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/**
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* Employee Behavior Patterns Simulation
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*
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* Generates realistic daily work schedules, productivity patterns, collaboration,
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* and communication behaviors for workforce modeling.
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*
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* PRIVACY NOTE: All data is synthetic. No real employee data is used or should
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* be used to train these models without explicit consent and proper anonymization.
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*/
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import { createSynth } from '../../src/index.js';
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/**
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* Generate daily work schedule patterns
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* Simulates diverse work hours including flexible schedules, remote work, etc.
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*/
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export async function generateWorkSchedules() {
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const synth = createSynth({
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provider: 'gemini',
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apiKey: process.env.GEMINI_API_KEY
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});
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const scheduleSchema = {
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employeeId: { type: 'string', required: true },
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date: { type: 'string', required: true },
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workMode: {
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type: 'string',
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required: true,
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enum: ['office', 'remote', 'hybrid']
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},
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checkIn: { type: 'string', required: true }, // ISO time
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checkOut: { type: 'string', required: true },
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breaks: {
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type: 'array',
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required: true,
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items: {
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type: 'object',
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properties: {
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start: { type: 'string' },
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duration: { type: 'number' } // minutes
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}
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}
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},
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overtimeMinutes: { type: 'number', required: false },
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timezone: { type: 'string', required: true }
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};
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const result = await synth.generateStructured({
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count: 500,
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schema: scheduleSchema,
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format: 'json',
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context: `Generate diverse work schedules representing:
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- Different time zones (US, Europe, Asia)
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- Various work modes (40% office, 30% remote, 30% hybrid)
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- Flexible start times (7am-10am)
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- Standard 8-hour days with realistic variations
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- Cultural diversity in break patterns
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- Occasional overtime (10% of records)`
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});
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return result;
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}
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/**
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* Generate productivity patterns throughout the day
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* Models realistic variations in focus and output
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*/
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export async function generateProductivityPatterns() {
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const synth = createSynth({
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provider: 'gemini'
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});
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const productivitySchema = {
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employeeId: { type: 'string', required: true },
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timestamp: { type: 'string', required: true },
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productivityScore: {
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type: 'number',
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required: true,
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min: 0,
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max: 100
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},
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focusLevel: {
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type: 'string',
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required: true,
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enum: ['deep_work', 'moderate', 'distracted', 'break']
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},
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tasksCompleted: { type: 'number', required: true },
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meetingsAttended: { type: 'number', required: true },
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codeCommits: { type: 'number', required: false },
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documentsEdited: { type: 'number', required: false },
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emailsProcessed: { type: 'number', required: false },
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energyLevel: {
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type: 'string',
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required: true,
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enum: ['high', 'medium', 'low']
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}
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};
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const result = await synth.generateTimeSeries({
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count: 1000,
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interval: '1h',
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metrics: ['productivityScore', 'focusLevel', 'energyLevel'],
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trend: 'cyclical', // Morning high, afternoon dip, late recovery pattern
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seasonality: true,
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context: `Model realistic productivity patterns:
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- Morning peak (9am-11am): 70-90% productivity
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- Post-lunch dip (1pm-3pm): 50-70% productivity
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- Afternoon recovery (3pm-5pm): 60-80% productivity
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- Individual variations based on chronotype
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- Friday effect (10-15% lower average)
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- Monday ramp-up period`
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});
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return result;
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}
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/**
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* Generate collaboration and communication patterns
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* Models team interactions, meeting participation, and communication frequency
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*/
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export async function generateCollaborationPatterns() {
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const synth = createSynth({
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provider: 'gemini'
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});
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const collaborationSchema = {
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employeeId: { type: 'string', required: true },
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date: { type: 'string', required: true },
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interactions: {
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type: 'object',
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required: true,
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properties: {
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slackMessages: { type: 'number' },
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emails: { type: 'number' },
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meetings: { type: 'number' },
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codeReviews: { type: 'number' },
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pairProgramming: { type: 'number' } // hours
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}
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},
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collaborators: {
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type: 'array',
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required: true,
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items: {
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type: 'object',
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properties: {
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employeeId: { type: 'string' },
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department: { type: 'string' },
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interactionCount: { type: 'number' },
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interactionType: { type: 'string' }
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}
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}
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},
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networkCentrality: {
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type: 'number',
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required: true,
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min: 0,
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max: 1
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},
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crossFunctionalScore: { type: 'number', required: true }
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};
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const result = await synth.generateStructured({
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count: 300,
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schema: collaborationSchema,
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format: 'json',
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context: `Generate realistic collaboration patterns:
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- Engineers: 60% internal team, 40% cross-functional
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- Managers: 80% cross-functional, 20% individual work
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- Designers: 70% collaboration, 30% individual work
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- Sales: 50% internal, 50% external
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- Network effects: 20% of employees are high connectors
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- Remote workers: 30% more async communication
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- Include diversity in communication styles`
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});
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return result;
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}
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/**
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* Generate meeting attendance and participation data
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* Models realistic meeting behaviors and engagement
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*/
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export async function generateMeetingBehavior() {
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const synth = createSynth({
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provider: 'gemini'
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});
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const meetingSchema = {
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meetingId: { type: 'string', required: true },
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employeeId: { type: 'string', required: true },
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meetingType: {
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type: 'string',
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required: true,
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enum: ['standup', 'planning', 'review', 'one-on-one', 'all-hands', 'brainstorm', 'training']
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},
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attended: { type: 'boolean', required: true },
