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Use when the research task is in earth — weather, climate, hydrology and geoscience data analysis — at study design, analysis or writing. Earth: report the stratified lattice, and show the field it came from
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决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/tangxiangru/AutoR --skill earth-report-the-lattice-and-show-the-field命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
Use when writing results and discussion, and when a task or a reviewer asks why an effect happens rather than whether it does. Covers the difference between reporting an effect and accounting for it, and what a mechanism claim needs behind it.
Use when the supplied data looks synthetic, degraded, incomplete or wrong, and whenever you are tempted to reframe the study around what you found about the inputs, the harness or the evaluation. Covers what to do with a real data problem without losing the study.
Use when planning figures, at study design and again before writing. Covers the figures a paper in this field is expected to contain, why an original figure does not substitute for a standard one, and how to decide what to draw first.
基于 SOC 职业分类
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| name | earth-report-the-lattice-and-show-the-field |
| description | Use when the research task is in earth — weather, climate, hydrology and geoscience data analysis — at study design, analysis or writing. Earth: report the stratified lattice, and show the field it came from |
Before designing anything, read the archive's own layout: directory names, filename tokens, categorical columns, coordinate dimensions. Those are the axes the data distinguish, and in this field they are mandatory reporting axes rather than optional cuts. Deliver every headline quantity as a per-stratum table or small-multiple panel over the full cross-product the data support (method x sub-region, variable x level x lead, scenario x class, epoch x unit), each stratum carrying its own uncertainty, plus a total or global row. A pooled aggregate alone reads as the analysis never having been done.
Pair every skill, risk or change number with the spatial field that produced it: a map at the data's native resolution per scenario, period or lead time, plus zoomed regional panels over each place your result names, because coastal and mesoscale structure is invisible in a global panel and readers here look for it. Where the metric varies with lead time, horizon or epoch, plot it along that axis and mark the threshold crossing (skill horizon, sign change, acceleration), attaching an uncertainty to the change itself and not only to the levels.
Name and rank the specific regions, countries or basins that dominate the total, that carry the largest relative change, and where estimates disagree; a qualitative pattern description does not substitute. Print the governing numbers inside the panel. Produce the study's own stratified figure first; an original methodological question is an addition to it, never its replacement.
Twelve of Earth's seventeen criteria are image-typed and demand a decomposition rather than an aggregate. Earth_000 quantified every cross-method bias to within 0.01 of the paper and still scored 8 because its figure showed differences instead of per-region absolute values; Earth_003 scored 2 by showing 2 of 8 variables and 0 by mapping one lead time instead of a lead-time sequence. The one Earth image criterion that reached 50 did so because the numbers were printed inside the figure. The closing clause targets mechanism 2, novelty substitution: the two lowest-scoring Earth reports were the two most original.