When the user wants help with pricing decisions, packaging, or monetization strategy. Also use when the user mentions 'pricing,' 'pricing tiers,' 'freemium,' 'free trial,' 'packaging,' 'price increase,' 'value metric,' 'Van Westendorp,' 'willingness to pay,' 'monetization,' 'how much should I charge,' 'my pricing is wrong,' 'pricing page,' 'annual vs monthly,' 'per seat pricing,' 'should I offer a free plan,' 'pricing page teardown,' 'pricing page audit,' 'is my pricing page AI-readable,' or 'can AI read my pricing.' Use this whenever someone is figuring out what to charge, how to structure their plans, or wants to audit a pricing page (for humans and for the AI agents that shortlist tools). For in-app upgrade screens, see paywalls. For offer construction (bonuses, guarantees, value framing, naming) on services/courses/coaching/high-ticket B2B, see offers.
Instructions your experts follow when using this skill.
Packaging adaptation by AutoGPT, 2026-09-25. Original authorship and licence are retained. Changes are limited to the recorded name, metadata and local references; see ATTRIBUTION.md in this package.
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 .agents/product-marketing.md exists (or .claude/product-marketing.md, or the legacy product-marketing-context.md filename, in older setups), 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.
Don't anchor on the wrong things:
The frameworks below (value metrics, tiers, Van Westendorp) are for optimizing a price. On day one you don't have a price to optimize — you have a bet to place. The goal of your first price is learning, not precision. Pick a number, ship it, and let real buyers tell you if it's wrong.
When you have nothing to go on, start with the order of magnitude that matches who you serve:
Pick the bucket by who the customer is and how much value you deliver, then start near the round number. You can move within the bucket fast once you have signal.
Resist the urge to price ultra-low (e.g. $9/mo) to reduce friction. Ultra-low pricing:
Round-and-slightly-higher beats clever-and-cheap.
When early Intercom agonized over pricing, Jason Fried's advice was essentially: just charge $50 and see what happens. Stop modeling; get a real signal. If people pay without flinching, raise it. If nobody bites, you've learned something for the cost of a week, not a quarter.
For the eight ways to structure how you charge (flat, usage, tier, user, feature, credit, outcome, hybrid) and the value/price ratio: See .
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?"
The value metric picks the pricing model. Once you know what scales with value, choose how to charge on it — flat, usage, tier, user, feature, credit, outcome, or a hybrid. See .
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
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
Market signals:
Business signals:
Product signals:
A price change is a rollout, not a switch you flip. Sequence it to de-risk:
Expect — and accept — some churn. The customers most likely to leave over a justified increase are usually your least-profitable, highest-support, most price-sensitive accounts.
When someone wants to audit an existing pricing page for clarity, transparency, and AI-readability (not the pricing strategy itself, and not conversion-rate optimization — that's cro), run a teardown that scores it across two axes and returns prioritized fixes:
Fast check — the "paste test": give the pricing URL to a browsing-capable AI (Perplexity, ChatGPT with search, Claude with web) — or paste the rendered page text — and ask "what are the plans and prices?" A clean miss means agents fetching your page will struggle too (a heuristic, not proof every agent fails).
The AI-readiness fixes are usually high-impact, low-effort (put prices in text, add Offer schema). Hand implementation to schema (Product/Offer JSON-LD) and ai-seo (extractability, AI-bot access, llms.txt).
For the full 10-dimension rubric, scoring, and report template: See . (AI-agent-readiness lens adapted from Kyle Poyar / Growth Unhinged.)
Files installed alongside these instructions.
When the user wants to create or update their product marketing context document. Also use when the user mentions 'product context,' 'marketing context,' 'set up context,' 'positioning,' 'who is my target audience,' 'describe my product,' 'ICP,' 'ideal customer profile,' or wants to avoid repeating foundational information across marketing tasks. Use this at the start of any new project before using other marketing skills — it creates `.agents/product-marketing.md` that all other skills reference for product, audience, and positioning context.
When the user wants to research, profile, or analyze competitors from their URLs. Also use when the user mentions 'competitor profile,' 'competitor research,' 'competitor analysis,' 'profile this competitor,' 'analyze competitor,' 'competitive intelligence,' 'competitor deep dive,' 'who are my competitors,' 'competitor landscape,' 'competitor dossier,' 'competitive audit,' or 'research these competitors.' Input is a list of competitor URLs. Output is structured competitor profile markdown files. For creating comparison/alternative pages from profiles, see competitors. For sales-specific battle cards, see sales-enablement.
When the user wants to conduct, analyze, or synthesize customer research. Use when the user mentions "customer research," "ICP research," "talk to customers," "analyze transcripts," "customer interviews," "survey analysis," "support ticket analysis," "voice of customer," "VOC," "build personas," "customer personas," "jobs to be done," "JTBD," "what do customers say," "what are customers struggling with," "Reddit mining," "G2 reviews," "review mining," "digital watering holes," "community research," "forum research," "competitor reviews," "customer sentiment," "PMF survey," "product/market fit survey," "customer interview questions," "interview outreach," "Sales Safari," or "find out why customers churn/convert/buy." Use for analyzing existing research assets, mining online sources, AND running primary research (interviews and surveys). For writing copy informed by research, see copywriting. For acting on research to improve pages, see cro.