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/judge

Launch a meta-judge then a judge sub-agent to evaluate results produced in the current conversation

From plugin
context-engineering-kit
1.3k134 skills23 agents1 command
Install
$ npx -y skills add NeoLabHQ/context-engineering-kit --skill judge --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/judge

Context preview

The summary Claude sees to decide when to auto-load this skill.

Launch a meta-judge then a judge sub-agent to evaluate results produced in the current conversation

SKILL.md

judge.SKILL.md
name: judge
description: Launch a meta-judge then a judge sub-agent to evaluate results produced in the current conversation
argument-hint: "[evaluation-focus]"

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:**

## 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

```yaml
{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

2. **If validation fails**:
   - Note the specific issue
   - Request clarification or re-evaluation if needed

3. **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

| Score Range | Verdict | Interpretation | Recommendation |
|-------------|---------|----------------|----------------|
|
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Ships withcontext-engineering-kit

A hand-crafted collection of advanced context engineering techniques and patterns with minimal token footprint, focused on improving agent result quality and predictability.

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