---

name: task-engine description: Create, organize, and prioritize tasks from briefs or brain dumps. Sync with Taskmaster MCP. Use to plan work, track status, and pick next steps. triggers:

• create tasks
• organize tasks
• what needs to be done
• prioritize
• תארגן את המשימות
• brain dump

---

Task Engine Skill

Transform project briefs and brain dumps into organized, prioritized task lists with dependencies. Syncs bidirectionally with Taskmaster MCP.

Purpose

The task engine manages all task-related operations including creation, organization, prioritization, and synchronization with Taskmaster MCP. It supports both structured input (from briefs) and unstructured input (brain dumps).

Taskmaster MCP Integration

Read Operations

Write Operations

Sync Direction

• Local → Taskmaster: Push changes from {project}_tasks.md to MCP
• Taskmaster → Local: Pull updates from MCP to {project}_tasks.md
• Bidirectional: Keep both in sync

Inputs

Required:

• source: Brief, brain dump, or raw task list

Optional:

• project_context: From {project}_brief.md
• existing_tasks: From {project}_tasks.md or Taskmaster

Workflow

Step 1: Capture Tasks

From Brief:

• Extract implied tasks from milestones
• Convert deliverables to tasks
• Identify dependencies from success criteria

From Brain Dump:

• Parse text for action items
• Identify verbs indicating tasks
• Group related items

From Existing List:

• Clean up format
• Standardize structure
• Fill missing fields

Step 2: Structure Tasks

Apply these rules:

• One clear outcome per task
• Create subtasks if work exceeds 2 hours
• Maximum 3 levels of nesting
• Each task needs: ID, name, status, priority

Step 3: Analyze Priority

Use the Eisenhower Matrix:

Step 4: Identify Dependencies

For each task, determine:

• What must complete before this?
• What does this block?
• Are there parallel paths?

Step 5: Sync with Taskmaster

Copy# Example sync workflow
tasks = taskmaster.get_tasks(project_root)
local_tasks = parse_tasks_md(f"{project}_tasks.md")

# Merge and update
for task in merged_tasks:
    if task.changed_locally:
        taskmaster.set_task_status(task.id, task.status)
    elif task.changed_remotely:
        update_local_task(task)

Step 6: Checkpoint

CHECKPOINT: Present organized list to user

Ask:

• "Does this capture everything?"
• "Is the priority correct?"
• "Any dependencies I missed?"

Step 7: Save

• Save as {project}_tasks.md
• Suggest: "Use delegation-advisor to assign high priority tasks"

Output Format

See resources/tasks_template.md for full template.

Copy# Tasks: {project_name}

**Last Updated:** {timestamp}
**Total Tasks:** {count} | **Completed:** {done_count}

## High Priority / Urgent

### [ ] {task_name}
- **ID:** {task_id}
- **Status:** {status}
- **Assignee:** {human|ai_agent|pending}
- **Dependencies:** {deps}

## Medium Priority
...

## Low Priority / Backlog
...

## Completed
- [x] {completed_task} (Done: {date})

Example: Brain Dump Processing

User Input:

"brain dump: צריך לחקור frameworks קיימים, לכתוב draft לדיקן, להכין מצגת, לתאם פגישה עם IT, לבדוק תקציב"

Processing:

1. Extract 5 action items
2. Categorize by type (research, writing, meetings)
3. Assign priorities based on dependencies
4. Sync with Taskmaster

Output:

Copy# Tasks: AI Strategy

## High Priority / Urgent
### [ ] Research existing AI frameworks
- **ID:** task-1
- **Status:** pending
- **Dependencies:** none

### [ ] Draft proposal for Dean
- **ID:** task-2
- **Dependencies:** task-1

## Medium Priority
### [ ] Prepare presentation
- **ID:** task-3
- **Dependencies:** task-2

Cross-Interface Notes

• Claude AI: Taskmaster MCP called via skill
• Claude Code: Taskmaster MCP called natively
• Both use same task format for compatibility