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/do-and-judge
Execute a single task with implementation sub-agent, meta-judge evaluation criteria, independent judge verification, and automatic retry loop until passing or max retries exceeded.
Claude Code Knowledge Pack7/10/2026
Overview
/do-and-judge
Execute a single task with implementation sub-agent, meta-judge evaluation criteria, independent judge verification, and automatic retry loop until passing or max retries exceeded.
- Purpose - Executes a single task with quality verification and feedback-driven iteration
- Pattern - Meta-Judge + Implement (parallel) → Judge (with meta-judge spec) → Iterate (if needed) → Report
- Output - Verified implementation with judge scores and improvement suggestions
Quality Assurance
Three-layer verification:
- self-critique (internal)
- meta-judge criteria (structured)
- LLM-as-a-judge (external)
Iteration - Retry with judge feedback until passing (score >=4, or >=3.0 with all low-priority issues) or max retries (3)
Pattern: Single-Task Execution with Meta-Judge and Judge Verification
Phase 1: Task Analysis and Model Selection
Complexity + Risk + Scope → Model Selection
│
Phase 2: Parallel Dispatch (single message, 2 tool calls)
┌──────────────────────┬──────────────────────────────┐
│ Meta-Judge (opus) │ Implementation Agent │
│ sadd:meta-judge │ [CoT + Task + Self-Critique] │
│ → Evaluation spec │ → Implementation artifact │
│ (YAML rubrics, │ │
│ checklists, │ │
│ scoring criteria) │ │
└──────────┬───────────┴────────────────────┬─────────┘
│ Waiting for both to complete │
▼ ▼
Phase 3: Dispatch Judge Agent (sadd:judge)
Judge applies meta-judge spec mechanically
│
Phase 4: Parse Verdict and Iterate
├─ PASS (>=4, or >=3.0 all low-priority) → Report Success
└─ FAIL → Retry with Feedback (max 3)
└─ Return to Phase 3 (same meta-judge spec)
│
Phase 5: Final Report or Escalation
Success summary OR escalate to user after max retries
Usage
# Basic usage
/do-and-judge "Refactor the UserService class to use dependency injection"
# Complex implementation
/do-and-judge "Implement rate limiting middleware with configurable limits per endpoint"
# Architecture change
/do-and-judge "Extract validation logic from UserController into separate UserValidator class"
When to Use
Good use cases:
- Single, well-defined tasks that benefit from quality verification
- Changes that should meet a quality threshold before shipping
- Tasks where feedback-driven iteration improves results
- Any implementation where you want an independent quality gate
Do NOT use when:
- Multi-step tasks with dependencies → use
/do-in-stepsinstead - Independent parallel tasks → use
/do-in-parallelinstead - High-stakes tasks needing multiple approaches → use
/do-competitivelyinstead - Simple tasks where verification overhead isn't justified → use
/launch-sub-agentinstead
Key Architecture Details
- Specialized agents: Uses
sadd:meta-judgeandsadd:judgeagent types for evaluation phases - CLAUDE_PLUGIN_ROOT: Must be included in prompts to both meta-judge and judge agents
- Evaluation specification: Meta-judge produces a YAML spec (rubrics, checklists, scoring criteria) that the judge applies mechanically
- Reuse on retries: The same meta-judge evaluation spec is reused across all retry attempts; only the implementation changes
- Pass threshold: Score >=4/5.0, OR >=3.0 with all low-priority issues
Quality Enhancement Techniques
| Phase | Technique | Benefit |
|---|---|---|
| Phase 2 | Meta-Judge | Generates task-specific evaluation criteria (YAML rubrics, checklists, scoring) before judging |
| Phase 2 | Zero-shot CoT | Systematic reasoning improves quality by 20-60% |
| Phase 2 | Self-Critique | Implementation agents verify own work before submission |
| Phase 3 | LLM-as-a-Judge | Judge applies meta-judge specification mechanically, catching blind spots self-critique misses |
| Phase 4 | Feedback Loop | Retry with specific issues until passing or max retries (3) |
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