Agent skill · product strategy · alirezarezvani
pricing-strategist
Use when choosing or revisiting pricing: seat, usage, value-based, freemium or hybrid, Van Westendorp willingness-to-pay analysis, or Good/Better/Best tiers. Runs pricing_model_picker.py, wtp_analyzer.py and packaging_designer.py and returns a range, not one number.
Why this skill is useful
Adds 3 executable scripts for pricing model selection, WTP analysis, and packaging design that aren't available in public resources.
What it needs
Requires python installed locally. About 5k tokens when loaded. Last updated 2026-08-05. 23,939 stars on the source repository.
What this skill does
pricing-strategist Purpose Help Commercial, Product Marketing, and CMO functions answer three questions at the pricing-design moment: 1. Which pricing model fits this product + customer + market? (subscription seat-based, usage-based, value-based, freemium, hybrid) 2. What does the customer actually pay before it feels too expensive? (Van Westendorp PSM on WTP survey responses) 3. How should we package this into tiers? (Good / Better / Best — with anti-pattern detection) The skill recommends a model and a range. The human picks the number, owns the trade-offs, and runs the GTM. When to use Launching a new SaaS / API / AI tool and choosing the first pricing model Revisiting pricing after 18+ months of GTM data (model shift, not just price increase) Designing or redesigning tier packaging (Good/Better/Best, Bronze/Silver/Gold) You have Van Westendorp survey data and want the optimal price range A board / exec is asking "what should we charge?" and you need the structured answer You suspect your packaging has anti-patterns (decoy tier, feature dump, no upgrade trigger) Do not use for: Per-deal discount approval → deal-desk Strategic CMO positioning, brand, category creation → c-level-advisor/cmo-advisor Whole-company revenue strategy → c-level-advisor/cro-advisor Technical-sale enablement → business-growth/sales-engineer Workflow Step 1 — Assess customer context Fill assets/pricingbrieftemplate.md (≈ 20 min). Capture: industry, deal size avg, customer count, value drivers, adoption curve, consumption pattern (seat / usage / value / hybrid), competitor models. Step 2 — Pick the pricing model Run scripts/pricingmodelpicker.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. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills alirezarezvani/pricing-strategist--95e35f