Reprice and repackage so your revenue is more predictable.
Move off pure per-seat, decide what to meter and what to bundle, set the tiers, and model the impact on deal size and win rate before you change a thing.
Inside: The failure point, the value metric, the tiers, and the modeled impact.
Install it in one line, or paste it in.
~/.claude/skills/ and runs automatically when it is relevant.Connect your context. Set it to your motion.
it reads real deal sizes, discounting, and win rates by segment.
it sees which features drive value and who is stuck on free.
| Set this | What it is | Example |
|---|---|---|
| Your value metric | The thing you charge for that scales with value | Seatsusageoutcomesrecordsrevenue processed |
| Your tiers | The packages a buyer chooses between | StarterTeamBusinessEnterprise |
| Your fence lines | What separates one tier from the next | Volumefeaturessupportsecurity |
| Your guardrails | The moves you will not make | No surprise overagesno removing a feature customers rely on |
Everything the skill does, in full.
Turns "our pricing is leaving money on the table" into a concrete repackaging plan. It finds where your current model breaks (usually a pure per-seat license that bets revenue on headcount), decides what to charge for and what to bundle, sets the tiers and the value metric, and models the effect on deal size, win rate, and free-to-paid conversion before you change a thing. Reprice to de-risk, not just to raise the number.
- 11. Find where the model breaks
Name what your current model bets on. A pure per-seat license bets revenue on the customer's headcount, so it caps you when they freeze hiring and it under-charges the accounts getting the most value. Say the failure out loud.
- 22. Pick the value metric
Choose what to charge for so the bill grows as the customer succeeds, not as they add chairs. The best metric is one the buyer already believes is fair and can predict.
- 33. Set the tiers and the fences
Decide what is bundled and what is metered, and draw clean fence lines between tiers so a buyer knows exactly why they move up. Bundle the things that drive adoption, meter the things that scale with value.
- 44. Handle the free base
If a large share of active users sit on free, decide what converts them: a usage ceiling, a value-gated feature, or a self-serve motion. Free is a funnel, not a leak, only if there is a path off it.
- 55. Model the impact before you ship
Estimate the effect on average deal size, win rate, and free-to-paid, using your own numbers. A reprice that lifts deal size but tanks win rate is a loss. Reprice to de-risk: bigger deals, held or better win rates, a smaller free base.
- Every projected lift is modeled from your own deal or usage data, or clearly labeled an assumption.
- No move that creates a surprise bill or removes a feature customers depend on.
- The plan states the migration path for existing customers, not just new logos.
- If the change raises deal size but hurts win rate or retention, it does not ship.
CURRENT: pure per-seat, revenue capped by customer headcount VALUE METRIC: shift core to seats + a usage tier for the heavy accounts TIERS: Starter (self-serve) / Team / Business / Enterprise, fences on volume + security FREE BASE: ~40% on free, add a usage ceiling + one value-gated feature MODELED IMPACT (from your data, illustrative): - Average deal size: +25 to 35% - Win rate: held (value-based framing, not a raw increase) - Free-to-paid: single digits to low double digits MIGRATION: grandfather existing seats 12 months, opt-in to new tiers
Your price list and tiers are yours. Deal sizes, discounting, and win rates come from your CRM when connected; feature value and free-user counts come from a product-analytics tool. Every projected lift is modeled from those numbers or labeled an assumption.
Where an operator takes this next.
The plan on paper is a hypothesis. Here's how an operator tests it before it touches a real quote.
Repricing is a bet. The connectors are what turn the bet into a number you can defend before you make it.
Connect the CRM so projected deal-size and win-rate shifts are computed from your actual discounting and close history, not an illustrative range.
Connect a product-analytics tool so the usage ceiling and value-gated feature get tuned against who's actually stuck on free, not a guess at the 40%.
Feed the affected-accounts list into a business-case skill so existing customers get a grandfathering plan with names attached, not a generic policy memo.
One skill is the on-ramp.
A single skill does one job. Chained into a playbook, or run as a full build, it becomes a system. Here is where this one plugs in.