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