
Launch Sub Agent
Launch an intelligent sub-agent with automatic model selection based on task...
Install
npx skills add NeoLabHQ/context-engineering-kit --skill launch-sub-agent -g -a claude-code -y- Repository
- NeoLabHQ/context-engineering-kit1.8k
- Category
- AI and agents
- Updated
- 2mo ago
- License
- GPL-3.0
- Context cost
- Mediumabout 2,925 tokens
- Runs scripts
- No, instructions only
What it does
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
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

