launch-sub-agent
This command launches a focused sub-agent to execute the provided task. Analyze the task to intelligently select the optimal model and agent configuration, then dispatch a sub-agent with Zero-shot Chain-of-Thought reasoning at the beginning and mandatory self-critique verification at the end. It implements the **Supervisor/Orchestrator pattern** from multi-agent architectures where you (the orches
Overview
launch-sub-agent
This command launches a focused sub-agent to execute the provided task. Analyze the task to intelligently select the optimal model and agent configuration, then dispatch a sub-agent with Zero-shot Chain-of-Thought reasoning at the beginning and mandatory self-critique verification at the end. It implements the Supervisor/Orchestrator pattern from multi-agent architectures where you (the orchestrator) dispatch focused sub-agents with isolated context. The primary benefit is context isolation - each sub-agent operates in a clean context window focused on its specific task without accumulated context pollution.
Usage
`/launch-sub-agent Design a caching strategy for our API that handles 10k requests/second`
Agent output:
**Analysis:**
- Task type: Architecture / design
- Complexity: High (performance requirements, system design)
- Output size: Medium (design document)
- Domain match: software-architect
**Selection:** Opus + software-architect agent
**Dispatch:** Task tool with Opus model, software-architect prompt, CoT prefix, critique suffix
Advanced Options
Explicit Model Override
When you know the appropriate model tier, override automatic selection:
/launch-sub-agent "Task description" --model opus|sonnet|haiku
Explicit Agent Selection
Force use of a specific specialized agent:
/launch-sub-agent "Task description" --agent developer|researcher|software-architect|tech-writer|business-analyst|code-explorer|tech-lead|security-auditor
Output Location
Specify where results should be written:
/launch-sub-agent "Task description" --output path/to/output.md
Combined Options
/launch-sub-agent "Implement the payment flow" --agent developer --model opus --output src/services/payment.ts
Core design principles
- Context isolation: Sub-agents operate with fresh context, preventing confirmation bias and attention scarcity
- Intelligent model selection: Match model capability to task complexity for optimal quality/cost tradeoff
- Specialized agent routing: Domain experts handle domain-specific tasks
- Zero-shot CoT: Systematic reasoning at task start improves quality by 20-60%
- Self-critique: Verification loop catches 40-60% of issues before delivery
When to use this command
- Tasks that benefit from fresh, focused context
- Tasks where model selection matters (quality vs. cost tradeoffs)
- Delegating work while maintaining quality gates
- Single, well-defined tasks with clear deliverables
When NOT to use
- Simple tasks you can complete directly (overhead not justified)
- Tasks requiring conversation history or accumulated session context
- Exploratory work where scope is undefined
Theoretical Foundation
Zero-shot Chain-of-Thought (Kojima et al., 2022)
- Adding "Let's think step by step" improves reasoning by 20-60%
- Explicit reasoning steps reduce errors and catch edge cases
- Reference: Large Language Models are Zero-Shot Reasoners
Constitutional AI / Self-Critique (Bai et al., 2022)
- Self-critique loops catch 40-60% of issues before delivery
- Verification questions force explicit quality checking
- Reference: Constitutional AI
Multi-Agent Context Isolation (Multi-agent architecture patterns)
- Fresh context prevents accumulated confusion and attention scarcity
- Focused tasks produce better results than context-polluted sessions
- Supervisor pattern enables quality gates between delegated work