Plans, runs, and optimizes paid acquisition across search, social, and display — account structure, targeting, creative, bidding, budget, and the analysis that says whether to scale or stop. Use this to set up or restructure campaigns, write and iterate ad creative, diagnose rising costs or falling performance, decide budget allocation, or judge whether a channel is working.
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Paid is the fastest way to buy a result and the fastest way to buy nothing. The difference is mostly discipline before launch.
Structure follows intent, since intent determines what a click is worth.
Decide the convention before the first campaign, because renaming later breaks historical reporting on every platform.
A workable pattern encodes, in fixed order: channel, objective, audience or intent tier, geography, and creative theme — separated consistently, with no spaces. It should be possible to filter a report by any one of those segments without opening a campaign.
Apply it to every level: campaign, ad set, and ad. Inconsistent naming is why cross-channel reporting takes a week to assemble and why nobody trusts it when it arrives.
Creative is the biggest lever on paid social and the most neglected.
Diagnose in sequence: impressions (is it serving), click-through (is the creative landing), conversion rate (is the page delivering), cost per acquisition (is it economic), and downstream quality (did those customers stay). Stopping at cost per acquisition is how channels get scaled that are buying bad customers cheaply.
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.
The platforms themselves: Google Ads, Meta Ads Manager, LinkedIn Campaign Manager, Microsoft Advertising, TikTok Ads Manager, Reddit Ads, and similar. Each has its own definition of a conversion, and reconciling them against one internal definition is most of the analytics work.
Management and automation layers — Skai, Optmyzr, Smartly, and similar — are worth it at spend levels where a full-time person could not manually cover the account structure, and not before.
Creative volume is the constraint more often than bidding. Budget for producing variants at the rate the platform consumes them.
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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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