Skill: Activity Schedule Generator

Purpose

Generate randomized daily LinkedIn activity schedules per profile per tier that simulate human-like behavior while maintaining operational constraints.

What it does

•Accepts profile tier, date, health state
•Generates randomized session times, durations, and activity mix
•Respects tier-specific activity bounds and daily limits
•Applies health state constraints (RED = zero, YELLOW = capped apps)
•Returns structured daily schedule with timestamps and activity counts

Why it matters

LinkedIn detects robotic patterns. Activity that looks mechanical gets flagged and restricted. By randomizing:

•Login times within windows (morning/afternoon/evening)
•Session durations and activity mixes
•Inter-session gaps
•Weekend/weekday variation

We maintain account health and avoid restrictions.

How to use it

Single profile schedule

python

from activity_simulator.schedule_generator import generate_schedule

schedule = generate_schedule({
    "profile_id": "P-001",
    "tier": "A",
    "date": "2026-02-15",
    "health_state": "GREEN",
    "primary_timezone": "EST"
})

for session in schedule["schedule"]:
    print(f"Session {session['session']} at {session['login_time']}")
    for activity in session['activities']:
        print(f"  - {activity['activity']}: {activity.get('count', activity.get('duration'))}")

Batch portfolio schedule

python

from activity_simulator.schedule_generator import generate_batch_schedules

portfolio = [
    {"profile_id": "P-001", "tier": "A", "health_state": "GREEN", "primary_timezone": "EST"},
    {"profile_id": "P-002", "tier": "B", "health_state": "YELLOW", "primary_timezone": "CST"},
    {"profile_id": "P-003", "tier": "C", "health_state": "GREEN", "primary_timezone": "PST"}
]

schedules = generate_batch_schedules(portfolio, date="2026-02-15")

for schedule in schedules:
    print(f"{schedule['profile_id']}: {schedule['total_sessions']} sessions, {schedule['total_duration_minutes']} min")

CLI usage

bash

# Demo with sample profiles
python schedule_generator.py

# Single profile from JSON
python schedule_generator.py profile_schedule_input.json

# Batch from JSON array
python schedule_generator.py portfolio_schedules.json

Input schema

json

{
  "profile_id": "P-001",
  "tier": "A",
  "date": "2026-02-15",
  "health_state": "GREEN",
  "primary_timezone": "EST"
}

Field descriptions

•profile_id: Unique identifier
•tier: A (thoroughbred), B (growth), C (warming), D (dormant)
•date: YYYY-MM-DD for the schedule
•health_state: GREEN, YELLOW, ORANGE, RED
•primary_timezone: EST, CST, PST, etc. (affects login window times)

Output schema

json

{
  "profile_id": "P-001",
  "date": "2026-02-15",
  "tier": "A",
  "health_state": "GREEN",
  "schedule": [
    {
      "session": 1,
      "login_time": "2026-02-15T08:15:30Z",
      "duration_minutes": 22,
      "activities": [
        { "activity": "browse", "duration": 8, "count": 1 },
        { "activity": "reactions", "count": 4 },
        { "activity": "profile_views", "count": 3 },
        { "activity": "connection_requests", "count": 2 },
        { "activity": "applications", "count": 2 }
      ]
    },
    {
      "session": 2,
      "login_time": "2026-02-15T13:45:15Z",
      "duration_minutes": 18,
      "activities": [...]
    }
  ],
  "total_sessions": 2,
  "total_duration_minutes": 40,
  "activities_summary": {
    "browse": 1,
    "reactions": 7,
    "profile_views": 6,
    "connection_requests": 5,
    "applications": 2
  },
  "timestamp": "2026-02-15T12:00:00Z"
}

Tier specifications

Tier A: Thoroughbred (Daily, 15-30 min)

•Sessions: 2 per day
•Duration: 15-30 min per session
•
Activities: Diverse mix
- •Browse: 5-10 minutes per session
- •Reactions: 3-5 per session
- •Profile views: 3-5 per session
- •Comments: 0-1 per session
- •Connection requests: 2-5 per session
- •Applications: 1-3 per session
•Daily app target: 2-5
•Connection request limit: 20/day
•Use case: High-velocity applications, diverse engagement

