Compare multiple models on identical tasks using objective and subjective scoring.
npx skills add https://github.com/tyroneross/build-loop --skill model-bakeoff
Please help me install this Agent Skill. Skill: model-bakeoff Source: https://github.com/tyroneross/build-loop/tree/main/skills/model-bakeoff Command: npx skills add https://github.com/tyroneross/build-loop --skill model-bakeoff
This Skill addresses the challenge of comparing the performance of multiple models on the same task, ensuring a fair and thorough evaluation.
To initiate a model bake-off, use the command: `model-bakeoff run --models "opus", "sonnet", "gpt-5.5" --task "refactor feature X" --branch "experiment" --judge "Fable" --metric "typecheck + build + test + betterer" --threshold 80%.
High-intent search queries and answers about installing and using this skill.
To compare multiple AI models, you run a controlled bake-off where each model diagnoses, plans, and executes the same change in isolated worktrees. Performance is then scored against predefined metrics like typecheck, build, and test results.
Yes, AI model evaluation can use both objective metrics (typecheck, build, test) and subjective criteria (code quality, intent fidelity). This dual-scoring approach ensures a fair and thorough assessment of each model's performance on the task.
The best way to benchmark code changes is to execute a multi-model comparison in isolated environments. This ensures models perform the same task under identical conditions, allowing you to accurately score their performance against a defined threshold.
After a performance benchmark, you can merge the best outcomes from the winning model while grafting improvements from other models. This allows you to synthesize the highest quality code from the entire evaluation.
Yes, you need agents and orchestrators to run a multi-model bake-off. They are required to manage the isolated worktrees and coordinate the scoring process across the different models being evaluated.
Task-specific model selection works by evaluating model performance on identical tasks using predefined metrics and subjective code review criteria. You can set a specific threshold, like 80%, to identify which model handles the task best.
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