Task Based Multiagent

Task-Based Multi-Agent Skill

Guide creation of task-based multi-agent systems using shared task files and worktree isolation.

When to Use

• Setting up parallel agent execution
• Managing multiple concurrent workflows
• Scaling beyond single-agent patterns
• Building task queue systems

Core Concept

Agents share a task file that acts as a coordination mechanism:

## To Do
- [ ] Task A
- [ ] Task B

## In Progress
- [🟡 abc123] Task C - being worked on

## Done
- [✅ def456] Task D - completed

Task File Format

tasks.md:

# Tasks

## Git Worktree {worktree-name}

## To Do
[] Pending task description                           # Available
[⏰] Blocked task (waits for above)                   # Blocked
[] Task with #opus tag                                # Model override
[] Task with #adw_plan_implement tag                  # Workflow override

## In Progress
[🟡, adw_12345] Task being processed                  # Claimed by agent

## Done
[✅ abc123, adw_12345] Completed task                 # Commit hash saved
[❌, adw_12345] Failed task // Error reason           # Error captured

Status Markers

Tag System

Tags modify agent behavior:

Implementation Architecture

┌─────────────────────────────────────────┐
│            CRON TRIGGER                  │
│  (polls tasks.md every N seconds)        │
└─────────────────┬───────────────────────┘
                  │
        ┌─────────┼─────────┐
        │         │         │
        v         v         v
   ┌────────┐ ┌────────┐ ┌────────┐
   │ Task A │ │ Task B │ │ Task C │
   │Worktree│ │Worktree│ │Worktree│
   │   1    │ │   2    │ │   3    │
   └────────┘ └────────┘ └────────┘

Setup Workflow

Step 1: Create Task File

# tasks.md

## To Do
[] First task to complete
[] Second task to complete
[⏰] Blocked until first completes

## In Progress

## Done

Step 2: Create Data Models

from pydantic import BaseModel
from typing import Literal, Optional, List

class Task(BaseModel):
    description: str
    status: Literal["[]", "[⏰]", "[🟡]", "[✅]", "[❌]"]
    adw_id: Optional[str] = None
    commit_hash: Optional[str] = None
    tags: List[str] = []
    worktree_name: Optional[str] = None

Step 3: Create Trigger Script

# adw_trigger_cron_tasks.py
def main():
    while True:
        tasks = parse_tasks_file("tasks.md")
        pending = [t for t in tasks if t.status == "[]"]

        for task in pending:
            if not is_blocked(task):
                # Mark as in progress
                claim_task(task)
                # Spawn subprocess
                spawn_task_workflow(task)

        time.sleep(5)  # Poll interval

Step 4: Create Task Workflows

# adw_build_update_task.py (simple)
def main(task_id: str):
    # Mark in progress
    update_task_status(task_id, "[🟡]")

    # Execute /build
    response = execute_template("/build", task_description)

    # Mark complete
    if response.success:
        update_task_status(task_id, "[✅]", commit_hash)
    else:
        update_task_status(task_id, "[❌]", error_reason)

Step 5: Add Worktree Isolation

Each task gets its own worktree:

git worktree add trees/{task_id} -b task-{task_id} origin/main

Coordination Rules

1. Claim before processing: Update status to [🟡] immediately
2. Respect blocking: Don't process [⏰] tasks until dependencies complete
3. Update on completion: Always update status, even on failure
4. Include context: Save commit hash, error reason, ADW ID

Key Memory References

• @git-worktree-patterns.md - Worktree isolation
• @composable-primitives.md - Workflow composition
• @zte-progression.md - Scaling to ZTE

Output Format

## Multi-Agent System Setup

**Task File:** tasks.md
**Trigger Interval:** 5 seconds
**Max Concurrent:** 5 agents

### Components
1. Task file format with status markers
2. Data models (Task, Status, Tags)
3. Cron trigger script
4. Task workflow scripts
5. Worktree isolation

### Workflow Routing
- Default: adw_build_update_task.py
- #adw_plan_implement: adw_plan_implement_update_task.py
- #opus: Use Opus model

### Status Flow
[] -> [🟡, id] -> [✅ hash, id]
                -> [❌, id] // error

Anti-Patterns

• Polling too frequently (< 1 second)
• Not claiming before processing (race conditions)
• Ignoring blocked tasks
• Not capturing failure reasons
• Running in same directory (no isolation)

Version History

• v1.0.0 (2025-12-26): Initial release

Last Updated

Date: 2025-12-26 Model: claude-opus-4-5-20251101

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