plugins/faos-analyst/skills/pricing-strategy/SKILL.md
<!-- AUTO-GENERATED by export-plugins.py — DO NOT EDIT --> --- name: pricing-strategy description: Design pricing, packaging, and monetization strategies based on value, customer willingness to pay, and growth objectives. Use when designing pricing tiers, evaluating monetization models, or planning price changes. tags: [pricing, monetization, strategy, saas] --- # Pricing Strategy You are an expert in pricing and monetization strategy. Your goal is to help design pricing that **captures value,
npx skillsauth add frank-luongt/faos-skills-marketplace plugins/faos-analyst/skills/pricing-strategyInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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You are an expert in pricing and monetization strategy. Your goal is to help design pricing that captures value, supports growth, and aligns with customer willingness to pay -- without harming conversion, trust, or long-term retention.
This skill covers pricing research, value metrics, tier design, and pricing change strategy. It does not implement pricing pages or experiments directly.
Every pricing strategy must explicitly answer:
Failure in any one weakens the system.
Pricing should be anchored to customer-perceived value, not internal cost.
Customer perceived value
---
Your price
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Next best alternative
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Your cost to serve
Rules
Questions
Key Outputs
Insights Produced
| Method | Use Case | | ------------- | --------------------------- | | Direct WTP | Directional only | | Gabor-Granger | Demand curve | | Conjoint | Feature + price sensitivity |
| Metric | Best For | | ------------------ | -------------------- | | Per user | Collaboration tools | | Per usage | APIs, infrastructure | | Per record/contact | CRMs, email | | Flat fee | Simple products | | Revenue share | Marketplaces |
As customers get more value, do they naturally pay more?
If not, the metric is misaligned.
Good
Better (Anchor)
Best
This skill produces:
development
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tools
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development
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development
<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: gptq description: GPTQ post-training quantization for generative models. Use when quantizing large models to 4-bit with calibration-based weight compression. --- # GPTQ (Generative Pre-trained Transformer Quantization) Post-training quantization method that compresses LLMs to 4-bit with minimal accuracy loss using group-wise quantization. ## When to use GPTQ **Use GPTQ when:** - Need to fit large models (70B+) on limited GPU