llm-judge

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Translated

LLM-as-judge methodology for comparing code implementations across repositories. Scores implementations on functionality, security, test quality, overengineering, and dead code using weighted rubrics. Used by /beagle:llm-judge command.

1installs

Sourceexistential-birds/beagle

Added on2026-02-04

NPX Install

npx skill4agent add existential-birds/beagle llm-judge

SKILL.md Content

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LLM Judge Skill

Compare code implementations across 2+ repositories using structured evaluation.

Overview

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

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

Reference Files

File	Purpose
references/fact-schema.md	JSON schema for Phase 1 facts
references/scoring-rubrics.md	Detailed rubrics for each dimension
references/repo-agent.md	Instructions for Phase 1 agents
references/judge-agents.md	Instructions for Phase 2 judges

Scoring Dimensions

Dimension	Default Weight	Evaluates
Functionality	30%	Spec compliance, test pass rate
Security	25%	Vulnerabilities, security patterns
Test Quality	20%	Coverage, DRY, mock boundaries
Overengineering	15%	Unnecessary complexity
Dead Code	10%	Unused code, TODOs

Scoring Scale

Score	Meaning
5	Excellent - Exceeds expectations
4	Good - Meets requirements, minor issues
3	Average - Functional but notable gaps
2	Below Average - Significant issues
1	Poor - Fails basic requirements

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:

Collect scores from all 5 judges

For each repo, compute weighted total:

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

Rank repos by weighted total (descending)
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