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From commercial-skills
Use when designing or revisiting product pricing — selecting a pricing model (subscription seat-based, usage-based, value-based, freemium, or hybrid), running Van Westendorp Price Sensitivity Meter analysis on WTP survey data, or designing Good/Better/Best packaging tiers. Recommends a model and a price range with trade-offs, never a single number. For Commercial leads, Product Marketing, and CMOs at the pricing-design moment — not deal-by-deal discounting, not brand positioning.
npx claudepluginhub marco3939/claude_skill --plugin commercial-skillsHow this skill is triggered — by the user, by Claude, or both
Slash command
/commercial-skills:pricing-strategistThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Help Commercial, Product Marketing, and CMO functions answer three questions at the pricing-design moment:
Guides technical evaluation of code review feedback: read fully, restate for understanding, verify against codebase, respond with reasoning or pushback before implementing.
Share bugs, ideas, or general feedback.
Help Commercial, Product Marketing, and CMO functions answer three questions at the pricing-design moment:
The skill recommends a model and a range. The human picks the number, owns the trade-offs, and runs the GTM.
Do not use for:
deal-deskc-level-advisor/cmo-advisorc-level-advisor/cro-advisorbusiness-growth/sales-engineerFill assets/pricing_brief_template.md (≈ 20 min). Capture: industry, deal size avg, customer count, value drivers, adoption curve, consumption pattern (seat / usage / value / hybrid), competitor models.
Run scripts/pricing_model_picker.py --input brief.json --profile saas --output markdown. Output ranks 5 models by fit-score 0-100 with trade-offs. Decision logic is deterministic: low usage variance + high seat-attach → subscription wins; power-law usage + variable customer value → usage-based wins.
If you have survey data (≥ 4 questions per respondent: too cheap / bargain / getting expensive / too expensive), run scripts/wtp_analyzer.py --input survey.json --output markdown. Output: 4 intersection points (OPP, IDP, PMC, PME) and the Range of Acceptable Prices.
PSM gives a range, not the price. See references/van_westendorp_methodology.md for common misinterpretations.
Run scripts/packaging_designer.py --input features.json --profile saas --output markdown. Output: 3-tier Good/Better/Best assignment with anti-pattern flags (decoy tier, feature dump, no upgrade trigger, Bronze loss leader, Enterprise no-anchor).
Take model + range + packaging into the pricing committee. Skill does not commit the number — you do.
scripts/pricing_model_picker.py — 5-model fit scorer (subscription / usage / value / freemium / hybrid)scripts/wtp_analyzer.py — Van Westendorp PSM implementationscripts/packaging_designer.py — Good/Better/Best tier designer with anti-pattern detectionAll scripts: stdlib only. --help and --sample work on all three.
references/saas_pricing_canon.md — Skok, Tunguz, Campbell, Ramanujam, BVP, Shevlin, Stanford GSBreferences/van_westendorp_methodology.md — original 1976 paper, NMS refinement, Conjoint.ly, Sawtooth, ESOMAR, Lipovetsky, Decision Analystreferences/packaging_anti_patterns.md — ProfitWell, OpenView, BVP vertical SaaS, Ramanujam, Poyar, SaaS Capitalpackaging_anti_patterns.md.Walked one at a time by /cs:grill-commercial or the orchestrator. Recommended answer + canon citation per question. Never bundled.
"Is your customer paying for outcomes, seats, or usage?" Recommended: outcomes (value-based) if you can measure them; usage if marginal cost is variable; seats only if usage is roughly flat per user. Canon: Ramanujam 2016 (Monetizing Innovation) — Mistake #1 of 9: seat-based pricing on a usage-variable product caps TAM at ~20% of WTP.
"Do you have a measurable value metric, or are you guessing?" Recommended: instrument the value metric BEFORE going to market with value-based pricing. Canon: Patrick Campbell / ProfitWell research — value-based without instrumentation collapses into bad usage-based pricing.
"What's the variance in customer usage across your top decile vs. median?" Recommended: variance > 10x → usage-based wins; variance < 3x → subscription wins; in between → hybrid with usage overage. Canon: Kyle Poyar (Growth Unhinged) — high-variance products lose 60%+ of revenue on flat-rate plans.
"What's your competitor's pricing model, and why are you choosing the same or different?" Recommended: surface the differentiation hypothesis explicitly. Identical pricing = identical value claim. Canon: David Skok (For Entrepreneurs) — pricing is a positioning signal.
"What sample size do you have for WTP analysis, and is it segmented?" Recommended: N≥30 per segment for PSM, N≥100 for conjoint. Canon: van Westendorp 1976 / Sawtooth Software methodology — sub-30 PSM is statistical noise.
"What's the ONE feature that forces a tier upgrade?" Recommended: every Better and Best tier needs a single non-negotiable upgrade trigger. Canon: Ramanujam (Monetizing Innovation) — Mistake #4: tiers with no clear differentiator make 70% of customers pick the cheapest.
Walk depth-first. Lock 1-3 before opening 4-6. After all 6 are answered, invoke pricing_model_picker.py → wtp_analyzer.py → packaging_designer.py in sequence.