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.

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 this to fail?

4. "Let me verify my approach before proceeding..."
   - Does my plan address all requirements?
   - Is there a simpler approach?
   - Am I following existing patterns?

Work through each step explicitly before implementing.

4.2 Task Body

<task>
{Task description from $ARGUMENTS}
</task>

<constraints>
{Any constraints inferred from the task or conversation context}
</constraints>

<context>
{Relevant context: files, patterns, requirements, codebase information}
</context>

<output>
{Expected deliverable: format, location, structure}
</output>

4.3 Self-Critique Suffix (REQUIRED - MUST BE LAST)

## Self-Critique Loop (MANDATORY)

Before completing, you MUST verify your work. Submitting unverified work is UNACCEPTABLE.

### 1. Generate 5 Verification Questions

Create 5 questions specific to this task that test correctness and completeness. There example questions:

| # | Verification Question | Why This Matters |
|---|----------------------|------------------|
| 1 | Does my solution fully address ALL stated requirements? | Partial solutions = failed task |
| 2 | Have I verified every assumption against available evidence? | Unverified assumptions = potential failures |
| 3 | Are there edge cases or error scenarios I haven't handled? | Edge cases cause production issues |
| 4 | Does my solution follow existing patterns in the codebase? | Pattern violations create maintenance debt |
| 5 | Is my solution clear enough for someone else to understand and use? | Unclear output reduces value |

### 2. Answer Each Question with Evidence

For each question, examine your solution and provide specific evidence:

[Q1] Requirements Coverage:
- Requirement 1: [COVERED/MISSING] - [specific evidence from solution]
- Requirement 2: [COVERED/MISSING] - [specific evidence from solution]
- Gap analysis: [any gaps identified]

[Q2] Assumption Verification:
- Assumption 1: [assumption made] - [VERIFIED/UNVERIFIED] - [evidence]
- Assumption 2: [assumption made] - [VERIFIED/UNVERIFIED] - [evidence]

[Q3] Edge Case Analysis:
- Edge case 1: [scenario] - [HANDLED/UNHANDLED] - [how]
- Edge case 2: [scenario] - [HANDLED/UNHANDLED] - [how]

[Q4] Pattern Adherence:
- Pattern 1: [pattern name] - [FOLLOWED/DEVIATED] - [evidence]
- Pattern 2: [pattern name] - [FOLLOWED/DEVIATED] - [evidence]

[Q5] Clarity Assessment:
- Is the solution well-organized? [YES/NO]
- Are complex parts explained? [YES/NO]
- Could someone else use this immediately? [YES/NO]

### 3. Revise If Needed

If ANY verification question reveals a gap:
1. **STOP** - Do not submit incomplete work
2. **FIX** - Address the specific gap identified
3. **RE-VERIFY** - Confirm the fix resolves the issue
4. **DOCUMENT** - Note what was changed and why

CRITICAL: Do not submit until ALL verification questions have satisfactory answers with evidence.

Phase 5: Dispatch Sub-Agent

Use the Task tool to dispatch with the selected configuration:

Use Task tool:
- description: "Sub-agent: {brief task summary}"
- prompt: {constructed prompt with CoT prefix + task + critique suffix}
- model: {selected model - opus/sonnet/haiku}

Context isolation reminder: Pass only context relevant to this specific task. Do not pass entire conversation history.

Examples

Example 1: Complex Architecture Task (Opus)

Input: /launch-sub-agent Design a caching strategy for our API that handles 10k requests/second

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

---

Example 2: Simple Documentation Update (Haiku)

Input: /launch-sub-agent Update the README to add --verbose flag to CLI options

Analysis:

• Task type: Documentation (simple edit)
• Complexity: Low (single file, well-defined)
• Output size: Small (one section)
• Domain match: None needed (too simple)

Selection: Haiku (fast, cheap, sufficient for task)

Dispatch: Task tool with Haiku model, basic CoT prefix, basic critique suffix

---

Example 3: Moderate Implementation (Sonnet + Developer)

Input: /launch-sub-agent Implement pagination for /users endpoint following patterns in /products

Analysis:

• Task type: Code implementation
• Complexity: Medium (follow existing patterns)
• Output size: Medium (implementation + tests)
• Domain match: developer

Selection: Sonnet + developer agent (non-complex but needs domain expertise)

Dispatch: Task tool with Sonnet model, developer prompt, CoT prefix, critique suffix

---

Example 4: Research Task (Opus + Researcher)

Input: /launch-sub-agent Research authentication options for mobile app - evaluate OAuth2, SAML, passwordless

Analysis:

• Task type: Research / comparison
• Complexity: High (comparative analysis, recommendations)
• Output size: Large (comprehensive research)
• Domain match: researcher

Selection: Opus + researcher agent

Dispatch: Task tool with Opus model, researcher prompt, CoT prefix, critique suffix

Best Practices

Context Isolation

• Pass only context relevant to the specific task
• Avoid passing entire conversation history
• Let sub-agent discover codebase patterns through tools
• Use file paths and references rather than embedding large content

Model Selection

• When in doubt, use Opus (quality over cost)
• Use Haiku only for truly trivial tasks
• Use Sonnet for "grunt work" - needs capability but not genius
• Production code always deserves Opus

Specialized Agents

• Use when domain expertise clearly improves quality
• Combine with CoT and critique patterns
• Don't force specialization on general tasks

Quality Gates

• Self-critique loop is non-negotiable
• Sub-agents must answer verification questions before completing
• Review sub-agent output before accepting