Tier B: Growth (Daily/Every Other, 10-20 min)

•Sessions: 1-2 per day
•Duration: 10-20 min per session
•
Activities: Moderate mix
- •Browse: 5-7 minutes per session
- •Reactions: 2-3 per session
- •Profile views: 2-3 per session
- •Comments: None
- •Connection requests: 3-8 per session
- •Applications: 0-1 per session
•Daily app target: 0-2
•Connection request limit: 15/day
•Use case: Growing pipeline, connection building

Tier C: Warming (Every 2-3 Days, 5-10 min)

•Sessions: 1 per day (but skipped 40% of days)
•Duration: 5-10 min per session
•
Activities: Light mix
- •Browse: 3-5 minutes per session
- •Reactions: 1-2 per session
- •Profile views: 3-5 per session
- •Comments: None
- •Connection requests: 3-5 per session
- •Applications: None
•Daily app target: 0
•Connection request limit: 8/day
•Use case: Account warming, trust building

Tier D: Dormant (Zero Activity)

•Sessions: 0
•Activities: None
•Use case: Reserve capacity

Health state constraints

GREEN (85-100)

•Full activity as per tier specs
•Applications allowed at tier rate (Tier A 2-5, B 0-2, C 0)

YELLOW (60-84)

•Activity allowed but capped
•Max 2 applications per day (override tier limit)
•Session gaps increased
•Activity diversity maintained

ORANGE (30-59)

•Hydration only (browse, reactions, views)
•Zero applications
•Minimal connections
•Large session gaps

RED (0-29)

•Zero activity
•No output, profile is quarantined

Randomization

Login times

•Randomized within tier-defined windows
•Morning: 7:30-9:30 (Tier A), 8:00-10:00 (Tier B), 9:00-11:00 (Tier C)
•Afternoon: 12:00-14:30 (Tier A), 13:00-15:00 (Tier B)
•Evening: 16:00-18:30 (Tier A)
•Variance: ±15 minutes from base window

Session duration

•Random within tier bounds
•Variance: ±3 minutes

Activity counts

•Random within tier bounds for each activity type
•Order randomized

Inter-session gaps

•2-5 hours between sessions
•Randomized per day

Weekend behavior

•50% probability of any activity
•If activity occurs, volume reduced 40-50%

Human-like patterns

The generator applies patterns that make activity look human:

•Variable timing: No two logins at exact same time
•Activity order randomization: Activities occur in different sequences
•Occasional skips: 10% chance to skip planned activities
•Session extension: 5% chance to extend session if engaged
•Quick returns: 20% chance to log back in within 1 hour
•Weekend reduction: Weekends have 50% lower activity
•Vacation patterns: Can schedule 1-week quiet periods

Safety constraints

All schedules enforce hard limits:

•Max 6 applications per day (absolute LinkedIn limit)
•Max 20 connection requests per day
•Min 3 seconds between actions
•Max 2 consecutive sessions without 2-5 hour gap
•Required 2-5 hour gap between sessions

Performance

•Single profile: <50ms
•Batch of 100: <5s
•Memory efficient, no database calls

Integration points

•Activity Executor: Uses schedule to time actions
•Connection Manager: Uses connection request counts
•Application Executor: Uses application schedule
•Portfolio Manager: Plans daily activity mix

Example output interpretation

Given this output:

json

{
  "profile_id": "P-001",
  "tier": "A",
  "total_sessions": 2,
  "activities_summary": {
    "applications": 2,
    "connection_requests": 4,
    "reactions": 6,
    "profile_views": 5
  }
}

Instructions for executor:

•
Login at morning window time, session 1 (15-30 min)
- •Browse feed 5-10 min
- •React to 3-5 posts
- •View 3-5 profiles
- •Send 2-5 connection requests
- •Submit 1-3 applications
•Wait 2-5 hours
•
Login at afternoon window time, session 2
- •Similar activity mix based on remaining budget
•No evening session for Tier A this day (randomized)