Pricing strategy
A founder/indie-hacker skills pack for Claude Code, Cursor, Codex and Gemini CLI: market research, competitor analysis, outcome-based pricing, landing-page copy, MVP spec, cold outreach, growth experiments and fundraising narrative.
npx -y skills add Hayatelin/founder-skills --skill pricing-strategyAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
One thing to look at
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What its author says it does
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Chooses a pricing model (per-seat, usage, outcome-based, or hybrid), sets tiers, and sanity-checks unit economics. Trigger on "how should I price", "help me price my SaaS", "pricing tiers", "what should I charge", "outcome-based pricing", or "are my unit economics okay".
SKILL.md
2.4 KB, as published. Nobody here has run it
When to use
When a founder must pick a pricing model and tiers, or pressure-test whether the current pricing supports a healthy business.
Inputs to gather
- What value/outcome the product delivers and how it's measured.
- ICP and their budget reality (who signs, what they spend today).
- Rough cost-to-serve per customer (incl. model/API costs).
- Current or guessed CAC and expected retention.
Process
- Pick the model by value metric:
- Per-seat — value scales with users (collaboration tools).
- Usage — value scales with volume; aligns cost-to-serve.
- Outcome-based — charge per result delivered (per resolved ticket, per confirmed booking, per agent action). The 2026 shift; NRR 120-150% when it lands. See Intercom Fin (per-resolution), Zendesk (per automated resolution), Salesforce (per agent action).
- Hybrid — small platform fee + outcome/usage, to floor revenue and de-risk.
- Set 3 tiers (entry / pro / enterprise) anchored to value, with a clear reason to upgrade between each. Use price anchoring — make the middle tier the obvious pick.
- Sanity-check unit economics: LTV = ARPA × gross margin × avg lifetime. Target LTV:CAC ≥ 3:1 and CAC payback < 12 months. Flag if model costs crush margin.
- Stress-test churn: assume B2B 10-20%/yr, B2C 40-60%/yr; if B2C, push toward annual or higher-value use cases.
Output
- Recommended model + one-line rationale tied to the value metric.
- Tier table: name, price, who it's for, gating feature, upgrade trigger.
- Unit economics: LTV, CAC, LTV:CAC, payback months, with assumptions.
- One pricing experiment to run next (e.g. test outcome vs. seat on 5 deals).
Quality bar & pitfalls
- Good = price maps to a value metric the customer already counts in their head.
- Outcome-based requires you can attribute and measure the result — verify that first.
- Avoid: cost-plus pricing; too many tiers; undercharging "to get started" (anchors low forever); ignoring API/model cost in margin. For vertical AI, outcome + proprietary data is the durable combo.