CSV to Task#

Use when (1) user pastes CSV data and wants to convert each row into an actionable task, ticket, or to-do item. (2) user says "convert these to tasks", "create tasks from this list", or "turn this spreadsheet into a todo list". (3) user provides a project roster, backlog, or table and asks to "make it a task list".

Core Position#

This skill solves the specific problem of: rows of tabular data need to become individual, trackable task units.

This skill IS NOT:

• A data analysis tool — it produces tasks, not insights
• A reminder/calendar tool — tasks can be for any system (Jira, Linear, Notion, plain text)
• Activated by general "make a list" requests without structured data

This skill IS activated ONLY when: structured tabular/CSV data + task creation intent are both present.

Modes#

/csv-to-task#

Default mode. Converts CSV rows to structured task objects with status, assignee, priority, and due date.

When to use: User provides CSV and wants task objects (Jira format, Markdown checklist, etc.)

/csv-to-task/estimate#

Adds time or complexity estimates to each task based on column data.

When to use: User wants to go beyond raw conversion and add sprint planning data.

Execution Steps#

Step 1 — Parse the CSV#

1. Receive CSV input (pasted text, file, or path)
2. Detect header row — columns become task field names
3. Identify key task fields:
   • Title/Name (required): usually first string column or explicitly labeled
   • Assignee (optional): person responsible
   • Priority (optional): high/medium/low, P0-P3, or numeric
   • Due date (optional): ISO date, MM/DD, or relative ("next Friday")
   • Status (optional): todo/done/in-progress or explicit column
   • Description (optional): longer text fields
4. If no clear title column exists, ask the user which column to use as task name

Step 2 — Map to Task Structure#

Map each row to a task object. Default fields:

CSV ColumnTask FieldAny text column (title-like)titlePerson name/email columnassigneeH/M/L or P0-P3priorityDate columndueDateCategory/tag columnlabelsLong text columndescription

Unmapped columns → attach as key-value metadata.

Step 3 — Format Output#

Choose format based on user intent or explicit request:

• Markdown checklist: - [ ] Task title @assignee #priority
• Jira-style: PROJECT-123: Task title [labels] — assignee
• JSON array: [{"title": "...", "assignee": "...", ...}]
• CSV with new columns: original CSV + added status and taskId columns

Step 4 — Validate#

• No rows silently dropped — count input rows vs. output tasks
• Title field is populated for every task
• Date formats are consistent and valid

Mandatory Rules#

Do not#

• Do not invent assignee names not in the data
• Do not set due dates arbitrarily — use column data or ask
• Do not merge or split rows without explicit instruction
• Do not use task status "done" unless user confirms completion

Do#

• Respect column-to-field mappings from the data
• Preserve all original CSV data as task metadata
• Handle empty cells explicitly (mark as "unassigned", "none")
• Output a consistent format for all rows

Quality Bar#

A good output:

• Every row becomes exactly one task with a title
• Field mapping is explicit and traceable to source columns
• All original data is preserved (no silent drop)
• Output format is valid and runnable/parseable

A bad output:

• Merges multiple rows into one task
• Drops rows with missing fields
• Produces plain text without structure (not parseable)
• Invents data not present in the CSV

Good vs. Bad Examples#

ScenarioBad OutputGood Output10 rows, no status columnAll marked "done" by defaultAll marked "todo" with note "no status column found"Missing assignee"Assignee: John" (random)"Assignee: unassigned"Priority in P0-P3 formatIgnoredMapped correctly to P0/P1/P2/P3Title column has 200 charsTruncated to 50Kept full, summarized in description

References#

• references/ — Field mapping templates, output format examples for Jira/Label/Notion/Linear