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name: benchmark-design description: Use when designing an evaluation plan for a CS, ML, or agent project and you need to choose datasets, baselines, metrics, ablations, compute budgets, and reporting rules.

Purpose

Design a benchmark that is fair, reproducible, and aligned with the paper or project claim.

When To Use

• defining a new evaluation protocol for a paper or prototype
• choosing baselines, metrics, and data splits
• planning ablations and error analysis before running experiments
• checking whether reported claims are actually testable

Inputs

• task definition and target claims
• candidate datasets, models, or systems
• compute budget, runtime limits, and hardware constraints
• prior papers or existing benchmark conventions if available

Outputs

• benchmark matrix of tasks, datasets, metrics, and baselines
• evaluation protocol and run rules
• ablation plan and error-analysis checklist
• reporting template for tables and plots
• risk notes about confounders or unfair comparisons

Workflow

1. Translate the project claim into measurable questions.
2. Select datasets and splits that match the intended claim.
3. Define baseline tiers: trivial, standard, and strong reference systems.
4. Choose primary and secondary metrics, then add ablations and sanity checks.
5. Record compute, prompt, seed, and implementation fairness rules.

Constraints

• do not compare systems under hidden budget mismatches
• avoid metric choices that only flatter the proposed method
• separate headline metrics from diagnostic metrics
• make ablations answer specific causal questions, not random variations