Measure and optimize growth using the AARRR (Pirate Metrics) framework with stage-specific KPIs and funnel analysis
中文简介正在准备中。当前显示原文说明。
guia-matthieu/clawfu-skills1502026年10月1日 更新
免费在 GitHub 公开
cohort-analysis
Analyze user retention by cohort. Use when: measuring customer retention; understanding lifecycle patterns; comparing acquisition cohorts; tracking engagement over time; identifying churn risks
中文简介正在准备中。当前显示原文说明。
guia-matthieu/clawfu-skills星标 150(GitHub 150 + cccskills 0)
实测评分 未实测
查看安装方法包含的文件（3）
- SKILL.md3.4 KB
- scripts/main.py10.2 KB
- scripts/requirements.txt73 B
SKILL.md（原文）
安装前可以查看交给 AI 代理的指令内容。
Cohort Analysis
Analyze retention and behavior patterns by grouping users into cohorts - understand how different customer groups behave over time.
When to Use This Skill
- Retention tracking - Measure how users stick around over time
- Acquisition analysis - Compare cohorts from different channels
- Product changes - Measure impact on user behavior
- Churn prediction - Identify at-risk cohorts
- LTV estimation - Project customer lifetime value
What Claude Does vs What You Decide
| Claude Does | You Decide |
|---|---|
| Structures analysis frameworks | Metric definitions |
| Identifies patterns in data | Business interpretation |
| Creates visualization templates | Dashboard design |
| Suggests optimization areas | Action priorities |
| Calculates statistical measures | Decision thresholds |
Dependencies
pip install pandas plotly click
Commands
Retention Analysis
python scripts/main.py retention data.csv --date-col signup --event-col purchase
python scripts/main.py retention data.csv --date-col signup --periods week
Visualize Cohorts
python scripts/main.py visualize cohorts.csv --output retention_chart.html
Export Report
python scripts/main.py report data.csv --date-col signup --event-col active --output report.html
Examples
Example 1: Analyze User Retention
python scripts/main.py retention users.csv --date-col signup_date --event-col last_active
# Output:
# Cohort Retention Analysis
# ──────────────────────────────────
# Cohort Users M1 M2 M3 M4
# Jan 2024 1,234 65% 48% 42% 38%
# Feb 2024 1,456 62% 45% 41% --
# Mar 2024 1,321 68% 52% -- --
# Apr 2024 1,567 64% -- -- --
#
# Avg Retention: 65% → 48% → 42% → 38%
# Best Cohort: Mar 2024 (68% M1)
Example 2: Generate Visual Report
python scripts/main.py report transactions.csv \
--date-col signup \
--event-col purchase_date \
--output retention_report.html
# Generates interactive HTML with:
# - Retention heatmap
# - Cohort size chart
# - Trend analysis
Cohort Table Format
| Cohort | Size | Period 0 | Period 1 | Period 2 | Period 3 |
|---|---|---|---|---|---|
| 2024-01 | 1234 | 100% | 65% | 48% | 42% |
| 2024-02 | 1456 | 100% | 62% | 45% | - |
| 2024-03 | 1321 | 100% | 68% | - | - |
Skill Boundaries
What This Skill Does Well
- Structuring data analysis
- Identifying patterns and trends
- Creating visualization frameworks
- Calculating statistical measures
What This Skill Cannot Do
- Access your actual data
- Replace statistical expertise
- Make business decisions
- Guarantee prediction accuracy
Related Skills
- ab-test-stats - Test retention experiments
- funnel-analyzer - Analyze conversion funnels
Skill Metadata
- Mode: centaur
category: analytics
subcategory: retention
dependencies: [pandas, plotly]
difficulty: intermediate
time_saved: 4+ hours/week
评价
还没有评价。欢迎分享你的使用感受。
评论
无需登录即可发表。欢迎分享使用感受、提问，或说明在哪些环境下无法运行。
还没有评论。来写第一条吧。
同一仓库的技能
Calculate A/B test statistical significance. Use when: determining if test results are significant; calculating required sample size; estimating test duration; analyzing conversion experiments; making data-driven decisions
中文简介正在准备中。当前显示原文说明。
guia-matthieu/clawfu-skills1502026年10月1日 更新
Assess customer account health using product usage, support sentiment, payment status, and relationship signals
中文简介正在准备中。当前显示原文说明。
guia-matthieu/clawfu-skills1502026年10月1日 更新
Analyze paid advertising performance across channels and recommend budget reallocation to maximize ROAS and minimize CAC. Use when: planning quarterly ad budget allocation, diagnosing underperforming ad channels, deciding whether to scale spend on a channel, calculating marginal ROI across Google Ads, Meta, LinkedIn, or TikTok, rebalancing media mix after performance shifts, or setting up a test-and-scale framework for new channels.
中文简介正在准备中。当前显示原文说明。
guia-matthieu/clawfu-skills1502026年10月1日 更新
Use when testing skills, commands, or agents for quality. Use after creating new skills, before deploying agents, or when debugging inconsistent agent behavior. Triggers on "evaluate", "test quality", "is this skill working", or QA of AI workflows.
中文简介正在准备中。当前显示原文说明。
guia-matthieu/clawfu-skills1502026年10月1日 更新
Use when analyzing server logs to understand how AI crawlers (GPTBot, ClaudeBot, PerplexityBot) interact with your site. Use when optimizing content placement for LLM retrieval, diagnosing why AI search isn't citing your content, or auditing crawl patterns to find optimization gaps.
中文简介正在准备中。当前显示原文说明。
guia-matthieu/clawfu-skills1502026年10月1日 更新