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: 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: 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: 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) 4.2 Task Body 4.3 Self Critique Suffix (REQUIRED MUST BE LAST) Phase 5: Dispatch Sub Agent Use the Task tool to dispatch with the selected configuration: 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: sdd:software architect Selection: Opus + sdd:software architect agent Dispatch: Task tool with Opus model, sdd: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: sdd:developer Selection: Sonnet + sdd:developer agent (non complex but needs domain expertise) Dispatch: Task tool with Sonnet model, sdd: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: sdd:researcher Selection: Opus + sdd:researcher agent Dispatch: Task tool with Opus model, sdd: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

launch-sub-agent