/launch-sub-agent

name: launch-sub-agent
description: Launch an intelligent sub-agent with automatic model selection based on task complexity, specialized agent matching, Zero-shot CoT reasoning, and mandatory self-critique verification
argument-hint: Task description (e.g., "Implement user authentication" or "Research caching strategies") [--model opus|sonnet|haiku] [--agent <agent-name>] [--output <path>]

launch-sub-agent

<task> Launch 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. </task>

<context> This command 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. </context>

Process

Phase 1: Task Analysis with Zero-shot CoT

Before dispatching, analyze the task systematically. Think through step by step:

Let me analyze this task step by step to determine the optimal configuration:

1. **Task Type Identification**
   "What type of work is being requested?"
   - Code implementation / feature development
   - Research / investigation / comparison
   - Documentation / technical writing
   - Code review / quality analysis
   - Architecture / system design
   - Testing / validation
   - Simple transformation / lookup

2. **Complexity Assessment**
   "How complex is the reasoning required?"
   - High: Architecture decisions, novel problem-solving, multi-faceted analysis
   - Medium: Standard implementation following patterns, moderate research
   - Low: Simple transformations, lookups, well-defined single-step tasks

3. **Output Size Estimation**
   "How extensive is the expected output?"
   - Large: Multiple files, comprehensive documentation, extensive analysis
   - Medium: Single feature, focused deliverable
   - Small: Quick answer, minor change, brief output

4. **Domain Expertise Check**
   "Does this task match a specialized agent profile?"
   - Development: code, implement, feature, endpoint, TDD, tests
   - Research: investigate, compare, evaluate, options, library
   - Documentation: document, README, guide, explain, tutorial
   - Architecture: design, system, structure, scalability
   - Exploration: understand, navigate, find, codebase patterns

Phase 2: Model Selection

Select the optimal model based on task analysis:

| Task Profile | Recommended Model | Rationale | |--------------|-------------------|-----------| | **Complex reasoning** (architecture, design, critical decisions) | `opus` | Maximum reasoning capability | | **Specialized domain** (matches agent profile) | Opus + Specialized Agent | Domain expertise + reasoning power | | **Non-complex but long** (extensive docs, verbose output) | `sonnet[1m]` | Good capability, cost-efficient for length | | **Simple and short** (trivial tasks, quick lookups) | `haiku` | Fast, cost-effective for easy tasks | | **Default** (when uncertain) | `opus` | Optimize for quality over cost |

**Decision Tree:**

Is task COMPLEX (architecture, design, novel problem, critical decision)?
|
+-- YES --> Use Opus (highest capability)
|           |
|           +-- Does it match a specialized domain?
|               +-- YES --> Include specialized agent prompt
|               +-- NO --> Use Opus alone
|
+-- NO --> Is task SIMPLE and SHORT?
           |
           +-- YES --> Use Haiku (fast, cheap)
           |
           +-- NO --> Is output LONG but task not complex?
                      |
                      +-- YES --> Use Sonnet (balanced)
                      |
                      +-- NO --> Use Opus (default)

Phase 3: Specialized Agent Matching

If the task matches a specialized domain, incorporate the relevant agent prompt. Specialized agents provide domain-specific best practices, quality standards, and structured approaches that improve output quality.

**Decision:** Use specialized agent when task clearly benefits from domain expertise. Skip for trivial tasks where specialization adds unnecessary overhead.

**Agents:** Available specialized agents depends on project and plugins installed. Common agents from the `sdd` plugin include: `sdd:developer`, `sdd:researcher`, `sdd:software-architect`, `sdd:tech-lead`, `sdd:team-lead`, `sdd:qa-engineer`, `sdd:code-explorer`, `sdd:business-analyst`. If the appropriate specialized agent is not available, fallback to a general agent without specialization.

**Integration with Model Selection:**

• Specialized agents are combined WITH model selection, not instead of
• Complex task + specialized domain = Opus + Specialized Agent
• Simple task matching domain = Haiku without specialization (overhead not justified)

**Usage:**

1. Read the agent definition 2. Include the agent's instructions in the sub-agent prompt AFTER the CoT prefix 3. Combine with Zero-shot CoT prefix and Critique suffix

Phase 4: Construct Sub-Agent Prompt

Build the sub-agent prompt with these mandatory components:

4.1 Zero-shot Chain-of-Thought Prefix (REQUIRED - MUST BE FIRST)

## Reasoning Approach

Before taking any action, you MUST think through the problem systematically.

Let's approach this step by step:

1. "Let me first understand what is being asked..."
   - What is the core objective?
   - What are the explicit requirements?
   - What constraints must I respect?

2. "Let me break this down into concrete steps..."
   - What are the major components of this task?
   - What order should I tackle them?
   - What dependencies exist between steps?

3. "Let me consider what could go wrong..."
   - What assumptions am I making?
   - What edge cases might exist?
   - What could cause