🎯

model-evaluation-benchmark

🎯Skill

from rysweet/amplihack

VibeIndex|
What it does

model-evaluation-benchmark skill from rysweet/amplihack

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Part of

rysweet/amplihack(81 items)

model-evaluation-benchmark

Installation

PythonRun Python server
python run_benchmarks.py --model {opus|sonnet} --tasks 1,2,3,4
πŸ“– Extracted from docs: rysweet/amplihack
14Installs
17
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Last UpdatedJan 26, 2026

Skill Details

SKILL.md

|

Overview

# 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

βœ… 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 Γ— 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: ~/.amplihack/.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)

  1. Synthesize findings

Phase 4: Generate Report

  1. Create markdown report following BENCHMARK_REPORT_V3.md structure
  2. Create GitHub issue with report
  3. Archive artifacts to GitHub release
  4. Update issue with release link

Phase 5: Cleanup (MANDATORY)

  1. Close all benchmark PRs: gh pr close {numbers}
  2. Close all benchmark issues: gh issue close {numbers}
  3. Remove worktrees: git worktree remove worktrees/bench-*
  4. Verify cleanup complete

See tests/benchmarks/benchmark_suite_v3/CLEANUP_PROCESS.md for detailed cleanup instructions.

Example Usage

```

User: "Run model evaluation benchmark"Assistant: I'll run the complete benchmark suite following the v3 reference implementation.

[Executes phases 1-5 above]

Final Report: See GitHub Issue #XXXX

Artifacts: https://github.com/.../releases/tag/benchmark-suite-v3-artifacts

```

References

  • Reference Report: tests/benchmarks/benchmark_suite_v3/BENCHMARK_REPORT_V3.md
  • Task Definitions: tests/benchmarks/benchmark_suite_v3/BENCHMARK_TASKS.md
  • Cleanup Guide: tests/benchmarks/benchmark_suite_v3/CLEANUP_PROCESS.md
  • Runner Script: tests/benchmarks/benchmark_suite_v3/run_benchmarks.py

---

Last Updated: 2025-11-26

Reference Implementation: Benchmark Suite V3

GitHub Issue Example: #1698