/judge
Launch a meta-judge then a judge sub-agent to evaluate results produced in the current conversation
$ npx -y skills add NeoLabHQ/context-engineering-kit --skill judge --agent claude-codeHow 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.mdname: 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 |
|-------------|---------|----------------|----------------|
|Read more
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 |
|-------------|---------|----------------|----------------|
|A hand-crafted collection of advanced context engineering techniques and patterns with minimal token footprint, focused on improving agent result quality and predictability.
Repo: NeoLabHQ/context-engineering-kit
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Open skill - /create-agent
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Open skill - /create-command
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Create and configure git hooks with intelligent project analysis, suggestions, and automated testing
Open skill

