/eval

Run the PluginEval quality evaluation on a plugin or skill directory.

Usage

/eval — evaluate at standard depth (static + LLM judge) /eval --depth quick — static analysis only (instant)

Process

Step 1: Run Static Analysis (Layer 1)

cd "${CLAUDE_PLUGIN_ROOT}"
uv run plugin-eval score {argument} --depth quick --output json

Parse the JSON output to get composite.score, composite.dimensions, and layers[0].anti_patterns.

Step 2: LLM Judge (Layer 2) — if NOT --depth quick

Dispatch the eval-judge agent with the skill path:

Evaluate the skill at: {resolved_path} Read the SKILL.md file and any references/ files, then score it on all 4 dimensions. Return your scores as JSON.

The judge returns scores for: triggering_accuracy, orchestration_fitness, output_quality, scope_calibration.

Step 3: Compute Final Score

If quick depth: Report the Layer 1 results directly from the CLI output.

If standard depth: Blend Layer 1 and Layer 2 scores.

For each dimension, use these blend weights (Static:Judge):

• triggering_accuracy: 0.375:0.625
• orchestration_fitness: 0.125:0.875
• output_quality: 0.0:1.0 (judge only)
• scope_calibration: 0.353:0.647
• progressive_disclosure: 1.0:0.0 (static only)
• token_efficiency: 0.8:0.2
• robustness: 0.0:1.0 (judge only)
• structural_completeness: 0.9:0.1
• code_template_quality: 0.3:0.7
• ecosystem_coherence: 0.85:0.15

Dimension weights: triggering(0.25), orchestration(0.20), output(0.15), scope(0.12), disclosure(0.10), efficiency(0.06), robustness(0.05), structural(0.03), code_quality(0.02), coherence(0.02)

Final = sum(weight * blended_score) * 100 * anti_pattern_penalty

Step 4: Present Results

## Overall Score: {score}/100 {badge}
## Layer Breakdown
| Layer | Score |
|-------|-------|
## Dimension Scores
| Dimension | Weight | Score | Grade |
|-----------|--------|-------|-------|
## Anti-Patterns Detected
## Recommendations

Badge thresholds: Platinum(90+), Gold(80+), Silver(70+), Bronze(60+)