avoid memory exhaustion
• Log conversion statistics: fields mapped, fields dropped, warnings issued

Quality Bar

| Criterion | Minimum | Ideal |
|-----------|---------|-------|
| Data fidelity | Zero data loss — all fields mapped | Full semantic equivalence, not just structural |
| Format validity | Output passes target parser | Output passes strict schema validation |
| Edge case handling | Handles missing/null/empty gracefully | Documents every edge case decision |
| Escaping correctness | Proper escaping for target format | Round-trip: convert back to source equals original |
| Performance | Completes within 2x manual time | Streaming output for large inputs |

A good output passes the target format parser without errors and preserves all semantic content.

Good vs. Bad Examples

| Scenario | Bad | Good |
|---------|-----|------|
| Missing field | Omits field from output silently | Reports "Field [X] absent — output null, flagged as warning" |
| Special characters | Only escapes visible chars | Escapes all special chars per target format spec |
| Large input | Loads entire file into memory | Streams in chunks, reports progress at 25/50/75% |
| Output validation | Skips validation | Runs target parser on output, confirms valid before returning |
| Format error | Returns raw output with error text appended | Returns nothing, reports "Output invalid: [parser error] at [location]" |