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.)"

1. 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.)

1. 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:

## 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:

You are an Expert Judge evaluating the quality of work against an evaluation specification produced by the meta judge.

CLAUDE_PLUGIN_ROOT=`${CLAUDE_PLUGIN_ROOT}`

## Work Under Evaluation

[ORIGINAL TASK]
{paste the original request/task}
[/ORIGINAL TASK]

[WORK OUTPUT]
{summary of what was created/modified}
[/WORK OUTPUT]

[FILES INVOLVED]
{list of files with brief descriptions}
[/FILES INVOLVED]

## Evaluation Specification

{meta-judge's evaluation specification YAML}

## Instructions

Follow your full judge process as defined in your agent instructions!

CRITICAL: You must reply with this exact structured evaluation report format in YAML at the START of your response!

CRITICAL: NEVER provide score threshold to judges in any format. Judge MUST not know what threshold for score is, in order to not be biased!!!

Dispatch:

Use Task tool:
  - description: "Judge: Evaluate {brief work summary}"
  - prompt: {judge prompt with exact meta-judge specification YAML}
  - model: opus
  - subagent_type: "sadd:judge"

Phase 4: Process and Present Results

After receiving the judge's evaluation:

1. Validate the evaluation:

• Check that all criteria have scores in valid range (1-5)
• Verify each score has supporting justification with evidence
• Confirm weighted total calculation is correct
• Check for contradictions between justification and score
• Verify self-verification was completed with documented adjustments

1. If validation fails:

• Note the specific issue
• Request clarification or re-evaluation if needed

1. Present results to user:

• Display the full evaluation report
• Highlight the verdict and key findings
• Offer follow-up options:
• Address specific improvements
• Request clarification on any judgment
• Proceed with the work as-is

Scoring Interpretation

Important Guidelines

1. Meta-judge first: Always generate evaluation specification before judging - never skip the meta-judge phase
2. Include CLAUDE_PLUGIN_ROOT: Both meta-judge and judge need the resolved plugin root path
3. Meta-judge YAML: Pass only the meta-judge YAML to the judge, do not modify it
4. Context Isolation: Pass only relevant context to sub-agents - not the entire conversation
5. Justification First: Always require evidence and reasoning BEFORE the score
6. Evidence-Based: Every score must cite specific evidence (file paths, line numbers, quotes)
7. Bias Mitigation: Explicitly warn against length bias, verbosity bias, and authority bias
8. Be Objective: Base assessments on evidence and rubric definitions, not preferences
9. Be Specific: Cite exact locations, not vague observations
10. Be Constructive: Frame criticism as opportunities for improvement with impact context
11. Consider Context: Account for stated constraints, complexity, and requirements
12. Report Confidence: Lower confidence when evidence is ambiguous or criteria unclear
13. Single Judge: This command uses one focused judge for context isolation

Notes

• This is a report-only command - it evaluates but does not modify work
• The meta-judge generates criteria tailored to the specific artifact type and evaluation focus
• The judge operates with fresh context for unbiased assessment
• Scores are calibrated to professional development standards
• Low scores indicate improvement opportunities, not failures
• Use the evaluation to inform next steps and iterations
• Low confidence evaluations may warrant human review