do-in-parallel

Execute tasks in parallel across multiple targets with intelligent model selection, independence validation, and quality-focused prompting.

• Purpose - Execute the same task across multiple independent targets in parallel

• Pattern - Supervisor/Orchestrator with parallel dispatch and context isolation

• Output - Multiple solutions, one per target, with aggregated summary

• Quality - Enhanced with Zero-shot CoT, Constitutional AI self-critique, and intelligent model selection

• Efficiency - Dramatic time savings through concurrent execution of independent work

Pattern: Parallel Orchestration with Independence Validation

This command implements a six-phase parallel orchestration pattern:

Phase 1: Parse Input and Identify Targets
                     │
Phase 2: Task Analysis with Zero-shot CoT
         ┌─ Task Type Identification ─────────────────┐
         │ (transformation, analysis, documentation)  │
         ├─ Per-Target Complexity Assessment ─────────┤
         │ (high/medium/low)                          │
         ├─ Independence Validation ──────────────────┤
         │ ⚠️ CRITICAL: Must pass before proceeding   │
         └────────────────────────────────────────────┘
                     │
Phase 3: Model and Agent Selection
         Is task COMPLEX? → Opus
         Is task SIMPLE/MECHANICAL? → Haiku
         Otherwise → Opus (default for balanced work)
                     │
Phase 4: Construct Per-Target Prompts
         [CoT Prefix] + [Task Body] + [Self-Critique Suffix]
         (Same structure for ALL agents, customized per target)
                     │
Phase 5: Parallel Dispatch (ALL agents in SINGLE response)
         ┌─ Agent 1 (target A) ─┐
         ├─ Agent 2 (target B) ─┼─→ Concurrent Execution
         └─ Agent 3 (target C) ─┘
                     │
Phase 6: Collect and Summarize Results
         Aggregate outcomes, report failures, suggest remediation

Usage

# Inferred targets from task description
/do-in-parallel "Apply consistent logging format to src/handlers/user.ts, src/handlers/order.ts, and src/handlers/product.ts"

Advanced Options

# Basic usage with file targets
/do-in-parallel "Simplify error handling to use early returns" \
  --files "src/services/user.ts,src/services/order.ts,src/services/payment.ts"

# With named targets
/do-in-parallel "Generate unit tests achieving 80% coverage" \
  --targets "UserService,OrderService,PaymentService"

# With model override
/do-in-parallel "Security audit for injection vulnerabilities" \
  --files "src/db/queries.ts,src/api/search.ts" \
  --model opus

When to Use

Good use cases:

• Same operation across multiple files (refactoring, formatting)

• Independent transformations (each file stands alone)

• Batch documentation generation (API docs per module)

• Parallel analysis tasks (security audit per component)

• Multi-file code generation (tests per service)

Do NOT use when:

• Only one target → use /launch-sub-agent instead

• Targets have dependencies → use /do-in-steps instead

• Tasks require sequential ordering → use /do-in-steps instead

• Shared state needed between executions → use /do-in-steps instead

• Quality-critical tasks needing comparison → use /do-competitively instead

Context Isolation Best Practices

• Minimal context: Each sub-agent receives only what it needs for its target

• No cross-references: Don't tell Agent A about Agent B's target

• Let them discover: Sub-agents read files to understand local patterns

• File system as truth: Changes are coordinated through the filesystem

Theoretical Foundation

Zero-shot Chain-of-Thought (Kojima et al., 2022)

• "Let's think step by step" improves reasoning by 20-60%

• Applied to each parallel agent independently

• Reference: Large Language Models are Zero-Shot Reasoners

Constitutional AI / Self-Critique (Bai et al., 2022)

• Each agent self-verifies before completing

• Catches issues without coordinator overhead

• Reference: Constitutional AI

Multi-Agent Context Isolation (Multi-agent architecture patterns)

• Fresh context prevents accumulated confusion

• Focused tasks produce better results than context-polluted sessions

• Reference: Multi-Agent Debate (Du et al., 2023)