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/do-in-steps
Execute complex tasks through sequential sub-agent orchestration with intelligent model selection, meta-judge → LLM-as-a-judge verification.
Claude Code Knowledge Pack7/10/2026
Overview
/do-in-steps
Execute complex tasks through sequential sub-agent orchestration with intelligent model selection, meta-judge → LLM-as-a-judge verification.
- Purpose - Execute dependent tasks sequentially where each step builds on previous outputs
- Pattern - Supervisor/Orchestrator with sequential dispatch, parallel meta-judge + implementation, judge verification, and iteration loop
- Output - Comprehensive report with all step results, judge scores, and integration summary
- Key Benefit - Prevents context pollution while ensuring quality through independent, specification-driven verification
Quality Assurance
Three-layer verification: self-critique (internal) + meta-judge evaluation spec (per step) + LLM-as-a-judge (external) with iteration until passing
Pattern: Sequential Orchestration with Meta-Judge and Judge Verification
Phase 1: Task Analysis and Decomposition
Task → Identify Dependencies → Define Step Boundaries
│
Phase 2: Model Selection
For each step: Assess Complexity + Scope + Risk → Select Model
│
Phase 3: Sequential Execution with Parallel Meta-Judge + Judge Verification
┌──────────────────────────────────────────────────────────────────────┐
│ For each Step N: │
│ │
│ ┌─────────────┐ │
│ │ Meta-Judge │──┐ (parallel) │
│ │ (sadd:meta- │ │ │
│ │ judge) │ │ ┌──────────┐ ┌──────────────────┐ │
│ └─────────────┘ ├──▶│ Judge │────▶│ Parse Verdict │ │
│ ┌─────────────┐ │ │ (sadd: │ │ (Orchestrator) │ │
│ │ Implementer │──┘ │ judge) │ └──────────────────┘ │
│ │ (Sub-agent) │ └──────────┘ │ │
│ └─────────────┘ ▼ │
│ ▲ ┌───────────────────────┐ │
│ │ │ PASS (≥4.0)? │ │
│ │ │ ├─ YES → Next Step │ │
│ │ │ ├─ ≥3.0 + low-pri │ │
│ │ │ │ issues → PASS │ │
│ │ │ └─ NO → Retry? │ │
│ │ │ ├─ <3 retries → │ │
│ │ │ │ Retry (reuse │ │
│ │ │ │ meta-judge │ │
│ │ │ │ spec) │ │
│ │ │ └─ ≥3 → Escalate │ │
│ │ └───────────────────────┘ │
│ │ │ │
│ └──────────── feedback ─────────────────┘ │
└──────────────────────────────────────────────────────────────────────┘
Step 1 → Judge ✓ → Step 2 → Judge ✓ → Step 3 → Judge ✓ → ...
(prev step summaries flow forward as context)
│
Phase 4: Final Summary and Report
Aggregate results, judge scores, meta-judge specs, files modified, decisions made
Usage
# Interface change with consumer updates
/do-in-steps "Change return type of UserService.getUser() from User to UserDTO and update all consumers"
# Feature addition across layers
/do-in-steps "Add email notification capability to the order processing system"
# Multi-file refactoring with breaking changes
/do-in-steps "Rename 'userId' to 'accountId' across the codebase - affects interfaces, implementations, and callers"
When to Use
Good use cases:
- Changes that cascade through multiple files/layers
- Interface modifications with consumers to update
- Feature additions spanning multiple components
- Refactoring with dependency chains
- Any task where "Step N depends on Step N-1"
Do NOT use when:
- Independent tasks that could run in parallel → use
/do-in-parallel - Single-step tasks → use
/launch-sub-agent - Tasks needing exploration before commitment → use
/tree-of-thoughts - High-stakes tasks needing multiple approaches → use
/do-competitively
Quality Enhancement Techniques
| Phase | Technique | Benefit |
|---|---|---|
| Phase 3 | Self-Critique | Implementation agents verify own work before submission, catching 40-60% of issues |
| Phase 3 | Meta-Judge (sadd:meta-judge) | Generates step-specific evaluation rubrics, checklists, and scoring criteria in parallel with implementation |
| Phase 3 | LLM-as-a-Judge (sadd:judge) | Independent judge evaluates each step against meta-judge specification; CLAUDE_PLUGIN_ROOT passed to both agents |
| Phase 3 | Iteration Loop | Failed steps retry with judge feedback until passing (max 3 retries) or escalate; retries reuse same meta-judge spec |
| Phase 3 | Context Passing | Previous step summaries (files modified, key changes, decisions) flow to next step's implementation agent; max ~200 words per step |
Theoretical Foundation
- Chain-of-Thought Prompting (Wei et al., 2022) - Step-by-step reasoning improves accuracy
- Constitutional AI (Bai et al., 2022) - Self-critique loops before submission
- LLM-as-a-Judge (Zheng et al., 2023) - Independent evaluation with structured rubrics
- Multi-Agent Debate (Du et al., 2023) - Fresh context prevents accumulated confusion