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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
기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
직접 명령은 검토 Prompt를 거치지 않습니다. 실행하기 전에 소스를 확인하세요.
npx skills add https://github.com/tangxiangru/AutoR --skill earth-report-the-lattice-and-show-the-field명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
로컬 사본을 원하시나요? SkillsMP에서 현재 제공할 수 있는 파일을 다운로드하세요.
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.
SKILL.md 표시 중
SOC 직업 분류 기준
| 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.