LLM Judge Skill

Compare code implementations across 2+ repositories using structured evaluation.

Overview

This skill implements a two-phase LLM-as-judge evaluation:

1. Phase 1: Fact Gathering – Parallel agents explore each repo and extract structured facts
2. Phase 2: Judging – Parallel judges score each dimension using consistent rubrics

Reference Files

Scoring Dimensions

Scoring Scale

Phase 1: Spawning Repo Agents

For each repository, spawn a Task agent with:

You are a Phase 1 Repo Agent for the LLM Judge evaluation.

**Your Repo:** $REPO_LABEL at $REPO_PATH
**Spec Document:**
$SPEC_CONTENT

**Instructions:** Read @beagle:llm-judge references/repo-agent.md

Gather facts and return a JSON object following the schema in references/fact-schema.md.

Load @beagle:llm-artifacts-detection for dead code and overengineering analysis.

Return ONLY valid JSON, no markdown or explanations.

Phase 2: Spawning Judge Agents

After all Phase 1 agents complete, spawn 5 judge agents (one per dimension):

You are the $DIMENSION Judge for the LLM Judge evaluation.

**Spec Document:**
$SPEC_CONTENT

**Facts from all repos:**
$ALL_FACTS_JSON

**Instructions:** Read @beagle:llm-judge references/judge-agents.md

Score each repo on $DIMENSION using the rubric in references/scoring-rubrics.md.

Return ONLY valid JSON following the judge output schema.

Aggregation

After Phase 2 completes:

1. Collect scores from all 5 judges
2. For each repo, compute weighted total:

   weighted_total = sum(score[dim] * weight[dim]) / 100
   

3. Rank repos by weighted total (descending)
4. Generate verdict explaining the ranking

Output

Write results to .beagle/llm-judge-report.json and display markdown summary.

Dependencies

• @beagle:llm-artifacts-detection – Reused by repo agents for dead code/overengineering