fcr-data-analysis
Use when executing and reporting the statistical analysis for a Field Crops Research (FCR) manuscript — mixed models for multi-environment, block-design, and split-plot designs, genotype-by-environment (G×E) and stability analysis, estimated marginal means with SED/LSD, and crop-model evaluation. FCR requires data analysed with appropriate statistics that match the design and address the objectives. Guides analysis norms; it does not fabricate results.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill fcr-data-analysis --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.
# Data Analysis (fcr-data-analysis) FCR requires that **data be analysed with appropriate statistics** and that results be **concise and address the objectives**. For field-crop work that almost always means **mixed models** that respect the design (blocks, split-plots, environments) — not a one-way ANOVA on pooled plots. Analysis execution lives here; design decisions live in `fcr-experimental-design`. ## When to trigger - Building the analysis and results section from trial or modelling data - A reviewer asked for the correct error structure, G×E modelling, or proper means separation - Reconciling main effects with interactions across environments - Evaluating a crop model against observations ## Analysis norms FCR expects 1. **Match the model to the design.** Use a **linear mixed model** with the error structure implied by the layout: blocks, whole-plot vs. sub-plot errors (split-plot), environment as a factor, and correct **random effects** (e.g., environment, block, genotype-within-environment). A wrong error term inflates significance. 2. **G×E done properly.** Test and interpret genotype/treatment × environment; where ranking matters, use **Finlay–Wilkinson**, **AMMI**, or *
- When to trigger
- Analysis norms FCR expects
- Crop-model evaluation
- Reproducibility while you work
- Error-structure decision table (match the model to the layout)
- Worked analysis vignette (illustrative)
- Anti-patterns
- Output format
- Supplementary resources
What does the fcr-data-analysis skill do?
Use when executing and reporting the statistical analysis for a Field Crops Research (FCR) manuscript — mixed models for multi-environment, block-design, and split-plot designs, genotype-by-environment (G×E) and stability analysis, estimated marginal means with SED/LSD, and crop-model evaluation. FCR requires data analysed with appropriate statistics that match the design and address the objectives. Guides analysis norms; it does not fabricate results.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill fcr-data-analysis --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 brycewang-stanford/Awesome-Journal-Skills, a repository with 909 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.