Skill: duclawbot/xmoney (version 1.0.0)

--- name: XMoney description: > Turn messy monetization ideas into clearer revenue decisions. Analyze pricing, payout logic, conversion friction, and revenue model fit for creators, operators, and digital businesses. version: 1.0.0 --- # XMoney > **Turn messy monetization ideas into clearer revenue decisions.** XMoney is a monetization and revenue-logic skill for creators, operators, and internet businesses. Here, **X** is the variable inside the monetization equation: the missing logic, friction, pricing gap, or offer weakness that prevents revenue from flowing cleanly. Use this skill when you need to: - improve monetization strategy - compare pricing models - simplify payout logic - diagnose conversion friction - design cleaner offers - turn audience, traffic, or product attention into a clearer revenue path This skill does NOT: - process payments - move real money - replace accounting, tax, or legal advice - provide regulated financial, banking, or compliance sign-off --- ## What This Skill Does XMoney helps: - analyze monetization models - compare revenue structures - identify conversion friction - clarify payout logic - detect value-price mismatch - improve monetization clarity before execution - turn vague money ideas into decision-ready monetization plans --- ## Best Use Cases - creator monetization strategy - pricing model redesign - payout structure analysis - subscription vs one-time comparison - offer engineering - revenue audit for digital businesses - creator economy monetization planning - platform earnings logic review --- ## What to Provide Useful input includes: - business model - audience type - product or offer - current monetization method - price points - conversion or payout pain points - platform constraints - revenue goal - known risks or tradeoffs If information is incomplete, this skill should identify what is missing before overconfident recommendations are made. --- ## Standard Output Format XMONEY ASSESSMENT ━━━━━━━━━━━━━━━━━━━━━━━━━━ Revenue Model: [Current / Proposed] Main Monetization Goal: [What money decision is being improved] CORE ISSUES ━━━━━━━━━━━━━━━━━━━━━━━━━━ Monetization Friction Score: [1-10] Value-Price Gap: [Overpriced / Underpriced / Balanced] - [Pricing issue] - [Payout issue] - [Conversion issue] - [Revenue logic issue] MODEL OPTIONS ━━━━━━━━━━━━━━━━━━━━━━━━━━ 1. [Option A] — [why it may fit] 2. [Option B] — [why it may fit] 3. [Option C] — [why it may fit] TRADEOFFS ━━━━━━━━━━━━━━━━━━━━━━━━━━ ⚠️ [Risk or downside] ⚠️ [Complexity or dependency] ⚠️ [Constraint or unknown] RECOMMENDED NEXT STEP ━━━━━━━━━━━━━━━━━━━━━━━━━━ - [What to test, simplify, or change next] --- ## Revenue Principles - price should match value logic - payout rules should be understandable - monetization should reduce friction, not add hidden complexity - revenue design should reflect audience behavior - unclear commercial structure slows growth - complexity is the tax on conversion - never confuse revenue potential with monetization fit --- ## Friction Review When analyzing monetization, check for: ### Pricing Friction - price feels arbitrary - value is unclear - anchor or benchmark is missing - offer tiers create confusion rather than clarity ### Offer Friction - buyer does not understand what they are paying for - too many options - weak packaging - weak differentiation between free and paid value ### Payout Friction - earnings logic is hard to understand - incentives are mismatched - creator or partner payout feels opaque - payout timing creates distrust ### Conversion Friction - too many steps before payment - audience intent does not match offer design - monetization path is unclear - monetization logic depends on unrealistic user behavior --- ## Pricing Anchor Check Before recommending or evaluating a price, ask: - Is there a useful reference point? - market benchmark - cost of alternative - internal build cost - competitor anchor - Is there a value-to-price ratio? - what result or leverage does the buyer get relative to price? - Is there a cost-of-inaction anchor? - what is lost by waiting, delaying, or keeping the current setup? Do not present price as a naked number if a clearer commercial frame is available. --- ## Execution Protocol (for AI agents) When user asks about monetization or payout logic, follow this sequence: ### Step 1: Parse business context Extract: - who is earning - what is being sold - how money currently flows - what constraints exist - what outcome the user wants ### Step 2: Identify monetization problem Classify the main issue: - weak pricing - weak conversion - weak monetization fit - payout complexity - incentive mismatch - unclear offer ladder - poor value communication ### Step 3: Score friction Estimate: - Monetization Friction Score (1-10) - Value-Price Gap (Overpriced / Underpriced / Balanced) Use these as judgment tools, not fake precision. ### Step 4: Compare options Evaluate realistic alternatives such as: - subscription - one-time purchase - commission - usage-based - tiered pricing - hybrid models ### Step 5: Show tradeoffs Explain: - upside - downside - complexity - operational friction - dependency risk ### Step 6: Recommend next move Return: - clearest monetization path - what to test next - what to simplify - what assumptions still need validation ### Step 7: Guardrails If tax, regulated payments, custody, or legal obligations are central: - say so clearly - do not fake certainty - recommend specialist review --- ## Activation Rules (for AI agents) ### Use this skill when the user asks about: - monetization strategy - pricing logic - payout structure - creator revenue - subscription models - revenue model fit - offer design - conversion logic - revenue audit - creator economy monetization ### Do NOT use this skill when: - user needs formal tax advice - user needs legal or compliance sign-off - user needs live payment execution - user wants banking, treasury, or regulated financial setup - user wants pure investment advice rather than monetization design ### If context is ambiguous Ask: "Do you want help with monetization strategy and pricing logic, or with accounting / legal / payment execution?" --- ## Works Well With - `@ethagent/content` for content monetization systems - `@ethagent/brand` for offer positioning - `@ethagent/launch` for monetization rollout planning --- ## Boundaries This skill supports monetization strategy, pricing logic, payout clarity, and revenue-model analysis. It does not replace: - accounting advice - tax advice - legal review - payment processor compliance - banking or treasury functions Use outputs as commercial analysis, not regulated sign-off.

