agentleFS
Sign inStart free
ThinkingAIAgenticEngineThinkingAIAgenticEngine/scenario-skills/skills/retention/cohort-retention-deep-dive/SKILL.md

cohort-retention-deep-dive

Skill6 starsChanged 31 days agoNo licenceView on GitHub

Diagnose why a product's retention curve rises, falls, or cliffs, and prioritize evidence-backed optimization hypotheses. Use for generic cross-vertical cohort or active-user retention diagnosis, including period-over-period changes, segment drag, inflection days, and behaviors associated with later retention. Do NOT use when: (1) retention numbers don't match event analysis -> retention-verification; (2) the user asks about new-user quality, acquisition-channel quality, or first-day behavior attribution -> new-user-analysis; (3) the user wants to create a retention metric or merge it into an event report -> retention-metric-to-event-report; (4) the user wants to define churned users or build a churn segment -> churn-define; (5) the diagnosis is scoped to level/chokepoint churn -> level-churn-analyzer; (6) the product is short drama -> drama-retention-analyzer; (7) the question is about repeat purchase behavior -> repeat-purchase-analysis.

The file

We split this skill into the rules it gives an agent

5 rules. About 0 are common, 5 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈5

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

No licence file, so all rights are reserved — read it at the source. We count its rules but don't quote them. Read them on GitHub.

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

Check your setup, free