PluginEval Core

Self-contained PluginEval quality evaluation engine. Measures 6 dimensions, detects anti-patterns, assigns badges. No external dependencies.

Self-contained quality evaluation for AI agent skills. Measures quality across 6 dimensions, detects anti-patterns, assigns quality badges.

Use When

• Evaluating skill quality before installation
• Checking installed skills for quality issues
• Improving skills to meet quality standards
• Publishing skills to ClawHub with quality badges

Input / Output

Input:

• Skill directory containing SKILL.md
• Optional: --layer1, --layer2, --anti-patterns flags

Output:

json

{
  "skill": "example-skill",
  "score": 87,
  "badge": "Gold",
  "grade": "B+",
  "anti_patterns": []
}

Usage

bash

# Layer 1: Static Analysis
python3 ~/.openclaw/skills/plugineval-core/scripts/eval.py --layer1 <skill-dir>

# Anti-Pattern Detection
python3 ~/.openclaw/skills/plugineval-core/scripts/eval.py --anti-patterns <skill-dir>

# Full Evaluation
python3 ~/.openclaw/skills/plugineval-core/scripts/eval.py <skill-dir>

Quality Dimensions

Dimension	Weight	Measures
Frontmatter Quality	35%	Name, description, trigger
Orchestration Wiring	25%	Input/Output, examples
Progressive Disclosure	15%	Conciseness
Structural Completeness	10%	Headings, troubleshooting
Token Efficiency	6%	Directives, duplication
Ecosystem Coherence	2%	Cross-references

Quality Badges

Badge	Score
Platinum ★★★★★	≥90
Gold ★★★★	≥80
Silver ★★★	≥70
Bronze ★★	≥60
Needs Improvement ★	<60

Anti-Patterns

Pattern	Penalty
OVER_CONSTRAINED	10%
EMPTY_DESCRIPTION	10-50%
MISSING_TRIGGER	15%
BLOATED_SKILL	10%
ORPHAN_REFERENCE	5%
DEAD_CROSS_REF	5%

References

• quality-framework.md
• anti-pattern-catalog.md

Examples

bash

# Evaluate skill
python3 scripts/eval.py --layer1 ~/.openclaw/skills/weather-pollen

# Output:
# [1/6] Frontmatter Quality: 100/100
# [2/6] Orchestration Wiring: 100/100
# ...
# Final: 87 | Badge: Gold ★★★★

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Version: 1.0.0 | License: MIT