Paid.ai — GTM agent skills by the Paid.ai team

Pricing0

Manny Medina

Founder & CEO

AI pricing model picker

Use this skill when the user is deciding how to price an AI agent product — "how should we price our agent," "per seat or per outcome," "usage-based vs outcome-based," "what do we charge for this agent." Input is what the agent does plus the target budget it displaces; output is a recommended pricing model (per agent, per action, per workflow, or per outcome), a tier structure, and defensible pricing anchors. Built on Manny Medina's four-model framework from analyzing pricing across 60+ AI agent companies at Paid.

Pricing0

Manny Medina

Founder & CEO

Credit packaging

Use this skill when the user is moving an AI product to credits or structuring a credit-based enterprise deal — "how many credits should this action cost," "design our credit tiers," "should the rate card go in the contract," "how do we explain credits to buyers." Translates agent actions into a credit schedule, designs tiers and allowances, and structures rate cards for enterprise negotiations. Built on Manny Medina's credit-selling playbook at Paid: credits are the bridge between subscriptions and outcomes — sell the outcome, meter the credit, and design every mechanic so the customer feels like they're winning.

Pricing0

Manny Medina

Founder & CEO

ROI proof generator

Use this skill when a renewal, QBR, or expansion conversation is coming and the user needs to prove what their AI agent actually delivered — "build the renewal deck," "show ROI for this account," "the buyer is asking what they got for the money." Turns raw agent activity into a renewal-ready value receipt: tasks completed, hours returned, cost avoided, and the ROI multiple, in the customer's own numbers. Built on Manny Medina's billing-first ROI approach: agents are cognitively invisible to the people paying for them, so their value has to be made explicit — continuously, not just at renewal.