Pricing Optimizer
Analyzes and optimizes pricing strategy using proven frameworks
Packaged view
This page reorganizes the original catalog entry around fit, installability, and workflow context first. The original raw source lives below.
Install command
npx @skill-hub/cli install openclaw-skills-afrexai-pricing-optimizer
Repository
Skill path: skills/1kalin/afrexai-pricing-optimizer
Analyzes and optimizes pricing strategy using proven frameworks
Open repositoryBest for
Primary workflow: Ship Full Stack.
Technical facets: Full Stack.
Target audience: everyone.
License: Unknown.
Original source
Catalog source: SkillHub Club.
Repository owner: openclaw.
This is still a mirrored public skill entry. Review the repository before installing into production workflows.
What it helps with
- Install Pricing Optimizer into Claude Code, Codex CLI, Gemini CLI, or OpenCode workflows
- Review https://github.com/openclaw/skills before adding Pricing Optimizer to shared team environments
- Use Pricing Optimizer for development workflows
Works across
Favorites: 0.
Sub-skills: 0.
Aggregator: No.
Original source / Raw SKILL.md
---
name: Pricing Optimizer
description: Analyzes and optimizes pricing strategy using proven frameworks
---
# 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)
---
## Skill Companion Files
> Additional files collected from the skill directory layout.
### _meta.json
```json
{
"owner": "1kalin",
"slug": "afrexai-pricing-optimizer",
"displayName": "AfrexAI Pricing Optimizer",
"latest": {
"version": "1.0.0",
"publishedAt": 1770951434965,
"commit": "https://github.com/openclaw/skills/commit/2793f6d839c8c4dcbf0ff67e84ecf4aae59caba6"
},
"history": []
}
```