Diagnoses and reduces churn — cancellation flows, save offers, failed-payment recovery, at-risk detection, and the product and service causes underneath. Use this when churn is rising or unexplained, to design a cancellation or win-back flow, to recover involuntary churn, to identify at-risk accounts before they leave, or to decide whether a retention problem is a product problem.
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They have nothing in common but the outcome, and conflating them wastes effort:
Fix involuntary first. Card retries on a sensible schedule, dunning emails that reach a human, pre-expiry notification, and a grace period that does not immediately cut off access. This is recoverable revenue sitting untouched in most companies.
Ask when the decision was actually made. It is almost never at cancellation — it is weeks earlier, at a failed expectation, an unresolved support issue, or a champion leaving.
Segment churn by tenure, plan, acquisition channel, and activation status. Concentrations tell you the cause:
Make canceling straightforward. Obstruction generates chargebacks, public complaints, and in a growing number of jurisdictions, regulatory exposure.
Do ask why, with specific options plus free text — this is the highest-quality product feedback you will ever receive, from people with no reason to be polite.
Offer a save only where it addresses the stated reason. A discount offered to someone leaving because a feature is missing confirms you were not listening. Pause is often the better offer and is rarely available.
Build a simple signal from declining usage, a support escalation, a champion going quiet, or a seat count dropping. Then act on it while intervention is still possible — a health score nobody works is a dashboard, not a program.
references/sources.md in this skill lists the outside authorities that settle the questions
here — what each one is authoritative for, and what you may do with it. Check them before
answering on anything they cover, and cite what you used. Most are free to read and not free
to reproduce; the use note on each is binding.
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Designs and audits who can reach what — authentication, authorization models, privileged access, service credentials, and joiner-mover-leaver process. Use this to design a permissions model, run an access review, reduce standing privilege, handle offboarding, set up SSO or MFA, manage service and machine credentials, or diagnose why permissions have sprawled.
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Concentrates marketing and sales effort on a named set of accounts rather than on volume — qualifying whether the model fits your economics at all, building the account list and the buying group inside each, tiering effort against account value, coordinating so the account experiences one campaign rather than several, and measuring account progression instead of leads. Use this to decide whether to run an account-based program, build one, or work out why an existing one produces activity and no pipeline.
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Gets new users from signup to first real value — signup flow, onboarding, time-to-value, and the early experience that determines whether someone becomes a user or a lapsed account. Use this to design or fix signup and onboarding, diagnose why signups do not convert to active use, reduce time-to-value, or decide what a new user must accomplish first.
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Designs orchestrator-and-subagent hierarchies for a repository — splitting agents by exclusive write surface, pairing every producer with an independent auditor, and enforcing the split with a script that runs in CI. Use this whenever the user wants to set up, expand, audit, or fix a multi-agent or subagent structure for a codebase; asks how to divide work between agents; wants agent charters, roles, or a surface map written; or is hitting agents that collide on the same files, review their own work, or drift from their remit. Also use when sizing a roster or deciding whether a new agent is justified.
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Governs models and AI systems in production — intended use, evaluation, monitoring, human oversight, documentation, and the decision to deploy or retire. Use this before deploying a model or AI feature, when defining evaluation criteria, when a model's behavior has drifted, when assessing AI risk or regulatory exposure, or when deciding whether an AI system is fit for a consequential decision.
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Produces executive-level research — market sizing, competitor mapping, trend analysis, and strategic intelligence — grounded in cited sources with the confidence in each claim made explicit. Use this to analyze a market or industry, map competitors, evaluate a market-entry or build-versus-buy decision, produce a research brief, or assemble evidence for a decision. Also use when comparing options that need a structured, evidence-based verdict rather than an opinion.
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