技能详情（站内镜像，无评论）
作者：Alireza Rezvani @alirezarezvani
许可证：MIT-0
MIT-0 ·免费使用、修改和重新分发。无需归因。
版本：v2.1.1
统计：⭐ 0 · 145 · 1 current installs · 1 all-time installs
⭐ 0
安装量（当前） 1
🛡 VirusTotal ：良性 · OpenClaw ：良性
Package：alirezarezvani/product-analytics
安全扫描（ClawHub）
- VirusTotal ：良性
- OpenClaw ：良性
OpenClaw 评估
The skill's files and runtime instructions are coherent with a product-analytics helper that reads CSV event data and computes retention, cohorts, and funnel metrics; it does not request credentials, install external code, or perform network I/O.
目的
Name/description (product KPIs, cohort/retention, dashboards) align with the included documentation and the CLI Python utility (scripts/metrics_calculator.py) which implements retention, cohort matrices, and funnel conversion from CSV input.
说明范围
SKILL.md limits runtime actions to selecting frameworks, defining KPIs, designing dashboards, and running the included CLI against local CSV files. The instructions do not ask the agent to read unrelated files, environment variables, system configs, or to transmit data externally.
安装机制
There is no install spec and only an included Python script; nothing is downloaded or extracted from external URLs. This is low-risk for disk persistence or arbitrary code retrieval.
证书
The skill requires no environment variables, no credentials, and no config paths. The included script reads only CSVs provided as command arguments and does not access secrets or network resources.
持久
Skill is not set to always:true and is user-invocable only. It does not modify other skills or system-wide agent settings; it does not request permanent presence or elevated privileges.
综合结论
This skill appears coherent and implementationally simple, but consider the following before installing: 1) Source is unknown — prefer skills from trusted publishers. 2) The CLI reads CSVs you supply; those files may contain PII or sensitive user data — avoid passing sensitive data unless you trust the environment. 3) The script loads the entire CSV into memory and assumes ISO-like date strings; large files may exhaust memory and malformed dat…
安装（复制给龙虾 AI）
将下方整段复制到龙虾中文库对话中，由龙虾按 SKILL.md 完成安装。
请把本段交给龙虾中文库（龙虾 AI）执行：为本机安装 OpenClaw 技能「Product Analytics」。简介：Use when defining product KPIs, building metric dashboards, running cohort or r…。
请 fetch 以下地址读取 SKILL.md 并按文档完成安装：https://raw.githubusercontent.com/openclaw/skills/refs/heads/main/skills/alirezarezvani/product-analytics/SKILL.md
（来源：yingzhi8.cn 技能库）SKILL.md
---
name: product-analytics
description: Use when defining product KPIs, building metric dashboards, running cohort or retention analysis, or interpreting feature adoption trends across product stages.
---
# Product Analytics
Define, track, and interpret product metrics across discovery, growth, and mature product stages.
## When To Use
Use this skill for:
- Metric framework selection (AARRR, North Star, HEART)
- KPI definition by product stage (pre-PMF, growth, mature)
- Dashboard design and metric hierarchy
- Cohort and retention analysis
- Feature adoption and funnel interpretation
## Workflow
1. Select metric framework
- AARRR for growth loops and funnel visibility
- North Star for cross-functional strategic alignment
- HEART for UX quality and user experience measurement
2. Define stage-appropriate KPIs
- Pre-PMF: activation, early retention, qualitative success
- Growth: acquisition efficiency, expansion, conversion velocity
- Mature: retention depth, revenue quality, operational efficiency
3. Design dashboard layers
- Executive layer: 5-7 directional metrics
- Product health layer: acquisition, activation, retention, engagement
- Feature layer: adoption, depth, repeat usage, outcome correlation
4. Run cohort + retention analysis
- Segment by signup cohort or feature exposure cohort
- Compare retention curves, not single-point snapshots
- Identify inflection points around onboarding and first value moment
5. Interpret and act
- Connect metric movement to product changes and release timeline
- Distinguish signal from noise using period-over-period context
- Propose one clear product action per major metric risk/opportunity
## KPI Guidance By Stage
### Pre-PMF
- Activation rate
- Week-1 retention
- Time-to-first-value
- Problem-solution fit interview score
### Growth
- Funnel conversion by stage
- Monthly retained users
- Feature adoption among new cohorts
- Expansion / upsell proxy metrics
### Mature
- Net revenue retention aligned product metrics
- Power-user share and depth of use
- Churn risk indicators by segment
- Reliability and support-deflection product metrics
## Dashboard Design Principles
- Show trends, not isolated point estimates.
- Keep one owner per KPI.
- Pair each KPI with target, threshold, and decision rule.
- Use cohort and segment filters by default.
- Prefer comparable time windows (weekly vs weekly, monthly vs monthly).
See:
- `references/metrics-frameworks.md`
- `references/dashboard-templates.md`
## Cohort Analysis Method
1. Define cohort anchor event (signup, activation, first purchase).
2. Define retained behavior (active day, key action, repeat session).
3. Build retention matrix by cohort week/month and age period.
4. Compare curve shape across cohorts.
5. Flag early drop points and investigate journey friction.
## Retention Curve Interpretation
- Sharp early drop, low plateau: onboarding mismatch or weak initial value.
- Moderate drop, stable plateau: healthy core audience with predictable churn.
- Flattening at low level: product used occasionally, revisit value metric.
- Improving newer cohorts: onboarding or positioning improvements are working.
## Tooling
### `scripts/metrics_calculator.py`
CLI utility for:
- Retention rate calculations by cohort age
- Cohort table generation
- Basic funnel conversion analysis
Examples:
```bash
python3 scripts/metrics_calculator.py retention events.csv
python3 scripts/metrics_calculator.py cohort events.csv --cohort-grain month
python3 scripts/metrics_calculator.py funnel funnel.csv --stages visit,signup,activate,pay
```