Sets up, audits, and reports on marketing measurement — tracking plans, event schemas, attribution models, and the dashboards built on them. Use this to instrument a site or product, audit tracking nobody trusts, choose or interpret an attribution model, build reporting that answers a specific question, or reconcile numbers that disagree between tools.
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Dashboards built on bad instrumentation are confident and wrong, which is worse than having none.
Define, in writing, before implementing: every event, when it fires, its properties and their types, and the question each one exists to answer. An event with no question behind it is noise that will be maintained forever.
Naming convention decided once and enforced: object_action, lowercase, past tense. Inconsistent
naming is unfixable later without breaking historical data.
Numbers nobody trusts usually come from one of:
Verify by doing the action yourself and watching the event arrive with the properties you expect. Not by reading the dashboard.
Every model is wrong in a known direction. Pick deliberately and state the bias:
Use one model consistently for decisions, and check it periodically against a holdout. Switching models to make a channel look better is how organizations mislead themselves.
Every report answers one question for one audience. Reports built to display everything get read by nobody.
Show the metric, its comparison period, and the decision it informs. A number with no comparison is not information. Where a number moved, the report should say why or say that the cause is unknown — "unknown" is a legitimate and useful finding.
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.
Product and web analytics: Google Analytics 4, Amplitude, Mixpanel, PostHog, Plausible, and similar.
Attribution: HubSpot or Salesforce campaign reporting, Dreamdata, Rockerbox, and similar. All of them model rather than observe — treat the output as directional and say so when you present it.
Warehouse-native reporting beats a vendor dashboard the moment you need to join spend to revenue on your own definitions.
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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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