Task ToDo

Manage tasks with persistent storage, supporting creation, retrieval, updates, deletion, filtering by status or priority, and status tracking via SQLite.

Task-Todo Agent Skill

Overview

A task management agent skill that provides persistent task storage and management using SQLite database. This skill enables AI agents to create, read, update, delete, and query tasks with status tracking and priority management.

Capabilities

• Task Creation: Add new tasks with title, description, status, and priority
• Task Retrieval: Get single tasks or list all tasks
• Task Filtering: Filter tasks by status or priority
• Task Updates: Modify any task field (title, description, status, priority)
• Task Deletion: Remove tasks from the database
• Persistent Storage: All tasks stored in SQLite database with automatic timestamps

Usage

Command Line Interface

bash

# Add task
python task_skill.py add "Task title" "Description" --status pending --priority high

# List all tasks
python task_skill.py list

# Filter by status
python task_skill.py list --status in_progress

# Filter by priority
python task_skill.py list --priority urgent

# Get task details
python task_skill.py get 1

# Update task
python task_skill.py update 1 --status completed --priority low

# Delete task
python task_skill.py delete 1

Data Model

Task Fields

Field	Type	Description	Required	Default
id	INTEGER	Auto-generated task ID	Auto	-
title	TEXT	Task title	Yes	-
description	TEXT	Task description	No	""
status	TEXT	Task status	Yes	"pending"
priority	TEXT	Task priority	Yes	"medium"
created_at	TIMESTAMP	Creation timestamp	Auto	Current time
updated_at	TIMESTAMP	Last update timestamp	Auto	Current time

Status Values

• pending - Task is pending and not started
• in_progress - Task is currently being worked on
• completed - Task is finished
• blocked - Task is blocked and cannot proceed

Priority Values

• low - Low priority task
• medium - Medium priority task (default)
• high - High priority task
• urgent - Urgent task requiring immediate attention

Response Format

All agent methods return a dictionary with a success field:

Successful Add

python

{
    "success": True,
    "task_id": 1,
    "message": "Task created with ID: 1"
}

Successful List

python

{
    "success": True,
    "tasks": [
        {
            "id": 1,
            "title": "Task title",
            "description": "Task description",
            "status": "pending",
            "priority": "medium",
            "created_at": "2026-02-11T10:30:00",
            "updated_at": "2026-02-11T10:30:00"
        }
    ],
    "count": 1
}

Successful Get

python

{
    "success": True,
    "task": {
        "id": 1,
        "title": "Task title",
        "description": "Task description",
        "status": "pending",
        "priority": "medium",
        "created_at": "2026-02-11T10:30:00",
        "updated_at": "2026-02-11T10:30:00"
    }
}

Failed Operation

python

{
    "success": False,
    "message": "Task 1 not found"
}

Database

• Database File: tasks.db (created automatically in current directory)
• Database Type: SQLite3
• Schema Constraints: Status and priority values are validated at database level
• Timestamps: Automatically managed by the database

Dependencies

None - uses Python's built-in sqlite3 module.

Use Cases

1. Task Tracking: Track personal or project tasks with status and priority
2. TODO Management: Maintain a persistent TODO list
3. Workflow Automation: Integrate task management into automated workflows
4. Project Management: Simple project task tracking
5. Agent Memory: Provide AI agents with persistent task storage

Notes

• The database connection is persistent across operations
• Always call agent.close() when finished to properly close the database
• Use context manager pattern for automatic cleanup:

  python

  with TaskAgent() as agent:
      agent.add_task("Task", "Description")
  

• Task IDs are auto-incrementing integers starting from 1
• All timestamps are in ISO 8601 format