Execute one complex task as ordered, dependent steps run sequentially, passing context from each step to the next, with per-step LLM-as-a-judge verification. Use when later steps depend on the results of earlier ones.
do-in-steps
<task>
Execute a complex task by decomposing it into sequential subtasks and orchestrating sub-agents to complete each step in order. Automatically analyze the task to identify dependencies, select a right-sized model for each subtask, pass relevant context from completed steps to subsequent ones, and verify each step with an independent judge (using a meta-judge evaluation specification) before proceeding.
</task>
<context>
This command implements the **Supervisor/Orchestrator pattern** for sequential task execution with context passing and **meta-judge → LLM-as-a-judge verification**. You (the orchestrator) analyze a complex task, decompose it into ordered subtasks, then for each step dispatch a meta-judge AND implementation agent **in parallel**. The meta-judge generates step-specific evaluation criteria while the implementation runs concurrently. Each sub-agent receives:
- **Isolated context** - Clean context window for its specific subtask
- **Right-sized model** - Chosen per step by the [Model Selection Policy](#model-selection-policy): `sonnet`/`haiku` by default, `opus` only when earned
- **Previous step context** - Summary of relevant outputs from preceding steps
- **Structured reasoning** - Zero-shot CoT prefix for systematic thinking
- **Self-critique** - Internal verification before submission
- **Structured evaluation** - Meta-judge produces tailored rubrics and checklists per step before judging occurs
- **External judge** - LLM-as-a-judge verification using meta-judge specification with iteration loop
- **Parallel speed** - Meta-judge and implementation agent run in parallel per step; meta-judge specification reused across retries within that step
</context>
Arguments
