菜单
Use when a new notebook-based analytics request is still vague or could change the hypothesis, randomization unit, metric hierarchy, time window, or robustness checks before analysis starts
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。
基于 SOC 职业分类
| name | ds-brainstorming |
| description | Use when a new notebook-based analytics request is still vague or could change the hypothesis, randomization unit, metric hierarchy, time window, or robustness checks before analysis starts |
Explore the analytical problem before opening a notebook. The goal is to turn a vague research request into a design with explicit units, metrics, assumptions, and success criteria.
Do NOT start SQL, pandas, notebook editing, metric computation, or statistical interpretation until you have presented a design and the user has approved it.Do not start SQL, pandas, or notebook work until the design is explicit enough to hand off to ds-analysis-plan or ds-experiment-design.
Exploratory analysis still needs a design. The design can be short, but it must make the hypothesis, unit, metrics, and robustness checks explicit before execution.
You MUST complete these in order:
ds-analysis-plandigraph brainstorming {
"Explore context" [shape=box];
"Ask clarifying questions" [shape=box];
"Propose 2-3 approaches" [shape=box];
"Present design sections" [shape=box];
"User approves design?" [shape=diamond];
"Write design doc" [shape=box];
"Invoke ds-analysis-plan" [shape=doublecircle];
"Explore context" -> "Ask clarifying questions";
"Ask clarifying questions" -> "Propose 2-3 approaches";
"Propose 2-3 approaches" -> "Present design sections";
"Present design sections" -> "User approves design?";
"User approves design?" -> "Present design sections" [label="no, revise"];
"User approves design?" -> "Write design doc" [label="yes"];
"Write design doc" -> "Invoke ds-analysis-plan";
}
ds-analysis-planDocumentation:
docs/plans/YYYY-MM-DD-<topic>-design.mdImplementation handoff:
ds-analysis-planUse when a research question spans multiple SQL, pandas, or notebook steps and you need explicit cohorts, windows, metrics, validation checks, outputs, and statistical methods before touching queries or cells
Use when you already have a written analysis plan and want to execute SQL, notebook, or validation tasks in a separate session with review checkpoints and rerun-based evidence between batches
Use when a metric definition, denominator, CUPED covariate, linearization choice, invariant, outlier treatment, missingness pattern, or leakage risk may distort an experiment or validation readout
Use when creating, editing, reviewing, or finalizing analytical notebooks whose code, assumptions, transformations, diagnostics, or conclusions need to be understandable to another analyst
Use when notebook results depend on parameters, caches, run order, hidden state, seeds, external helper code, or exported tables and the analysis must be rerunnable by another analyst
Use when an experiment analysis, metric study, notebook investigation, or methodology proposal is close to decision-ready and you need an independent check of design, statistics, interpretation, and reproducibility