From saas-pricing-engine
This skill should be used when the user says "research competitor pricing", "what are competitors charging", "pricing intelligence", "competitor scan", "market pricing analysis", "how should I price my SaaS", "what's the market rate for", "pricing benchmarks", "analyze competitor tiers", "scrape pricing pages", "willingness to pay research", "pricing survey design", or any request to gather competitive intelligence, benchmark pricing, or understand market positioning before setting prices. Also trigger when the user pastes a competitor URL and asks about their pricing, or when building a pricing strategy from scratch. This is the FIRST skill to use in any pricing workflow — research comes before modeling.
How this skill is triggered — by the user, by Claude, or both
Slash command
/saas-pricing-engine:pricing-researchThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Deep competitive intelligence and market research for SaaS and API gateway pricing. This skill produces the raw research package that feeds into pricing-modeler.
Deep competitive intelligence and market research for SaaS and API gateway pricing. This skill produces the raw research package that feeds into pricing-modeler.
Run this BEFORE pricing-modeler. Every pricing decision should start with evidence. This skill gathers that evidence from three source layers:
Before scraping anything, establish what you're pricing:
For STIGViewer specifically, consider:
Scrape and analyze pricing pages for direct competitors. For each competitor, capture:
Use Rube/Apify tools when available for automated scraping. Fall back to manual URL analysis.
Read references/competitor-matrix-template.md for the structured capture format.
Map pricing from adjacent tools the buyer already pays for. This establishes the buyer's existing spend envelope and price anchors.
For API gateway pricing specifically, research:
Read references/api-pricing-patterns.md for common API pricing models.
Extract pricing-relevant intelligence from public reviews:
What to extract:
When Supabase/analytics access is available, pull:
Design a WTP research approach using one or more frameworks:
Four questions to ask prospects/customers:
Plot the curves to find the acceptable price range and optimal price point.
Show a series of price points and ask "Would you buy at $X?" at each level. Build a demand curve.
If resources allow, design a conjoint study testing feature bundles at different price points to find the highest-value configuration.
For each framework, produce:
Read references/wtp-frameworks.md for detailed survey templates.
Compile everything into a structured deliverable:
# Pricing Research Package: [Product Name]
## Date: [date]
## 1. Market Overview
- Category definition and size
- Growth trajectory
- Key players and market share estimates
## 2. Competitor Pricing Matrix
[Structured table from Phase 2]
## 3. Value Metric Analysis
- What competitors charge per (and why)
- Recommended value metric candidates for your product
- Pros/cons of each metric
## 4. Price Range Intelligence
- Lowest price point in market
- Median price point
- Highest price point
- Your recommended positioning zone
## 5. Review & Sentiment Intelligence
- Key themes from review mining
- Price sensitivity signals
- Perceived value drivers
## 6. Internal Usage Patterns
[If data available]
## 7. WTP Research Design
- Recommended framework
- Survey instrument
- Target respondent profile
## 8. Strategic Recommendations
- Recommended pricing model
- Recommended value metric
- Preliminary price range
- Key risks and mitigations
Save the research package to pricing-research-[product]-[date].md.
When researching pricing for STIGViewer products, pay special attention to:
Provides UI/UX resources: 50+ styles, color palettes, font pairings, guidelines, charts for web/mobile across React, Next.js, Vue, Svelte, Tailwind, React Native, Flutter. Aids planning, building, reviewing interfaces.
Searches MemPalace before answering questions about past work, people, projects, or prior decisions. Returns verbatim stored content instead of guessing from model memory.
npx claudepluginhub moxywolfllc/moxywolf-plugins --plugin saas-pricing-engine