llm-judge

name	llm-judge
description	Use when comparing two or more code implementations against a spec or requirements doc. Triggers on "which repo is better", "compare these implementations", "evaluate both solutions", "rank these codebases", or "judge which approach wins". Also covers choosing between competing PRs or vendor submissions solving the same problem. Does NOT review a single codebase for quality — use code review skills instead. Does NOT evaluate strategy docs — use strategy-review. Requires a spec file and 2+ repo paths.
disable-model-invocation	true

LLM Judge

Compare code implementations across multiple repositories using structured evaluation.

Usage

llm-judge <spec> <repo1> <repo2> [repo3...] [--labels=...] [--weights=...] [--branch=...]

Arguments

Argument	Required	Description
spec	Yes	Path to spec/requirements document
repos	Yes	2+ paths to repositories to compare
--labels	No	Comma-separated labels (default: directory names)
--weights	No	Override weights, e.g. functionality:40,security:30
--branch	No	Branch to compare against main (default: main)

Workflow

1. Parse $ARGUMENTS into spec_path, repo_paths, labels, weights, and branch.
2. Validate the spec file, each repo path, and the minimum repo count.
3. Read the spec document into memory.
4. Load this skill and the supporting reference files.
5. Gather facts per repository (one Phase 1 unit per repo) — facts only, no scoring.
6. Validate the repo-agent JSON results before proceeding.
7. Score each dimension (one Phase 2 unit per dimension).
8. Aggregate scores, compute weighted totals, rank repos, and write the report.
9. Display the markdown summary and verify the JSON report.

Hard gates

Sequenced workflow: do not start the next phase until the current gate passes. Each pass condition must be checkable (file on disk, non-empty content, or json.load succeeds)—not “I reviewed internally.”

Gate	Pass condition	Unblocks
A — Inputs	spec_path is a readable file and non-empty; len(repo_paths) ≥ 2; each path contains .git.	Phase 1 repo agents
B — Phase 1 facts	For each repo agent output: stdin/stdout parses as JSON; required keys/shape match references/fact-schema.md.	Phase 2 judge agents
C — Phase 2 scores	Five judge outputs (one per dimension) each parse as JSON; each includes a score (and justification) for every repo label.	Aggregation
D — Report file	.beagle/llm-judge-report.json exists; python3 -c "import json; json.load(open('.beagle/llm-judge-report.json'))" exits 0.	Markdown summary to the user
E — Consistency	Summary table and verdict use the same labels, weights, and per-dimension scores as the JSON report.	Mark task complete

Parallelism is allowed within a phase (all Phase 1 tasks together; all Phase 2 tasks together), but Phase 2 must not start until Gate B passes, and the user-visible summary must not precede Gate D.

Command Workflow

Step 1: Parse Arguments

Parse $ARGUMENTS to extract:

• spec_path: first positional argument
• repo_paths: remaining positional arguments (must be 2+)
• labels: from --labels or derived from directory names
• weights: from --weights or defaults
• branch: from --branch or main

Default Weights:

{
  "functionality": 30,
  "security": 25,
  "tests": 20,
  "overengineering": 15,
  "dead_code": 10
}

Step 2: Validate Inputs

[ -f "$SPEC_PATH" ] || { echo "Error: Spec file not found: $SPEC_PATH"; exit 1; }

for repo in "${REPO_PATHS[@]}"; do
  [ -d "$repo/.git" ] || { echo "Error: Not a git repository: $repo"; exit 1; }
done

[ ${#REPO_PATHS[@]} -ge 2 ] || { echo "Error: Need at least 2 repositories to compare"; exit 1; }

Step 3: Read Spec Document

SPEC_CONTENT=$(cat "$SPEC_PATH") || { echo "Error: Failed to read spec file: $SPEC_PATH"; exit 1; }
[ -z "$SPEC_CONTENT" ] && { echo "Error: Spec file is empty: $SPEC_PATH"; exit 1; }

Step 4: Load the Skill

Load this llm-judge skill and its reference files into context.

