Launch Sub Agent
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
- Skill ID
- neolabhq/context-engineering-kit/launch-sub-agent
- Publisher
- neolabhq
- Repository
- context-engineering-kit
- Installs
- 0
- Files
- 1
- Synced
- Sep 23, 2026
Open any RiverX project, open the Skills panel in the chat, and search for this identifier. The files are fetched from the source repository at install time.
neolabhq/context-engineering-kit/launch-sub-agentInstalls these files- SKILL.md
What this skill tells the agent
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 patternsPhase 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:code-explorer, sdd:business-analyst, sdd:code-reviewer, sdd:tech-writer. 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:
- Read the agent definition
- Include the agent's instructions in the sub-agent prompt AFTER the CoT prefix
- 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:More from neolabhq
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