model-evaluation-benchmark
Automated reproduction of comprehensive model evaluation benchmarks following the Benchmark Suite V3. Auto-activates for model benchmarking, comparison evaluation, or performance testing between AI models.
npx skills add majiayu000/claude-skill-registry --skill model-evaluation-benchmark-rysweet-amplihack --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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: ###
- Skill Description
- When to Use
- Execution Instructions
- Phase 1: Setup
- Phase 2: Execute Benchmarks
- Phase 3: Analyze Results
- Phase 4: Generate Report
- Phase 5: Cleanup (MANDATORY)
- Example Usage
- References
cd tests/benchmarks/benchmark_suite_v3
python run_benchmarks.py --model {opus|sonnet} --tasks 1,2,3,4What does the model-evaluation-benchmark skill do?
Automated reproduction of comprehensive model evaluation benchmarks following the Benchmark Suite V3. Auto-activates for model benchmarking, comparison evaluation, or performance testing between AI models.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill model-evaluation-benchmark-rysweet-amplihack --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.