parallel-execution
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.
How do I install this agent skill?
npx skills add https://github.com/cloudai-x/claude-workflow-v2 --skill parallel-executionIs this agent skill safe to install?
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The skill provides instructions and templates for parallel subagent orchestration using the Task tool. It demonstrates how to spawn multiple agents to handle independent tasks simultaneously. The analysis identified a potential surface for indirect prompt injection because the suggested prompt templates for subagents ingest external project context and task descriptions without specifying sanitization or boundary markers to isolate untrusted data.
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What does this agent skill do?
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:
- Implement login endpoint
- Implement registration endpoint
- Implement password reset endpoint
- Add authentication middleware
- 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: trueis 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
How can the creator link this skill?
Add the canonical catalog link to the repository README so users can inspect current installs and available audits. The publishing guide covers the complete discovery path.
<a href="https://skillzs.dev/skills/cloudai-x/claude-workflow-v2/parallel-execution">View parallel-execution on skillZs</a>