Parallel Execution

Parallel Execution Skill

Manage parallel work item execution and coordination.

When to Use

• Running multiple work items concurrently
• Monitoring status of background tasks
• Coordinating non-blocking patterns
• Operating in L3 autonomy mode

Parallel Execution Rules

Independence Check

Before parallel execution, verify:

• No data dependencies between items
• No file conflicts (same file modified)
• No sequential requirements (check BlockedBy)

Maximum Concurrency

• Default: 5 parallel items
• Configurable via autonomy.max_parallel
• Respect system resource limits

Non-Blocking Patterns

Launch Background Task (Claude Code)

Task tool with run_in_background: true

• Returns immediately with task ID
• Continue with other work
• Check status periodically

Monitor Status

TaskOutput with task_id, block: false

• Non-blocking status check
• Returns current progress

Wait for Completion

TaskOutput with task_id, block: true

• Blocks until task completes
• Returns final result

Queue-Based Execution

Use selected backend queue from plan-work-items / run-work-items contract:

• Project/global tracking config decides provider
• .agent/queue/ is fallback and offline-safe mode

Identify Parallel Items

From selected backend queue, items can run in parallel if:

• Neither has BlockedBy referencing the other
• They modify different files/components
• They're independent features

Launch Parallel Work

# Example: two independent items
# 001-pending-frontend.md (no blockers)
# 002-pending-backend.md (no blockers)
# Can run in parallel

Track Status in Queue

Update status in backend-native form:

• GitHub: labels/state/close status
• file-based: filename status prefixes

For file-based mode, update status in filenames:

• pending → in_progress when started
• in_progress → completed when done
• in_progress → blocked if dependency issue found

Prioritization

1. Items blocking others (critical path)
2. Independent items (parallelizable)
3. Items with most blockers (finish last)

Error Handling

L3 (continue_on_error: true):

• Log failed items
• Continue with remaining items
• Report all failures at end

L1/L2:

• Stop on first failure
• Report error to user
• Await guidance

Coordination Patterns

Fork-Join Pattern

1. Identify independent work items
2. Launch all in parallel (Task tool with run_in_background)
3. Wait for all to complete
4. Aggregate results

Pipeline Pattern

1. Item A produces output
2. Item B blocked by A
3. Execute sequentially
4. Respect dependency metadata in selected backend

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