user-journey-optimization

user-journey-optimization is a cursor rule for Cursor from madebyaris/poinf-of-sales. It costs 17 tokens per session (4,883 once invoked), scanned A, original, MIT.

A set of user-journey design rules for point-of-sale software, covering roles such as administrators, servers, counter staff, and kitchen teams. A point-of-sale system handles orders, payments, and related restaurant operations.

In plain words
What is it for?
It is for planning and evaluating dashboards, order creation, product selection, table assignment, payments, receipts, queues, kitchen updates, and team communication.
Why use it?
It gives each role specific speed and workflow targets, helping teams design around the tasks people actually perform.

Cursor rule for Cursor

Written for Cursor: installed under .cursor/.

Good fit It is for planning and evaluating dashboards, order creation, product selection, table assignment, payments, receipts, queues, kitchen updates, and team communication.

Compare 6 cursor rules from other repositories ↓
Install with agentmods
npx agentmods add rules/madebyaris/poinf-of-sales/user-journey-optimization
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Clone the repo
git clone --depth 1 https://github.com/madebyaris/poinf-of-sales

Made for: Cursor.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for user-journey-optimization

README.md
[![agentmods](https://agentmods.dev/badge/rules/madebyaris/poinf-of-sales/user-journey-optimization/github.svg)](https://agentmods.dev/rules/madebyaris/poinf-of-sales/user-journey-optimization)
Your own site
<a href="https://agentmods.dev/rules/madebyaris/poinf-of-sales/user-journey-optimization"><img src="https://agentmods.dev/badge/rules/madebyaris/poinf-of-sales/user-journey-optimization/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for user-journey-optimization

Your own site · 80×15
<a href="https://agentmods.dev/rules/madebyaris/poinf-of-sales/user-journey-optimization"><img src="https://agentmods.dev/badge/rules/madebyaris/poinf-of-sales/user-journey-optimization.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 17 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 4,883 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00017 $0.04883
Opus 5 $0.00009 $0.02441
Sonnet 5 $0.00003 $0.00977
Haiku 4.5 $0.00002 $0.00488

Measured 13d ago against content hash cb9c2240def3, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

user-journey-optimization scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 13d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.cursor/rules/user-journey-optimization.mdc · 682 lines

How it starts

The opening of the file, as written. The whole thing — 682 lines — stays where its author put it; the contents beside it link to each section on GitHub.

👥 User Journey Optimization & Role-Specific Patterns

🎯 Journey-First Design Philosophy

Performance Targets by Role

interface RolePerformanceTargets {
  admin: {
    dashboardLoad: '< 2 seconds',
    reportGeneration: '< 5 seconds',
    userManagement: '< 1 second per action',
    systemOverview: '< 1.5 seconds'
  },
  server: {
    orderCreation: '< 30 seconds total',
    productSelection: '< 5 seconds per item',
    tableAssignment: '< 3 seconds',
    customerInteraction: 'seamless, no delays'
  },
  counter: {
    paymentProcessing: '< 10 seconds',
    orderTypeSwitch: '< 2 seconds',
    receiptGeneration: '< 3 seconds',
    queueManagement: 'real-time updates'
  },
  kitchen: {
    statusUpdates: '< 1 second',
    orderPrioritization: 'real-time',
    workflowOptimization: 'continuous',
    communicationDelay: '< 2 seconds'
  }
}

👑 Admin Journey Optimization

1. Executive Dashboard Experience

// ✅ ADMIN-OPTIMIZED: Executive dashboard with business intelligence
class AdminDashboardOptimization {
  // Intelligent data aggregation for C-level insights
  async loadExecutiveDashboard(): Promise<ExecutiveDashboard> {
    // Parallel data loading for instant insights
    const [
      realtimeMetrics,
      financialSummary,
      operationalHealth,
      staffPerformance,
      customerSatisfaction,
      systemAlerts
    ] = await Promise.all([
      this.getRealtimeBusinessMetrics(), // Revenue, orders/hour, avg ticket
      this.getFinancialSummary(), // Daily/weekly/monthly trends
      this.getOperationalHealth(), // Kitchen efficiency, table turnover
      this.getStaffPerformance(), // Individual and team metrics
      this.getCustomerSatisfaction(), // Wait times, order accuracy
      this.getSystemAlerts() // Technical and business alerts
    ])

