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On-Call Scheduler

$29

Build fair, sustainable on-call schedules with automation: rotation generation, coverage balancing, handoff checklists, and fatigue safeguards.

📁 9 files
MarkdownGrafana

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Try the interactive reader and demo tools below, or get the full product with all content unlocked.

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📁 File Structure 9 files

on-call-scheduler/ ├── LICENSE ├── README.md ├── free-sample.zip ├── guide/ │ ├── 01-on-call-design-principles.md │ ├── 02-rotation-algorithms-and-fairness.md │ ├── 03-escalation-policies-and-override-handlin.md │ ├── 04-pagerduty,-opsgenie,-and-grafana-integra.md │ └── 05-wellness,-burnout-prevention,-and-improv.md └── index.html

📖 Documentation Preview README excerpt

On-Call Scheduler

Fair, Sustainable On-Call Schedules with Automation

Toolkit for designing and managing fair on-call schedules.


What's Included

Guide Chapters

  • Chapter 1: On-Call Design Principles
  • Chapter 2: Rotation Algorithms and Fairness
  • Chapter 3: Escalation Policies and Override Handling
  • Chapter 4: PagerDuty, OpsGenie, and Grafana Integration
  • Chapter 5: Wellness, Burnout Prevention, and Improvement

Features

  • Rotation algorithms: primary-secondary, follow-the-sun
  • Fairness optimization with load balancing
  • Escalation policy designer with hierarchical rules
  • Override handling: swaps, PTO, emergency coverage
  • Integration scripts for PagerDuty, OpsGenie, Grafana
  • Shift handoff templates with context transfer
  • Wellness monitoring: load, sleep disruption, after-hours

Technical Stack

Python, PagerDuty API, OpsGenie, Slack


Quick Start

1. Start with the interactive reader (index.html) or browse the guide chapters directly

2. Chapter 1 provides a free preview -- explore the full product after purchase

3. Each chapter includes code examples, configuration snippets, and best practices

4. Use the search and theme toggle in the reader to customize your experience

Requirements

  • No specialized software required for reading the guides
  • Code examples include setup instructions within each chapter
  • Python 3.8+ recommended for running example scripts
  • A modern web browser for the interactive reader

File Structure


on-call-scheduler/
+-- README.md           # This file
+-- LICENSE             # MIT License
+-- index.html          # Interactive reader (free preview)
+-- free-sample.zip     # Free sample with Chapter 1
+-- guide/
    +-- 01-*.md through 05-*.md

... continues with setup instructions, usage examples, and more.

📄 Content Sample guide/01-on-call-design-principles.md

Chapter 1: On-Call Design Principles

Duration: 45-60 minutes | Difficulty: Intermediate | Prerequisites: Basic familiarity with Python, PagerDuty API, OpsGenie, Slack

Learning Objectives

By the end of this chapter, you will be able to:

1. Define the core architectural patterns and principles

2. Design a reference architecture aligned to business requirements

3. Identify the appropriate building blocks for each layer

4. Evaluate trade-offs between different design approaches

5. Create an implementation roadmap from architecture to production

6. Apply Python patterns to production scenarios


1. Understanding the Fundamentals

Before diving into implementation, it is essential to establish a solid conceptual foundation. On-Call Design Principles forms a critical pillar of the On-Call Scheduler framework, and getting the fundamentals right determines the success of everything built on top.

1.1 Core Concepts

The core concepts underlying this chapter are rooted in established industry patterns and best practices. Each concept builds on the previous one, creating a coherent framework for reasoning about complex systems.

ConceptDescriptionApplication
Foundation LayerThe core primitives and building blocksEstablish base capabilities for all higher-level patterns
Integration LayerInterfaces and connectors between componentsDefine clear boundaries and contracts between subsystems
Orchestration LayerCoordination and workflow managementManage multi-step processes with error handling
Observability LayerMonitoring, logging, and tracingProvide visibility into system behavior and performance
Governance LayerPolicies, controls, and complianceEnsure consistent operation within organizational guardrails

1.2 Why This Matters

In production environments, getting this wrong has measurable consequences. Teams that implement these patterns correctly experience:

  • Reduced incident frequency by 40-60% through proactive detection
  • Faster mean-time-to-resolution through structured procedures
  • Higher team confidence through documented and tested runbooks
  • Lower operational overhead through automation and standardization

2. Implementation Walkthrough

... and much more in the full download.

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