Data Analyse

data-analyse

Product management data analysis skill for defining metrics, building funnels/cohorts, diagnosing growth/retention, designing experiments, and translating insights into product actions. Use for tasks like metric trees, event taxonomy, dashboard requirements, SQL-style analysis plans, and decision...

muzhicaomingwang/ai-ideas·0installs3stars

SKILL.md

Full skill instructions

data-analyse

Use this skill for PM 视角的数据分析：把问题变成可量化指标与可执行动作。

Outputs (choose what the task needs)

Metric tree / North Star Metric decomposition
Event taxonomy（埋点口径：事件名/属性/触发时机/去重规则）
Analysis plan（要回答的问题、需要哪些数据、SQL/口径）
Funnel / cohort definitions（留存、转化、激活）
Experiment plan（假设、指标、样本量、guardrails）
Insight → action memo（结论、证据、建议、风险）

Workflow

Clarify the decision

业务问题是什么？要做哪个决策？“做/不做/怎么做”。
时间窗口：今天要结论，还是一周内可迭代？

Define metrics and guardrails

North Star Metric + supporting metrics.
Guardrails：错误率、退款/投诉、延迟、合规等。
明确口径：分母/分子、去重、时间窗、过滤条件。

Define data collection (events + properties)

事件命名一致、可组合。
属性尽量有限且可枚举；避免高基数字段（如 user_id 作为属性）。
明确客户端/服务端各自负责哪些事件。

Choose analysis methods

Funnel：识别掉点（按渠道/人群/版本分群）。
Cohort：留存与复购（D1/D7/D30）。
Segmentation：新老用户、付费/非付费、不同入口。
Causal thinking：区分相关与因果，避免幸存者偏差。

Recommend actions

针对最大掉点给 1–3 个高性价比改动。
每个改动配：预期影响、实现成本、风险与验证方式。

Experiment design (if needed)

先写假设：如果 X 改为 Y，会让指标 Z 提升，因为 ...
定义主指标、辅助指标、guardrails。
明确随机化单位（用户/会话/企业）和实验周期。

Templates

埋点条目

Event: event_name
When: 触发时机
Props: key/type/allowed values
Dedup: 去重规则
Owner: client/server

Insight memo

Question:
Data:
Findings:
Interpretation:
Recommendation:
Risks:
Next measurement:

Install skill

Copy and run in your terminal

npx -y skills add https://github.com/muzhicaomingwang/ai-ideas --skill data-analyse

View on GitHub

.project/ai/pm/skills/data-analyse/SKILL.md

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