Pricing Optimizer
Analyzes and optimizes pricing strategy using proven frameworks
About this skill
The "Pricing Optimizer" skill transforms an AI agent into a sophisticated pricing consultant. It guides the agent through a structured process to analyze a product or service's current pricing, market position, and cost structure. It then applies proven pricing frameworks like Value-Based Pricing, Competitive Positioning, and pricing psychology principles to identify optimization opportunities. This skill is ideal for businesses launching new products, re-evaluating existing pricing models, or seeking to improve profitability. It can help identify untapped revenue potential, refine target customer segments, and strategically position offerings against competitors. Users benefit by gaining data-driven pricing recommendations without needing a human consultant. It ensures a comprehensive approach, covering financial metrics, market dynamics, and customer psychology, leading to more effective and revenue-maximizing pricing strategies.
Best use case
The primary use case is for businesses, startups, and product managers who need to develop, refine, or optimize their product or service pricing strategy. It's particularly beneficial for those looking to maximize revenue, improve conversion rates, and better understand their market position, acting as a virtual pricing consultant.
Analyzes and optimizes pricing strategy using proven frameworks
Users should expect a detailed pricing analysis including recommended tiers, prices, target segments, and an estimated impact on revenue, conversion, and ARPU, along with implementation and A/B testing suggestions.
Practical example
Example input
Optimize the pricing for our new SaaS product, 'TaskFlow Pro'. We currently charge $29/month, targeting small businesses. Our main competitor charges $35/month with fewer features. COGS is about $5/user/month. We need to maximize subscriptions and ARPU.
Example output
## Pricing Analysis: TaskFlow Pro ### Current State - ARPU: $29 ### Recommended Pricing | Tier | Price | Target | Key Features | |------|--------|-------------|--------------| | Basic| $19/mo | Solopreneurs| Essential PM | | Pro | $47/mo | Small Teams | Advanced PM, AI-assist | ### Expected Impact - Revenue change: +15-25% (est.) - ARPU change: +$8-15 (est.) ### A/B Test Suggestions - Test $47 vs $49 for Pro tier.
When to use this skill
- Launching a new product or service.
- Revisiting an existing product's pricing to boost revenue.
- Understanding competitive pricing dynamics and market positioning.
- Identifying psychological triggers for better customer conversion.
When not to use this skill
- When detailed, real-time market data beyond what the user can provide is required.
- For highly regulated industries requiring specific legal pricing compliance.
- If the user has no pricing data or market understanding to input.
- For simple cost-plus pricing calculations where no optimization is needed.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/afrexai-pricing-optimizer/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How Pricing Optimizer Compares
| Feature / Agent | Pricing Optimizer | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | easy | N/A |
Frequently Asked Questions
What does this skill do?
Analyzes and optimizes pricing strategy using proven frameworks
How difficult is it to install?
The installation complexity is rated as easy. You can find the installation instructions above.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# Pricing Optimizer You optimize pricing strategy like a pricing consultant. Data-driven, psychology-informed, revenue-maximizing. ## Process ### 1. Discovery Ask about: - Current pricing (tiers, amounts, billing frequency) - Target customer (B2B/B2C, segment, budget range) - Competitors and their pricing - Current conversion rates and churn - Cost structure (COGS, CAC, margins) - Value metrics (what drives customer value?) ### 2. Analysis Frameworks **Value-Based Pricing:** - What's the customer's next best alternative? - What's the economic value your product creates? - Price should be between cost and value created **Competitive Positioning:** - Map competitors on price vs. feature matrix - Identify pricing gaps and opportunities - Determine if you're premium, mid-market, or budget **Psychology:** - Anchoring (show expensive tier first) - Charm pricing ($47 vs $50) - Decoy effect (3-tier with obvious "best value") - Annual discount (lock-in + cash flow) ### 3. Output ``` ## Pricing Analysis: [Product] ### Current State - Revenue: ... - Conversion: ... - ARPU: ... ### Recommended Pricing | Tier | Price | Target | Key Features | |------|-------|--------|-------------| | ... | ... | ... | ... | ### Expected Impact - Revenue change: +X% - Conversion change: ... - ARPU change: ... ### Implementation Plan 1. ... ### A/B Test Suggestions - ... ``` ## Rules - Always consider willingness-to-pay, not just cost-plus - Recommend A/B testing before full rollout - Consider annual vs monthly trade-offs - Flag if current pricing leaves money on the table ## Related Tools - Revenue calculator: https://afrexai-cto.github.io/ai-revenue-calculator/ - Lead scoring: `clawhub install afrexai-lead-scorer` - Industry context: https://afrexai-cto.github.io/context-packs/ ($47/pack)
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