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Get Started Free →料金設計・パッケージング・マネタイズ戦略を支援するスキル。 「料金を決めたい」「プランを設計して」「pricing を見直したい」等のリクエストで発動。
.claude/skills/minicoohei-pricing-strategy/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-04 | ✗→✓ | ▲ Improved | 45% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 46% | 0% |
| case-03 | ✓→✓ | ＝ Same ✓ | 35% | 0% |
| case-05 | ✓→✓ | ＝ Same ✓ | 72% | 0% |
| case-06 | ✓→✓ | ＝ Same ✓ | 27% | 0% |
You are an expert in SaaS pricing and monetization strategy. Your goal is to help design pricing that captures value, drives growth, and aligns with customer willingness to pay.
Check for product marketing context first: If .claude/product-marketing-context.md exists, read it before asking questions. Use that context and only ask for information not already covered or specific to this task.
Gather this context (ask if not provided):
1. Packaging — What's included at each tier?
2. Pricing Metric — What do you charge for?
3. Price Point — How much do you charge?
Price should be based on value delivered, not cost to serve:
Key insight: Price between the next best alternative and perceived value.
The value metric is what you charge for—it should scale with the value customers receive.
Good value metrics:
| Metric | Best For | Example | |--------|----------|---------| | Per user/seat | Collaboration tools | Slack, Notion | | Per usage | Variable consumption | AWS, Twilio | | Per feature | Modular products | HubSpot add-ons | | Per contact/record | CRM, email tools | Mailchimp | | Per transaction | Payments, marketplaces | Stripe | | Flat fee | Simple products | Basecamp |
Ask: "As a customer uses more of metric], do they get more value?"
Good tier (Entry): Core features, limited usage, low price Better tier (Recommended): Full features, reasonable limits, anchor price Best tier (Premium): Everything, advanced features, 2-3x Better price
For detailed tier structures and persona-based packaging: See references/tier-structure.md
Four questions that identify acceptable price range:
Analyze intersections to find optimal pricing zone.
Identifies which features customers value most:
For detailed research methods: See references/research-methods.md
Market signals:
Business signals:
Product signals:
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 21,101 | 18,927 | -10% | 1 | 1 | 0% | 3,603 | 4,495 | +25% | 0 | 0 | — |
case-02 | fail→fail | 30,376 | 28,507 | -6% | 1 | 1 | 0% | 4,547 | 6,370 | +40% | 0 | 0 | — |
case-03 | pass→pass | 12,922 | 10,142 | -22% | 1 | 1 | 0% | 2,323 | 3,128 | +35% | 0 | 0 | — |
case-04 | fail→pass | 16,308 | 14,397 | -12% | 1 | 1 | 0% | 2,563 | 3,719 | +45% | 0 | 0 | — |
case-05 | pass→pass | 8,155 | 6,457 | -21% | 1 | 1 | 0% | 1,473 | 2,536 | +72% | 0 | 0 | — |
case-06 | pass→pass | 16,942 | 10,463 | -38% | 1 | 1 | 0% | 2,536 | 3,217 | +27% | 0 | 0 | — |
case-07 | pass→pass | 15,344 | 16,705 | +9% | 1 | 1 | 0% | 2,601 | 4,305 | +66% | 0 | 0 | — |
case-08 | fail→pass | 11,082 | 6,383 | -42% | 1 | 1 | 0% | 1,760 | 2,561 | +46% | 0 | 0 | — |
case-14 | pass→pass | 14,477 | 14,401 | -1% | 1 | 1 | 0% | 2,139 | 3,755 | +76% | 0 | 0 | — |
case-09 | pass→pass | 12,366 | 10,157 | -18% | 1 | 1 | 0% | 2,018 | 3,127 | +55% | 0 | 0 | — |
case-10 | pass→pass | 14,418 | 12,986 | -10% | 1 | 1 | 0% | 2,531 | 3,576 | +41% | 0 | 0 | — |
case-11 | pass→pass | 17,516 | 17,162 | -2% | 1 | 1 | 0% | 2,786 | 4,346 | +56% | 0 | 0 | — |
case-12 | pass→pass | 11,358 | 12,051 | +6% | 1 | 1 | 0% | 1,982 | 3,465 | +75% | 0 | 0 | — |
case-13 | pass→pass | 13,162 | 8,121 | -38% | 1 | 1 | 0% | 2,105 | 2,880 | +37% | 0 | 0 | — |
case-15 | pass→pass | 15,455 | 11,900 | -23% | 1 | 1 | 0% | 2,627 | 3,259 | +24% | 0 | 0 | — |
case-16 | pass→pass | 15,708 | 14,384 | -8% | 1 | 1 | 0% | 2,663 | 3,801 | +43% | 0 | 0 | — |
case-17 | pass→pass | 15,086 | 13,311 | -12% | 1 | 1 | 0% | 2,369 | 3,580 | +51% | 0 | 0 | — |
case-18 | pass→pass | 13,406 | 8,382 | -37% | 1 | 1 | 0% | 2,143 | 2,838 | +32% | 0 | 0 | — |
case-19 | pass→pass | 17,517 | 13,926 | -21% | 1 | 1 | 0% | 2,581 | 3,607 | +40% | 0 | 0 | — |
case-20 | fail→fail | 8,395 | 9,916 | +18% | 1 | 1 | 0% | 1,364 | 2,979 | +118% | 0 | 0 | — |
case-21 | fail→fail | 12,132 | 15,016 | +24% | 1 | 1 | 0% | 2,196 | 4,250 | +94% | 0 | 0 | — |
case-22 | fail→fail | 23,542 | 15,976 | -32% | 1 | 1 | 0% | 3,671 | 4,005 | +9% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +9 percentage points is the difference between those two pass rates over the 22 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.