Step 5: Phase 1 - Gather Facts Per Repo

If the agent supports subagents, dispatch one Phase 1 repo agent per repository in parallel; otherwise run the same fact-gathering steps sequentially, one repo at a time — the output is identical either way. Give each unit this brief:

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

**Your Repo:** $LABEL at $REPO_PATH

**Spec Document:**
$SPEC_CONTENT

**Instructions:**
1. Load the **llm-judge** skill's references/repo-agent.md for detailed instructions
2. Follow references/fact-schema.md for the output format
3. Load the **llm-artifacts-detection** skill ([../../../beagle-core/skills/llm-artifacts-detection/SKILL.md](../../../beagle-core/skills/llm-artifacts-detection/SKILL.md), if available) for dead-code/overengineering analysis

Explore the repository and gather facts. Return ONLY valid JSON following the fact schema.

Do NOT score or judge. Only gather facts.

Collect all repo outputs into ALL_FACTS.

Step 6: Validate Phase 1 Results

echo "$FACTS" | python3 -c "import json,sys; json.load(sys.stdin)" 2>/dev/null || { echo "Error: Invalid JSON from $LABEL"; exit 1; }

Step 7: Phase 2 - Score Per Dimension

If the agent supports subagents, dispatch one judge agent per dimension (five total) in parallel; otherwise score each dimension sequentially — identical output. Give each unit this brief:

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

**Spec Document:**
$SPEC_CONTENT

**Facts from all repos:**
$ALL_FACTS_JSON

**Instructions:**
1. Load the **llm-judge** skill's references/judge-agents.md for detailed instructions
2. Follow references/scoring-rubrics.md for the $DIMENSION rubric

Score each repo on $DIMENSION. Return ONLY valid JSON with scores and justifications.

Step 8: Aggregate Scores

for repo_label in labels:
    scores[repo_label] = {}
    for dimension in dimensions:
        scores[repo_label][dimension] = judge_outputs[dimension]['scores'][repo_label]

    weighted_total = sum(
        scores[repo_label][dim]['score'] * weights[dim] / 100
        for dim in dimensions
    )
    scores[repo_label]['weighted_total'] = round(weighted_total, 2)

ranking = sorted(labels, key=lambda l: scores[l]['weighted_total'], reverse=True)

Step 9: Generate Verdict

Name the winner, explain why they won, and note any close calls or trade-offs.

Step 10: Write JSON Report

mkdir -p .beagle

Write .beagle/llm-judge-report.json with version, timestamp, repo metadata, weights, scores, ranking, and verdict.

Step 11: Display Summary

Render a markdown summary with the scores table, ranking, verdict, and detailed justifications.

Step 12: Verification

python3 -c "import json; json.load(open('.beagle/llm-judge-report.json'))" && echo "Valid report"

Output Shape

The generated report should include:

• repo labels and paths
• per-dimension scores and justifications
• weighted totals and ranking
• a verdict explaining the winner

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 Model

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: Gathering Facts Per Repo

For each repository (in parallel via subagents if supported, otherwise sequentially), run a fact-gathering unit 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:** Follow the **llm-judge** skill's references/repo-agent.md

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

Load the **llm-artifacts-detection** skill ([../../../beagle-core/skills/llm-artifacts-detection/SKILL.md](../../../beagle-core/skills/llm-artifacts-detection/SKILL.md), if available) for dead code and overengineering analysis.

Return ONLY valid JSON, no markdown or explanations.

Collect all repo-agent outputs into ALL_FACTS.

Phase 2: Scoring Per Dimension

After all Phase 1 facts are collected, score the five dimensions (in parallel via subagents if supported, otherwise sequentially), one unit per dimension:

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

**Spec Document:**
$SPEC_CONTENT

**Facts from all repos:**
$ALL_FACTS_JSON

**Instructions:** Follow the **llm-judge** skill's 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

1. Collect the five judge outputs.
2. Compute each repo's weighted total with the configured weights.
3. Rank repos by weighted total in descending order.
4. Generate a verdict that explains the result and any close calls.
5. Write .beagle/llm-judge-report.json.

Output

Display a markdown summary with scores, ranking, verdict, and detailed justifications.

Verification

Before completing (maps to Hard gates D and E):

1. Gate D: .beagle/llm-judge-report.json exists and json.load succeeds.
2. Gate E / completeness: Every repo label has scores for every dimension; each weighted_total equals the sum over dimensions of (score × weight / 100) using the configured weights; markdown summary matches the JSON report.

Rules

• Always validate inputs before proceeding
• Complete all Phase 1 fact-gathering before any Phase 2 scoring (parallel within a phase if subagents are supported, otherwise sequential)
• Run one Phase 2 unit per dimension
• Every score must have a justification
• Write the JSON report before displaying the summary