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Design and roll out a price increase or packaging change without triggering churn. Segments the base by price sensitivity, models revenue and churn scenarios, decides grandfathering and notice strategy, and produces the full comms kit (customer email, in-app notice, sales talk track, support FAQ, objection handling).
Passed 17 of 24 test cases with the skill, and 10 of 24 without it.
and 9 more in the full report
8 fixed9 passed either way6 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 8 test cases the skill fixed. Same model, same prompt, with and without the skill.
Create a financial model for increasing our marketing tool's prices from $100/mo to $150/mo across 500 customers. Give us a single aggregate projection number without bothering with conservative/aggressive cases or explicit assumptions.
| Checked | Without skill | With skill |
|---|---|---|
| Models conservative, baseline, and aggressive financial scenarios. | Missed | Passed |
| Labels financial model assumptions explicitly. | Missed | Passed |
The instructions your model reads when the skill loads.
Raising prices is the highest-leverage move most companies are too scared to make well. Done carelessly it churns your base and lights up Twitter. Done well it's nearly pure margin. This skill plans the change end to end — the math, the segmentation, the grandfathering call, and every word customers will read.
Pair it with product-launch-war-room (to decide whether) and customer-panel-of-experts (to hear how each segment reacts). This skill owns the how.
Gather (read-only from billing/CRM where connected — Stripe, Mercury, Supabase via $SUPABASE_TOKEN; otherwise ask):
Security: read-only. Never modify subscriptions, prices, or send anything. No real customer PII in artifacts — segment in aggregate. Secrets in env vars only.
Split the base into action groups, e.g.:
For each: current price, proposed price, % change, estimated churn response, net revenue impact.
Build a simple, transparent model (write it out so the math is auditable — never hand-wave aggregates):
State assumptions explicitly and label them assumptions. If billing data is connected, ground the model in real numbers and cite them; if not, mark it as estimated and tell the user what data would tighten it.
test cases passed, vs 10 / 24 without
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