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parallel-execution

CloudAI-X/claude-workflow/skills/parallel-execution/SKILL.md

Patterns for parallel subagent execution using the Agent tool (formerly Task). Use when coordinating multiple independent tasks, spawning dynamic subagents, or implementing features that can be parallelized.

Skill1.4k starsChanged 9 months ago

What's in it

  1. Parallel Execution Patterns
  2. When to Load
  3. Core Concept
  4. Execution Protocol
  5. Step 1: Identify Parallelizable Tasks
  6. Step 2: Prepare Dynamic Subagent Prompts
  7. Step 3: Launch All Tasks in ONE Message
  8. Step 4: Collect Results
  9. Step 5: Synthesize Results
  10. Dynamic Subagent Patterns
  11. Pattern 1: Task-Based Parallelization
  12. Pattern 2: Directory-Based Parallelization
  13. Pattern 3: Perspective-Based Parallelization
  14. Task List Integration
  15. When to Use Parallel Execution
  16. Performance Benefits
  17. Example: Feature Implementation
  18. Troubleshooting
---
name: parallel-execution
description: Patterns for parallel subagent execution using the Agent tool (formerly Task). Use when coordinating multiple independent tasks, spawning dynamic subagents, or implementing features that can be parallelized.
---

# 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 Agent tool (named `Task` before Claude Code 2.1.63; `Task` still works as an alias). Subagents run in the background by default, so N tasks run concurrently, dramatically reducing total execution time.

**Critical Rule**: ALL Agent calls MUST be in a SINGLE assistant message for true parallelism. If the calls are in separate messages, they launch one after another.

## 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 Agent calls in the SAME assistant message:

```
I'm launching N parallel subagents:

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

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

[Agent 3]
description: "Subagent C - [brief purpose]"
prompt: "[detailed instructions for subagent C]"
```

On Claude Code versions that still run subagents in the foreground by default, add `run_in_background: true` to each call.

### Step 4: Collect Results

Each subagent returns its final result to the parent conversation automatically when it finishes. Wait until every subagent has reported before synthesizing; do not poll, and do not start dependent work early. (The separate `TaskOutput` call is deprecated.)

### 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

Wave 1 - spawn 3 subagents (independent of each other):
- Subagent 1: Implements auth module
- Subagent 2: Creates API endpoints
- Subagent 3: Adds database schema

Wave 2 - after wave 1 has finished (these depend on its output):
- 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
```

## Task List Integration

When using parallel execution, task tracking (`TaskCreate`/`TaskUpdate`, or `TodoWrite` on older versions) 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" }
]

# As each subagent reports back, mark its task 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**:

1. Implement login endpoint
2. Implement registration endpoint
3. Implement password reset endpoint
4. Add authentication middleware
5. Write integration tests

**Parallel execution**:

```
Wave 1 - launching 4 subagents in parallel:

[Agent 1] Login endpoint implementation
[Agent 2] Registration endpoint implementation
[Agent 3] Password reset endpoint implementation
[Agent 4] Auth middleware implementation

[Results arrive as each subagent finishes]

Wave 2 - depends on wave 1:

[Agent 5] Integration test writing

[Synthesize into cohesive implementation]
```

## Troubleshooting

**Tasks running sequentially?**

- Verify ALL Agent calls are in a SINGLE message
- On older Claude Code versions, check `run_in_background: true` is set for each

**Results not available?**

- Results are delivered when each subagent finishes; wait for all of them
- A subagent that was denied a permission may return without finishing its work; check its report

**Conflicts in output?**

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

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