parallel-execution

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Translated

Patterns for parallel subagent execution using Task tool with run_in_background. Use when coordinating multiple independent tasks, spawning dynamic subagents, or implementing features that can be parallelized.

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Sourcecloudai-x/claude-workflow-v2

Added on2026-03-31

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npx skill4agent add cloudai-x/claude-workflow-v2 parallel-execution

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Parallel Execution Patterns

When to Load

Trigger: Multi-agent tasks, concurrent operations, spawning subagents, parallelizing independent work
Skip: Single-step tasks or sequential workflows with no parallelization opportunity

Core Concept

Parallel execution spawns multiple subagents simultaneously using the Task tool with

run_in_background: true

. This enables N tasks to run concurrently, dramatically reducing total execution time.

Critical Rule: ALL Task calls MUST be in a SINGLE assistant message for true parallelism. If Task calls are in separate messages, they run sequentially.

Execution Protocol

Step 1: Identify Parallelizable Tasks

Before spawning, verify tasks are independent:

No task depends on another's output
Tasks target different files or concerns
Can run simultaneously without conflicts

Step 2: Prepare Dynamic Subagent Prompts

Each subagent receives a custom prompt defining its role:

You are a [ROLE] specialist for this specific task.

Task: [CLEAR DESCRIPTION]

Context:
[RELEVANT CONTEXT ABOUT THE CODEBASE/PROJECT]

Files to work with:
[SPECIFIC FILES OR PATTERNS]

Output format:
[EXPECTED OUTPUT STRUCTURE]

Focus areas:
- [PRIORITY 1]
- [PRIORITY 2]

Step 3: Launch All Tasks in ONE Message

CRITICAL: Make ALL Task calls in the SAME assistant message:

I'm launching N parallel subagents:

[Task 1]
description: "Subagent A - [brief purpose]"
prompt: "[detailed instructions for subagent A]"
run_in_background: true

[Task 2]
description: "Subagent B - [brief purpose]"
prompt: "[detailed instructions for subagent B]"
run_in_background: true

[Task 3]
description: "Subagent C - [brief purpose]"
prompt: "[detailed instructions for subagent C]"
run_in_background: true

Step 4: Retrieve Results with TaskOutput

After launching, retrieve each result:

[Wait for completion, then retrieve]

TaskOutput: task_1_id
TaskOutput: task_2_id
TaskOutput: task_3_id

Step 5: Synthesize Results

Combine all subagent outputs into unified result:

Merge related findings
Resolve conflicts between recommendations
Prioritize by severity/importance
Create actionable summary

Dynamic Subagent Patterns

Pattern 1: Task-Based Parallelization

When you have N tasks to implement, spawn N subagents:

Plan:
1. Implement auth module
2. Create API endpoints
3. Add database schema
4. Write unit tests
5. Update documentation

Spawn 5 subagents (one per task):
- Subagent 1: Implements auth module
- Subagent 2: Creates API endpoints
- Subagent 3: Adds database schema
- Subagent 4: Writes unit tests
- Subagent 5: Updates documentation

Pattern 2: Directory-Based Parallelization

Analyze multiple directories simultaneously:

Directories: src/auth, src/api, src/db

Spawn 3 subagents:
- Subagent 1: Analyzes src/auth
- Subagent 2: Analyzes src/api
- Subagent 3: Analyzes src/db

Pattern 3: Perspective-Based Parallelization

Review from multiple angles simultaneously:

Perspectives: Security, Performance, Testing, Architecture

Spawn 4 subagents:
- Subagent 1: Security review
- Subagent 2: Performance analysis
- Subagent 3: Test coverage review
- Subagent 4: Architecture assessment

TodoWrite Integration

When using parallel execution, TodoWrite behavior differs:

Sequential execution: Only ONE task

in_progress

at a time Parallel execution: MULTIPLE tasks can be

in_progress

simultaneously

# Before launching parallel tasks
todos = [
  { content: "Task A", status: "in_progress" },
  { content: "Task B", status: "in_progress" },
  { content: "Task C", status: "in_progress" },
  { content: "Synthesize results", status: "pending" }
]

# After each TaskOutput retrieval, mark as completed
todos = [
  { content: "Task A", status: "completed" },
  { content: "Task B", status: "completed" },
  { content: "Task C", status: "completed" },
  { content: "Synthesize results", status: "in_progress" }
]

When to Use Parallel Execution

Good candidates:

Multiple independent analyses (code review, security, tests)
Multi-file processing where files are independent
Exploratory tasks with different perspectives
Verification tasks with different checks
Feature implementation with independent components

Avoid parallelization when:

Tasks have dependencies (Task B needs Task A's output)
Sequential workflows are required (commit -> push -> PR)
Tasks modify the same files (risk of conflicts)
Order matters for correctness

Performance Benefits

Approach	5 Tasks @ 30s each	Total Time
Sequential	30s + 30s + 30s + 30s + 30s	~150s
Parallel	All 5 run simultaneously	~30s

Parallel execution is approximately Nx faster where N is the number of independent tasks.

Example: Feature Implementation

User request: "Implement user authentication with login, registration, and password reset"

Orchestrator creates plan:

Implement login endpoint
Implement registration endpoint
Implement password reset endpoint
Add authentication middleware
Write integration tests

Parallel execution:

Launching 5 subagents in parallel:

[Task 1] Login endpoint implementation
[Task 2] Registration endpoint implementation
[Task 3] Password reset endpoint implementation
[Task 4] Auth middleware implementation
[Task 5] Integration test writing

All tasks run simultaneously...

[Collect results via TaskOutput]

[Synthesize into cohesive implementation]

Troubleshooting

Tasks running sequentially?

Verify ALL Task calls are in SINGLE message
Check
```
run_in_background: true
```
is set for each

Results not available?

Use TaskOutput with correct task IDs
Wait for tasks to complete before retrieving

Conflicts in output?

Ensure tasks don't modify same files
Add conflict resolution in synthesis step