path: README.md
content: # XMoney

> Turn messy monetization ideas into clearer revenue decisions.

XMoney is a monetization and revenue-logic skill for creators, operators, and internet businesses.

## What it does

This skill helps you:
- improve monetization strategy
- compare pricing options
- simplify payout logic
- diagnose conversion friction
- design cleaner revenue paths
- make monetization decisions easier to act on

## What it does NOT do

- process payments
- replace accounting or tax advice
- move real money
- provide regulated financial sign-off

## Works Well With

- `@ethagent/content` for content monetization
- `@ethagent/brand` for commercial positioning
- `@ethagent/launch` for offer rollout planning


path: examples.md
content: # XMoney — Examples

## Example 1: Creator monetization audit

User:
I have audience growth but weak revenue. Should I push subscriptions, courses, or premium community access?

Assistant:
Returns:
- monetization options
- friction score
- value-price gap
- fit by audience behavior
- recommended next test

## Example 2: Pricing redesign

User:
My $49 offer converts poorly. Help me figure out whether pricing, packaging, or payout structure is the issue.

Assistant:
Returns:
- likely monetization friction
- pricing alternatives
- anchor logic
- next-step recommendation

## Example 3: Platform payout logic

User:
We run a creator platform and need a cleaner payout structure for contributors. What model should we use?

Assistant:
Returns:
- payout model options
- incentive tradeoffs
- complexity warnings
- recommended structure


path: skill.json
content: {
  "name": "xmoney",
  "version": "1.0.0",
  "description": "Turn messy monetization ideas into clearer revenue decisions. Analyze pricing, payout logic, conversion friction, and revenue model fit.",
  "author": "ethagent",
  "license": "MIT-0",
  "homepage": "https://clawhub.ai",
  "keywords": [
    "xmoney",
    "monetization",
    "pricing",
    "payouts",
    "creator revenue",
    "subscriptions",
    "revenue model",
    "conversion logic",
    "revenue audit",
    "offer engineering",
    "creator economy"
  ],
  "files": {
    "skill.md": "skill.md"
  },
  "capabilities": {
    "monetization_analysis": "Analyze monetization and revenue models",
    "pricing_design": "Compare pricing structures and value logic",
    "payout_logic_review": "Clarify payout rules and incentives",
    "conversion_friction_review": "Identify where monetization complexity slows conversion",
    "revenue_strategy": "Recommend next-step monetization improvements"
  },
  "triggers": [
    "monetization strategy",
    "pricing model",
    "creator revenue",
    "payout logic",
    "subscription pricing",
    "revenue model",
    "how should I monetize",
    "conversion logic",
    "revenue audit",
    "offer design"
  ],
  "audience": [
    "Creators improving monetization",
    "Operators designing payouts or pricing",
    "Founders building online revenue models",
    "Digital businesses refining offer structure"
  ],
  "openclaw": {
    "primaryEnv": null,
    "requires": {}
  }
}


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)