Judge

# Judge Command

<task>
You are a coordinator launching a two-phase evaluation pipeline to assess work produced earlier in this conversation. First, a meta-judge generates tailored evaluation criteria. Then, a judge sub-agent applies those criteria with isolated context, structured scoring, and evidence-based feedback. The evaluation is **report-only** - findings are presented without automatic changes.
</task>

<context>
This command implements the **meta-judge -> LLM-as-Judge** pattern with context isolation:
- **Structured Evaluation**: Meta-judge produces tailored rubrics, checklists, and scoring criteria before judging
- **Context Isolation**: Judge operates with fresh context, preventing confirmation bias from accumulated session state
- **Evidence-Based**: Every score requires specific citations from the work (file locations, line numbers)
- **Multi-Dimensional Rubric**: Generated by meta-judge to match the specific artifact type and evaluation focus
- **Self-Verification**: Dynamic verification questions with documented adjustments
</context>

## Your Workflow

### Phase 1: Context Extraction

Before launching the evaluation pipeline, identify what needs evaluation:

1. **Identify the work to evaluate**:
   - Review conversation history for completed work
   - If arguments provided: Use them to focus on specific aspects
   - If unclear: Ask user "What work should I evaluate? (code changes, analysis, documentation, etc.)"

2. **Extract evaluation context**:
   - Original task or request that prompted the work
   - The actual output/result produced
   - Files created or modified (with brief descriptions)
   - Any constraints, requirements, or acceptance criteria mentioned
   - Artifact type (code, documentation, configuration, etc.)

3. **Provide scope for user**:

   ```
   Evaluation Scope:
   - Original request: [summary]
   - Work produced: [description]
   - Files involved: [list]
   - Artifact type: [code | documentation | configuration | etc.]
   - Evaluation focus: [from arguments or "general quality"]

   Launching meta-judge to generate evaluation criteria...
   ```

**IMPORTANT**: Pass only the extracted context to the sub-agents - not the entire conversation. This prevents context pollution and enables focused assessment.

### Phase 2: Dispatch Meta-Judge

Launch a meta-judge agent to generate an evaluation specification tailored to the specific work being evaluated. The meta-judge will return an evaluation specification YAML containing rubrics, checklists, and scoring criteria.

**Meta-Judge Prompt:**

```markdown
## Task

Generate an evaluation specification yaml for the following evaluation task. You will produce rubrics, checklists, and scoring criteria that a judge agent will use to evaluate the work.

CLAUDE_PLUGIN_ROOT=`${CLAUDE_PLUGIN_ROOT}`

## User Prompt
{Original task or request that prompted the work}

## Context
{Any relevant context about the work being evaluated}
{Evaluation focus from arguments, or "General quality assessment"}

## Artifact Type
{code | documentation | configuration | etc.}

## Instructions
Return only the final evaluation specification YAML in your response.
```

**Dispatch:**

```
Use Task tool:
  - description: "Meta-judge: Generate evaluation criteria for {brief work summary}"
  - prompt: {meta-judge prompt}
  - model: opus
  - subagent_type: "sadd:meta-judge"
```

Wait for the meta-judge to complete before proceeding to Phase 3.

### Phase 3: Dispatch Judge Agent

After the meta-judge completes, extract its evaluation specification YAML and dispatch the judge agent with both the work context and the specification.

CRITICAL: Provide to the judge the EXACT meta-judge evaluation specification YAML. Do not skip, add, modify, shorten, or summarize any text in it!

**Judge Agent Prompt:**

```markdown
You are