model-evaluation-benchmark

# Model Evaluation Benchmark Skill

**Purpose**: Automated reproduction of comprehensive model evaluation benchmarks following the Benchmark Suite V3 reference implementation.

**Auto-activates when**: User requests model benchmarking, comparison evaluation, or performance testing between AI models in agentic workflows.

## Skill Description

This skill orchestrates end-to-end model evaluation benchmarks that measure:

- **Efficiency**: Duration, turns, cost, tool calls
- **Quality**: Code quality scores via reviewer agents
- **Workflow Adherence**: Subagent calls, skills used, workflow step compliance
- **Artifacts**: GitHub issues, PRs, documentation generated

The skill automates the entire benchmark workflow from execution through cleanup, following the v3 reference implementation.

## When to Use

✅ **Use when**:

- Comparing AI models (Opus vs Sonnet, etc.)
- Measuring workflow adherence
- Generating comprehensive benchmark reports
- Need reproducible benchmarking

❌ **Don't use when**:

- Simple code reviews (use `reviewer`)
- Performance profiling (use `optimizer`)
- Architecture decisions (use `architect`)

## Execution Instructions

When this skill is invoked, follow these steps:

### Phase 1: Setup

1. Read `tests/benchmarks/benchmark_suite_v3/BENCHMARK_TASKS.md`
2. Identify models to benchmark (default: Opus 4.5, Sonnet 4.5)
3. Create TodoWrite list with all phases

### Phase 2: Execute Benchmarks

For each task × model:

```bash
cd tests/benchmarks/benchmark_suite_v3
python run_benchmarks.py --model {opus|sonnet} --tasks 1,2,3,4
```

### Phase 3: Analyze Results

1. Read all result files: `.claude/runtime/benchmarks/suite_v3/*/result.json`
2. Launch parallel Task tool calls with `subagent_type="reviewer"` to:
   - Analyze trace logs for tool/agent/skill usage
   - Score code quality (1-5 scale)
3. Synthesize findings

### Phase 4: Generate Report

1. Create markdown report following `BENCHMARK_REPORT_V3.md` structure
2. Create GitHub issue with report
3