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.
git clone --depth 1 https://github.com/madebyaris/poinf-of-salesWrote 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.
[](https://agentmods.dev/rules/madebyaris/poinf-of-sales/user-journey-optimization)<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.
<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>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.
| Model | Per session | Once 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 |
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.
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)
}
}
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.
- 13d ago First seen · 682 lines · 17 tokens per session scan A cb9c2240def3
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.
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