DI

distributed-task-orchestrator

Splits complex projects into parallel agent workflows.

Install

mkdir -p .claude/skills/distributed-task-orchestrator && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/7664" && unzip -o skill.zip -d .claude/skills/distributed-task-orchestrator && rm skill.zip

Installs to .claude/skills/distributed-task-orchestrator

Activation

This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.

Decompose complex tasks into parallel sub-agents. Use for multi-step operations, batch processing, or when user mentions "parallel", "agents", or "orchestrate".
160 chars✓ has a “when” trigger
Advanced

Key capabilities

  • Decompose complex requests into independent atomic tasks
  • Create master execution plans with dependency mapping
  • Manage parallel execution of sub-agents
  • Aggregate results from multiple sub-tasks into a final report

How it works

The skill analyzes a request to create a master plan, assigns atomic tasks to sub-agents, executes them in parallel or series based on dependencies, and merges the final output.

Inputs & outputs

You give it
Complex multi-step request
You get back
Orchestrated execution plan and aggregated results

When to use distributed-task-orchestrator

  • Decompose a large feature implementation
  • Orchestrate batch refactoring across multiple files
  • Manage multi-step migration tasks

About this skill

Distributed Task Orchestrator

You are an advanced distributed task orchestration system. 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):

# Windows - Parallel execution
$jobs = Get-ChildItem ".orchestrator/agent_tasks/*.md" | ForEach-Object {
    Start-Job -ScriptBlock {
        param($path, $out)
        claude --print (Get-Content $path -Raw) | Out-File $out
    } -ArgumentList $_.FullName, ".orchestrator/results/$($_.BaseName)-result.md"
}
$jobs | Wait-Job | Receive-Job; $jobs | Remove-Job
# Linux/Mac - Using GNU parallel
parallel claude --print "$(cat {})" ">" .orchestrator/results/{/.}-result.md ::: .orchestrator/agent_tasks/*.md

Phase 4: Aggregate

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

# Execution Report
- Tasks: N total, X succeeded, Y failed
- Duration: Zs

## Results
[Integrated findings organized logically]

## Key Takeaways
1. [Finding 1]
2. [Finding 2]

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

StrategyWhen to Use
Retry (3x, exponential backoff)Timeouts, transient failures
Skip and continueNon-critical tasks
Fail-fastCritical dependencies

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

Trigger Conditions

USE when:

  • 3+ independent steps possible
  • User mentions: "parallel", "concurrent", "subtasks", "agents"
  • Batch processing needed
  • Claude CLI sub-agents requested

SKIP when:

  • Single-step task
  • Quick query/explanation
  • Purely sequential with no parallel benefit

Related Files

When not to use it

  • For single-file operations or simple queries
  • For tasks with fewer than 3 steps
  • For purely sequential tasks without parallel benefit

Limitations

  • Requires tasks to be divisible into independent atomic units
  • Performance depends on the granularity of the task decomposition

How it compares

It automates the decomposition and parallel management of tasks that would otherwise require manual step-by-step execution.

Compared to similar skills

distributed-task-orchestrator side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
distributed-task-orchestrator (this skill)17moReviewAdvanced
autonomous-agents106moNo flagsAdvanced
agent-goal-planner26moNo flagsAdvanced
planner16moNo flagsIntermediate

Try saying

Example prompts that trigger this skill in your AI assistant.

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