    // Business intelligence: Automatic insights generation
    const insights = this.generateBusinessInsights({
      metrics: realtimeMetrics,
      trends: financialSummary,
      operations: operationalHealth
    })

    return {
      kpis: this.createKPIDashboard(realtimeMetrics),
      trends: this.createTrendAnalysis(financialSummary),
      alerts: this.prioritizeAlerts(systemAlerts),
      recommendations: insights.recommendations,
      quickActions: this.generateQuickActions(insights)
    }
  }

  // Predictive business insights
  private generateBusinessInsights(data: DashboardData): BusinessInsights {
    const insights: BusinessInsight[] = []

    // Revenue optimization insights
    if (data.metrics.averageTicket < data.historical.averageTicket * 0.95) {
      insights.push({
        type: 'revenue_optimization',
        severity: 'medium',
        title: 'Average Ticket Size Declining',
        description: 'Consider implementing upselling strategies or menu optimization',
        actionable: true,
        quickActions: [
          { label: 'View Menu Performance', action: 'navigate_to_menu_analytics' },
          { label: 'Staff Upselling Training', action: 'create_training_task' }
        ]
      })
    }

    // Operational efficiency insights
    if (data.operations.kitchenEfficiency < 0.85) {
      insights.push({
        type: 'operational_efficiency',
        severity: 'high',
        title: 'Kitchen Efficiency Below Target',
        description: 'Kitchen preparation times are impacting customer satisfaction',
        actionable: true,
        quickActions: [
          { label: 'View Kitchen Analytics', action: 'navigate_to_kitchen_dashboard' },
          { label: 'Optimize Kitchen Workflow', action: 'open_workflow_optimizer' }
        ]
      })
    }

    return {
      insights,
      recommendations: this.generateActionableRecommendations(insights),
      predictedImpact: this.calculatePredictedBusinessImpact(insights)
    }
  }
}

// Admin interface switching optimization
class AdminInterfaceSwitching {
  // Seamless role interface switching with context preservation
  async switchToRoleInterface(targetRole: UserRole, preserveContext: boolean = true): Promise<void> {
    // Pre-load target interface data
    const targetData = await this.preloadRoleData(targetRole)
    
    if (preserveContext) {
      // Preserve admin context for quick return
      this.preserveAdminContext({
        currentDashboard: this.getCurrentDashboardState(),
        activeReports: this.getActiveReports(),
        notifications: this.getPendingNotifications()
      })
    }

    // Optimized transition with loading states
    this.showTransitionLoading(`Switching to ${targetRole} interface...`)
    
    // Load role-specific optimizations
    const roleOptimizations = await this.loadRoleOptimizations(targetRole)
    
    // Smooth transition with preserved user experience
    this.transitionToRoleInterface(targetRole, targetData, roleOptimizations)
  }

  // Role-specific data preloading
  private async preloadRoleData(role: UserRole): Promise<RoleData> {
    const preloadStrategies = {
      server: () => Promise.all([
        this.menuService.getAvailableProducts(),
        this.tableService.getAvailableTables(),
        this.orderService.getActiveOrders()
      ]),
      counter: () => Promise.all([
        this.orderService.getPendingPayments(),
        this.paymentService.getPaymentMethods(),
        this.customerService.getLoyaltyPrograms()
      ]),
      kitchen: () => Promise.all([
        this.kitchenService.getActiveOrders(),
        this.kitchenService.getPreparationQueue(),
        this.kitchenService.getKitchenStations()
      ])
    }

    return preloadStrategies[role]?.() || Promise.resolve(null)
  }
}

Read the full file on GitHub · 682 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 13d ago First seen · 682 lines · 17 tokens per session scan A cb9c2240def3

Subscribe to this mod's changes

user-journey-optimization is a cursor rule published in the GitHub repository madebyaris/poinf-of-sales (142 stars, last pushed 1y ago), licensed MIT. It adds 17 tokens to every session and 4,883 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.