cohort-analysis
by phurynPerform cohort-analysis on user retention, engagement decay, and feature adoption by cohort. This cohort-analysis skill is built for Data Analysis workflows that need validation, calculation, visualization, and clear insights from structured user behavior data.
This skill scores 77/100, which means it is solidly listable for directory users: it has a clear cohort-analysis use case, a real workflow, and enough operational detail to help an agent trigger and execute it with less guesswork than a generic prompt. It is useful, but users should still expect some adoption friction because there are no supporting scripts, references, or install command to reinforce the workflow.
- Clear triggerability: the description explicitly covers retention curves, feature adoption trends, churn patterns, and engagement analysis use cases.
- Operational workflow is spelled out in steps, including data validation, quantitative analysis, visualization, and insight generation.
- Good body depth for agent execution: 4,710 characters with multiple headings and practical instructions, plus code-fence support for Python analysis scripts.
- No support files or references are included, so users must trust the single SKILL.md for method details and examples.
- No install command is provided, which may make adoption less straightforward for some directory users.
Overview of cohort-analysis skill
What cohort-analysis does
The cohort-analysis skill helps you analyze user retention, engagement decay, and feature adoption by cohort. It is a good fit for Data Analysis work when you need to answer questions like “Which signup group retained best?”, “Where do users drop off?”, or “Is a new feature improving long-term engagement?” The main value of this cohort-analysis skill is that it structures the work around validation, calculation, visualization, and insight generation instead of leaving you with a generic summary.
Who should install it
Install the cohort-analysis install if you regularly work with product analytics, lifecycle metrics, or customer behavior data. It is most useful for analysts, growth teams, product managers, and anyone turning raw event tables into cohort-based decisions. If your data already includes cohort labels, time buckets, and engagement measures, the skill can save time and reduce prompt ambiguity.
What makes it useful
This cohort-analysis guide is oriented toward practical analysis rather than reporting fluff. The skill expects real input data, checks structure before analysis, and can generate retention heatmaps, progression charts, and feature adoption comparisons. That makes it stronger than a one-off prompt when you want repeatable cohort-analysis usage across different datasets.
How to Use cohort-analysis skill
Install and open the skill files
Use the standard install flow for your environment, then open SKILL.md first. If your workspace includes supporting files, review README.md, AGENTS.md, metadata.json, and any rules/, resources/, references/, or scripts/ folders. For this repository, the main source of truth is the skill file itself, so the first read should focus on the workflow and data expectations in SKILL.md.
Prepare input the skill can actually analyze
For best cohort-analysis usage, provide structured data with a clear cohort identifier, a time dimension, and one or more engagement metrics. Good inputs look like:
- signup month plus monthly active users
- acquisition cohort plus retention by week
- account tier plus feature adoption counts
- event-level data with timestamps and user IDs, if the skill needs to derive cohorts
If your data is messy, say what column names mean and what aggregation level you want. That matters more than adding extra narrative.
Turn a rough request into a usable prompt
A weak request says: “Do cohort analysis on this data.”
A stronger request says: “Use cohort-analysis to compare monthly retention for users who signed up in Q1 vs Q2, flag the biggest drop-off month, and produce a short interpretation for a product team.”
The second version gives the skill a target, a comparison frame, and the intended output.
Follow the workflow that improves results
Use the skill in this order: validate the dataset, confirm the cohort logic, run the quantitative analysis, then request visualizations and takeaways. If you skip validation, you may get misleading retention rates from incomplete periods or mixed time buckets. If you need Python output, ask for it explicitly so the skill can produce pandas/numpy-oriented analysis rather than only narrative findings.
cohort-analysis skill FAQ
Is cohort-analysis only for retention reporting?
No. The cohort-analysis skill also covers feature adoption trends, churn patterns, and segment-level engagement. Retention is the most common use case, but the skill is broader when your question depends on behavior over time by group.
Do I need advanced analytics experience?
No, but you do need to know what your cohorts and time periods represent. This cohort-analysis guide is beginner-friendly if your data is already clean. If your dataset is ambiguous, the skill works better when you specify the cohort definition and the exact metric to analyze.
When is a generic prompt enough?
A generic prompt is fine for a quick summary on a small, clean table. Use the cohort-analysis skill when you want repeatable structure, clearer validation, better visualization guidance, or a more reliable path from raw data to decision-ready insight.
When should I not use it?
Do not use cohort-analysis for problems that are not time-based or group-based, such as static segmentation without a temporal dimension. If you only need a simple KPI dashboard or a one-off descriptive statistic, a lighter prompt may be faster.
How to Improve cohort-analysis skill
Provide cleaner cohort definitions
The biggest quality boost comes from defining the cohort logic clearly: by signup date, first purchase date, first use of a feature, or another anchor event. Say whether cohorts are daily, weekly, or monthly, and define the retention window. This prevents the skill from guessing and makes the output easier to trust.
State the exact business question
The cohort-analysis skill performs best when you tell it what decision the analysis should support. For example: “Identify whether week-1 retention improved after the March launch,” or “Compare adoption of Feature X between SMB and enterprise cohorts.” That focuses the analysis on a decision, not just a chart.
Ask for the format you need
If you want a notebook-ready result, ask for calculations, assumptions, and chart suggestions. If you want a stakeholder summary, ask for plain-English findings with the top three takeaways and one caution about data limitations. This improves cohort-analysis usage because the output is shaped for the next step instead of being forced to be generic.
Iterate using anomalies and edge cases
After the first pass, ask the skill to explain unexpected spikes, sudden drop-offs, or unusually strong cohorts. Also ask what data could invalidate the conclusion, such as incomplete observation windows or mixed acquisition channels. That second pass is where cohort-analysis for Data Analysis becomes more decision-useful, because it turns a table of rates into a defensible interpretation.