菜单
Analyze and communicate A/B tests or product experiments, including hypothesis, guardrails, sample ratio, metric definitions, effect size, uncertainty, segmentation, caveats, and launch recommendation.
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
Design dashboard specifications for executives, product teams, sales, finance, operations, and support, including audience, metrics, filters, visuals, refresh cadence, alerts, and interpretation notes.
End-to-end data analyst workflow for business, product, operations, and growth questions. Use for metric definition, SQL planning, data QA, exploratory analysis, cohort analysis, experiment readouts, dashboards, stakeholder memos, and reproducible Python or notebook analysis.
Audit data quality for analysis, dashboards, or models, checking duplicates, nulls, outliers, joins, freshness, timezone, units, currencies, late-arriving data, schema drift, and source-of-truth conflicts.
Turn analysis into executive-ready memos with answer-first summary, evidence, charts to include, assumptions, caveats, recommendation, owner, and next decision.
Define business, product, growth, finance, and operations metrics with grain, denominator, filters, time window, edge cases, owner, and decision use.
Plan and write SQL analysis for product, business, cohort, funnel, retention, revenue, and operations questions with explicit joins, grain, QA checks, and reproducibility notes.
| name | experiment-readout |
| description | Analyze and communicate A/B tests or product experiments, including hypothesis, guardrails, sample ratio, metric definitions, effect size, uncertainty, segmentation, caveats, and launch recommendation. |
| version | 0.1.0 |
Use this skill for experiment interpretation and stakeholder updates.
Return Experiment summary, Results table, Validity checks, Decision,
and Follow-up analysis.
