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Design reproducible, well-controlled experiments to test a hypothesis. Use when planning data collection, choosing measures and controls, or structuring an analysis.
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想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
Use when a user asks to simulate powder diffraction patterns, run GSAS-II Rietveld/Pawley/Le Bail refinement, fit powder diffraction data, inspect GSAS-II service health, or build workflows combining CIF structures with XRD/neutron powder data through the deployed chemistry gateway.
Use when a user asks to parse material formulas, inspect compositions, parse or summarize crystal structures, convert CIF/POSCAR/JSON/CSSR/XSF structures, or analyze symmetry through the deployed chemistry pymatgen gateway service. Triggers on pymatgen, materials, composition, formula parsing, CIF, POSCAR, crystal structure, lattice, space group, symmetry, primitive cell, or structure conversion.
Design and prioritize candidate materials from tabular experimental or computational data. Use when identifying controllable material descriptors and one or more target properties, fitting and validating surrogate models, defining a feasible search space, optimizing single- or multi-objective performance, quantifying uncertainty, or preparing a reproducible materials-discovery recommendation report.
基于 SOC 职业分类
正在显示 SKILL.md
| name | experiment-design |
| description | Design reproducible, well-controlled experiments to test a hypothesis. Use when planning data collection, choosing measures and controls, or structuring an analysis. |
Turn a hypothesis into a concrete, reproducible experimental plan with unambiguous outcome measures.
Require: one falsifiable hypothesis (from hypothesis-generation) and knowledge of the domain's measurement tools.
Define the unit and population. What is the experimental unit (subject, sample, run, cell)? What population are you reasoning about, and how will units be obtained?
Fix the manipulation. Specify the independent variable, levels, and which levels are control vs treatment. Prefer minimal manipulation to isolate the mechanism.
Choose the outcome. Define the dependent variable operationally — precisely enough that two researchers would measure the same number. State the measurement instrument.
Control confounds. List plausible alternative explanations and how the design rules them out (randomization, blocking, blinding, negative/positive controls, counterbalancing).
Plan the sample. Decide the number of units, effect size you can detect, and analysis. State the statistical test before running it; pre-register the analysis plan.
Write the protocol. A step-by-step protocol that another lab could execute from prose alone, including data format and where results are stored.
research/experiments/<name>/protocol.md — hypothesis, design, measures, controls, sample size & analysis plan.research/experiments/<name>/run.py (or .R/.ts) — analysis script producing results from raw data.research/experiments/<name>/results/ — data and outputs, reproducible on demand.