feat(plugin-eval): add Claude Code plugin shell (commands, agents, skill)

Add plugin.json, three commands (eval/compare/certify), eval-orchestrator agent,
and evaluation-methodology skill to wire plugin-eval into the claude-agents ecosystem.

plugins/plugin-eval/.claude-plugin/plugin.json

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+{
+  "name": "plugin-eval"
+}

plugins/plugin-eval/agents/eval-orchestrator.md

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+---
+name: eval-orchestrator
+description: "Orchestrates plugin quality evaluation. Use PROACTIVELY when evaluating, scoring, or certifying plugin quality."
+model: opus
+---
+
+You are the PluginEval orchestrator. You coordinate quality evaluation of Claude Code plugins.
+
+## Your Role
+
+When asked to evaluate a plugin or skill:
+
+1. Identify the target path
+2. Determine the appropriate depth (quick for CI, standard for dev, deep for certification)
+3. Run `plugin-eval` CLI with the right flags
+4. Interpret the results and provide actionable recommendations
+
+## Tools Available
+
+Run the CLI:
+```bash
+cd /Users/wshobson/workspace/claude-agents/plugins/plugin-eval
+uv run plugin-eval score <path> --depth <level> --output markdown
+```
+
+## Interpreting Results
+
+- **Platinum (90+, Elo 1600+):** Reference quality. Highlight what makes it exceptional.
+- **Gold (80+, Elo 1500+):** Production ready. Note 1-2 areas for improvement.
+- **Silver (70+, Elo 1400+):** Functional but needs work. Prioritize recommendations.
+- **Bronze (60+):** Minimum viable. Significant improvements needed.
+- **No badge (<60):** Below standard. Identify critical issues.
+
+Focus recommendations on the lowest-scoring dimensions and any detected anti-patterns.

plugins/plugin-eval/commands/certify.md

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+---
+description: Full quality certification with badge
+argument-hint: <path>
+---
+
+Run the complete PluginEval certification pipeline (all three layers + Elo ranking) and assign a quality badge.
+
+This takes 15-20 minutes and uses your Max plan for all LLM calls.
+
+## Running
+
+```bash
+cd plugins/plugin-eval
+uv run plugin-eval certify {argument} --output markdown
+```

plugins/plugin-eval/commands/compare.md

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+---
+description: Compare two skills head-to-head
+argument-hint: <skill-a> <skill-b>
+---
+
+Run a pairwise comparison between two skills and report which is better on each quality dimension.
+
+## Running
+
+```bash
+cd plugins/plugin-eval
+uv run plugin-eval compare {argument}
+```

plugins/plugin-eval/commands/eval.md

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+---
+description: Evaluate a plugin or skill for quality
+argument-hint: <path> [--depth quick|standard|deep]
+---
+
+Run the PluginEval quality evaluation on a plugin or skill directory.
+
+## Usage
+
+/eval <path> — evaluate at standard depth (Layers 1+2)
+/eval <path> --depth quick — static analysis only (instant)
+/eval <path> --depth deep — full evaluation with Monte Carlo (15-20 min)
+
+## What It Does
+
+1. Parses the plugin/skill structure (frontmatter, sections, references)
+2. Runs static analysis for anti-patterns and structural quality
+3. At standard+ depth: LLM judge assesses triggering, orchestration, output quality, scope
+4. At deep+ depth: Monte Carlo simulation measures statistical reliability
+5. Produces a composite score (0-100), dimension breakdown, and quality badge
+
+## Running
+
+```bash
+cd plugins/plugin-eval
+uv run plugin-eval score {argument} --depth standard --output markdown
+```

plugins/plugin-eval/skills/evaluation-methodology/SKILL.md

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+---
+name: evaluation-methodology
+description: "PluginEval quality methodology — dimensions, rubrics, statistical methods. Use when understanding how plugin quality is measured or when interpreting evaluation results."
+---
+
+# Evaluation Methodology
+
+## Quality Dimensions (Ranked by Weight)
+
+1. **Triggering accuracy (0.25)** — Does the skill fire when it should?
+2. **Orchestration fitness (0.20)** — Works as a worker in agent→skill hierarchy?
+3. **Output quality (0.15)** — Consistent, correct results?
+4. **Scope calibration (0.12)** — Right depth and breadth?
+5. **Progressive disclosure (0.10)** — SKILL.md lean, depth in refs?
+6. **Token efficiency (0.06)** — Minimal context waste?
+7. **Robustness (0.05)** — Handles edge cases?
+8. **Structural completeness (0.03)** — Right sections present?
+9. **Code template quality (0.02)** — Working examples?
+10. **Ecosystem coherence (0.02)** — Cross-references, no duplication?
+
+## Three Evaluation Layers
+
+- **Layer 1 (Static):** Deterministic analysis, <2 seconds, no LLM
+- **Layer 2 (LLM Judge):** G-Eval style rubric assessment via Agent SDK
+- **Layer 3 (Monte Carlo):** N simulated invocations with statistical CIs
+
+## Statistical Methods
+
+- **Wilson score interval:** Triggering activation rate CIs
+- **Bootstrap resampling (1000x):** Output quality CIs
+- **Clopper-Pearson exact:** Failure rate CIs
+- **Cohen's kappa:** Inter-judge agreement
+- **Elo/Bradley-Terry:** Pairwise ranking against gold corpus
+
+## Quality Badges
+
+| Badge | Composite | Elo | Meaning |
+|-------|-----------|-----|---------|
+| Platinum | 90+ | 1600+ | Reference quality |
+| Gold | 80+ | 1500+ | Production ready |
+| Silver | 70+ | 1400+ | Functional, needs improvement |
+| Bronze | 60+ | 1300+ | Minimum viable |