/eval
Evaluate a plugin or skill for quality
$ npx -y skills add wshobson/agents --agent claude-code
Command definition
eval.mddescription: Evaluate a plugin or skill for quality
argument-hint: <path> [--depth quick|standard]
Run the PluginEval quality evaluation on a plugin or skill directory.
Usage
/eval <path> — evaluate at standard depth (static + LLM judge) /eval <path> --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 jsonParse 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
## RecommendationsBadge thresholds: Platinum(90+), Gold(80+), Silver(70+), Bronze(60+)
Read more
description: Evaluate a plugin or skill for quality argument-hint: <path> [--depth quick|standard]
Run the PluginEval quality evaluation on a plugin or skill directory.
Usage
/eval <path> — evaluate at standard depth (static + LLM judge) /eval <path> --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 jsonParse 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
## RecommendationsBadge thresholds: Platinum(90+), Gold(80+), Silver(70+), Bronze(60+)
Use one collection of plugins, agents, skills, and commands across Claude Code, OpenAI Codex CLI, Cursor, OpenCode, Antigravity CLI, GitHub Copilot, and Pi. A harness is the coding tool that loads and runs the components.
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