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onTime: { type: 'boolean', required: true },
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duration: { type: 'number', required: true }, // minutes
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participationScore: {
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type: 'number',
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required: true,
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min: 0,
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max: 10
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},
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contributions: {
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type: 'object',
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required: true,
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properties: {
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questions: { type: 'number' },
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comments: { type: 'number' },
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actionItems: { type: 'number' }
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}
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},
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multitasking: { type: 'boolean', required: true },
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cameraOn: { type: 'boolean', required: true }
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};
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const result = await synth.generateEvents({
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count: 2000,
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eventTypes: ['standup', 'planning', 'review', 'one-on-one', 'all-hands', 'brainstorm', 'training'],
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distribution: 'normal',
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timeRange: {
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start: new Date(Date.now() - 30 * 24 * 60 * 60 * 1000), // 30 days
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end: new Date()
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},
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context: `Generate realistic meeting behaviors:
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- 85% attendance rate overall
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- 70% on-time arrival
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- Higher participation in smaller meetings
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- Standup: 15 mins, 60% participation
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- Planning: 60 mins, 80% participation
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- All-hands: 45 mins, 30% participation
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- Remote meeting: 60% camera on
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- 25% multitasking during meetings`
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});
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return result;
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}
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/**
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* Generate task completion rates and patterns
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* Models realistic work output and completion behaviors
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*/
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export async function generateTaskCompletion() {
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const synth = createSynth({
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provider: 'gemini'
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});
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const taskSchema = {
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taskId: { type: 'string', required: true },
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employeeId: { type: 'string', required: true },
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createdAt: { type: 'string', required: true },
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completedAt: { type: 'string', required: false },
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estimatedHours: { type: 'number', required: true },
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actualHours: { type: 'number', required: false },
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priority: {
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type: 'string',
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required: true,
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enum: ['critical', 'high', 'medium', 'low']
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},
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status: {
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type: 'string',
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required: true,
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enum: ['todo', 'in_progress', 'review', 'done', 'blocked']
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},
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complexity: {
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type: 'string',
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required: true,
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enum: ['simple', 'moderate', 'complex', 'very_complex']
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},
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blockedDays: { type: 'number', required: false },
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qualityScore: { type: 'number', required: false }
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};
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const result = await synth.generateStructured({
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count: 1000,
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schema: taskSchema,
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format: 'json',
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context: `Generate realistic task completion patterns:
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- 75% completion rate within sprint
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- 15% variance from estimates
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- Priority impact: Critical 95% done, Low 60% done
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- 10% of tasks get blocked (avg 2.5 days)
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- Complex tasks: 30% longer than estimate
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- Quality score: 75-95% range with normal distribution
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- Include edge cases: abandoned tasks, scope changes`
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});
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return result;
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}
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/**
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* Generate work-from-home vs office patterns
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* Models hybrid work preferences and patterns
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*/
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export async function generateWorkLocationPatterns() {
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const synth = createSynth({
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provider: 'gemini'
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});
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const locationSchema = {
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employeeId: { type: 'string', required: true },
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week: { type: 'string', required: true },
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schedule: {
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type: 'object',
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required: true,
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properties: {
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monday: { type: 'string', enum: ['office', 'remote', 'off'] },
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tuesday: { type: 'string', enum: ['office', 'remote', 'off'] },
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wednesday: { type: 'string', enum: ['office', 'remote', 'off'] },
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thursday: { type: 'string', enum: ['office', 'remote', 'off'] },
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friday: { type: 'string', enum: ['office', 'remote', 'off'] }
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}
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},
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officeCollaboration: { type: 'number', required: true },
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remoteProductivity: { type: 'number', required: true },
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commuteTime: { type: 'number', required: false }, // minutes
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workLifeBalance: {
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type: 'number',
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required: true,
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min: 1,
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max: 10
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}
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};
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const result = await synth.generateStructured({
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count: 200,
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schema: locationSchema,
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format: 'json',
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context: `Generate hybrid work patterns:
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- 30% fully remote
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- 20% fully office
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- 50% hybrid (2-3 days office)
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- Tuesday-Thursday most popular office days
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- Friday: 70% remote
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- Correlation: longer commute = more remote days
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- Remote workers report 15% higher productivity
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- Office workers report 20% more collaboration
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- Include regional differences`
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});
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return result;
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}
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/**
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* Run all workforce behavior examples
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*/
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export async function runAllBehaviorExamples() {
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console.log('=== Workforce Behavior Simulation Examples ===\n');
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console.log('1. Generating Work Schedules...');
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const schedules = await generateWorkSchedules();
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console.log(`Generated ${schedules.data.length} work schedule records`);
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console.log('Sample:', JSON.stringify(schedules.data[0], null, 2));
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console.log('\n2. Generating Productivity Patterns...');
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const productivity = await generateProductivityPatterns();
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console.log(`Generated ${productivity.data.length} productivity data points`);
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console.log('Sample:', JSON.stringify(productivity.data[0], null, 2));
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console.log('\n3. Generating Collaboration Patterns...');
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const collaboration = await generateCollaborationPatterns();
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console.log(`Generated ${collaboration.data.length} collaboration records`);
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console.log('Sample:', JSON.stringify(collaboration.data[0], null, 2));
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console.log('\n4. Generating Meeting Behavior...');
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const meetings = await generateMeetingBehavior();
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console.log(`Generated ${meetings.data.length} meeting attendance records`);
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console.log('Sample:', JSON.stringify(meetings.data[0], null, 2));
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console.log('\n5. Generating Task Completion...');
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const tasks = await generateTaskCompletion();
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console.log(`Generated ${tasks.data.length} task records`);
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console.log('Sample:', JSON.stringify(tasks.data[0], null, 2));
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console.log('\n6. Generating Work Location Patterns...');
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const locations = await generateWorkLocationPatterns();
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console.log(`Generated ${locations.data.length} work location records`);
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console.log('Sample:', JSON.stringify(locations.data[0], null, 2));
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}
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// Uncomment to run
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// runAllBehaviorExamples().catch(console.error);
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