name: distributed-task-orchestrator
description: Decompose complex tasks into parallel sub-agents. Use for multi-step operations, batch processing, or when user mentions "parallel", "agents", "orchestrate", "subtasks", or "concurrent". Supports simulated parallel execution and real Claude CLI sub-agent launching.
license: Apache-2.0
metadata:
author: ai-agent-toolkit
version: "1.0.0"

Distributed Task Orchestrator

Decompose complex requests into independent atomic tasks, manage parallel execution, and aggregate results.

Quick Decision

Is task complex? (3+ independent steps, multiple files, parallel benefit)
├── NO → Execute directly, skip orchestration
└── YES → Use orchestration
    ├── Simulated mode (default) → Present as parallel batches
    └── CLI mode (user requests) → Launch real Claude CLI sub-agents

Skip orchestration for: single-file ops, simple queries, < 3 steps, purely sequential tasks.

Core Workflow

Phase 1: Decompose

Analyze request → Break into atomic tasks → Map dependencies → Create .orchestrator/master_plan.md

# Task Plan

## Request
> [Original request]

## Tasks
| ID | Task | Deps | Status |
|----|------|------|--------|
| T-01 | [Description] | None | 🟡 |
| T-02 | [Description] | T-01 | ⏸️ |

Status: 🟡 Pending · 🔵 Running · ✅ Done · ❌ Failed · ⏸️ Waiting

Phase 2: Assign Agents

Create .orchestrator/agent_tasks/agent-XX.md for each task:

# Agent-XX: [Task Name]
**Input:** [parameters]
**Do:** [specific instructions]
**Output:** [expected format]

Phase 3: Execute

Simulated Mode (Default):

═══ Batch #1 (No Dependencies) ═══
🤖 Agent-01 [T-01: Task Name]
   ⚙️ [Execution steps...]
   ✅ Completed

═══ Batch #2 (After Batch #1) ═══
🤖 Agent-02 [T-02: Task Name]
   ⚙️ [Execution steps...]
   ✅ Completed

CLI Mode (When Requested): See cli-integration.md

Phase 4: Aggregate

Collect results → Merge by dependency order → Generate .orchestrator/final_output.md

Dependency Patterns

• Parallel: T-01, T-02, T-03 → T-04 (first three run together)
• Serial: T-01 → T-02 → T-03 (each waits for previous)
• DAG: Complex graphs use topological sort

Error Handling

Best Practices

1. Granularity: Target 1-5 min per task; split large, merge trivial
2. Parallelism: Minimize dependencies; use file-based data passing
3. State: Update master_plan.md on every status change

Reference Files

• workflow.md - Detailed workflow specification
• templates.md - Complete templates for all files
• cli-integration.md - Claude CLI integration guide
• examples.md - Practical examples