# Stem Experiment Designer

> Experiment design, validation planning, baseline selection, ablation study planning, statistical testing, and reproducibility planning for STEM papers. Use when users need to design experiments for algorithms, models, systems, materials, instruments, simulations, biomedical studies, control systems, communication systems, or engineering prototypes. Trigger phrases include experiment design, validation plan, ablation study, baseline, benchmark, metrics, statistical test, reproducibility, and Chinese requests about shiyan sheji or xiaorong shiyan.

- Skill: `xiangdong-415/stem-experiment-designer` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add xiangdong-415/stem-experiment-designer`
- Raw SKILL.md: https://api.skillmd.com/api/skills/xiangdong-415/stem-experiment-designer/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: Xiangdong-415 (https://skillmd.com/u/xiangdong-415)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/xiangdong-415/stem-experiment-designer

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# STEM Experiment Designer

## Purpose

Use this skill to design experiments that convincingly test a STEM paper's claims. It focuses on matching claims to evidence, selecting fair baselines, and making evaluation reproducible.

## Inputs To Ask For

- Main claims or hypotheses.
- Method or system being evaluated.
- Dataset, sample, simulation, apparatus, or benchmark.
- Constraints such as budget, runtime, safety, ethics, equipment, or available data.
- Target venue and expected standards.

## Workflow

1. Convert claims into testable questions.
   - For each claim, define what evidence would support it and what result would weaken it.

2. Choose baselines.
   - Include classical, current strong, domain-standard, and simple sanity-check baselines.
   - Explain why each baseline is fair.

3. Define metrics.
   - Use primary metrics aligned with the claim.
   - Add secondary metrics for trade-offs: latency, memory, energy, cost, robustness, uncertainty, safety, interpretability, or sample efficiency.

4. Plan core experiments.
   - Main comparison.
   - Ablation study.
   - Sensitivity analysis.
   - Robustness or generalization test.
   - Failure-case analysis.
   - Runtime or resource analysis when relevant.

5. Add statistical and reproducibility controls.
   - Repeats, random seeds, confidence intervals, effect sizes, significance tests, calibration, train/test separation, blinding, or cross-validation as appropriate.
   - Define data splits and exclusion criteria before seeing results when possible.

6. Map experiments to paper figures and tables.
   - Decide which result belongs in the main paper and which belongs in appendix or supplementary material.

## Output Format

Provide:

- Claim-to-experiment matrix.
- Baseline and metric rationale.
- Experiment protocol.
- Ablation and sensitivity plan.
- Statistical analysis plan.
- Expected tables and figures.
- Risk list: confounders, weak evidence, missing controls, and likely reviewer objections.

## Quality Checklist

- Every major claim has at least one direct experiment.
- Baselines include strong recent competitors and simple controls.
- Metrics match the actual research goal.
- Ablations isolate the contribution rather than merely adding extra numbers.
- Randomness, data leakage, confounding, and reproducibility are addressed.