frontend-network-resilience

Implement exponential backoff retry and offline detection for frontend network calls.

Updated Nov 11, 2025

Search Tags:#frontend #resilience #offline #network #retry #backoff

Ready to run

npx skills add https://github.com/shredbx/demo-3d-model --skill frontend-network-resilience

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: frontend-network-resilience
Source: https://github.com/shredbx/demo-3d-model/tree/main/.claude/skills/frontend-network-resilience
Command: npx skills add https://github.com/shredbx/demo-3d-model --skill frontend-network-resilience

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Patterns for building robust frontend network interactions, including retry logic with exponential backoff, error differentiation, offline detection, and user feedback.

Core Features & Use Cases

Exponential Backoff Retry: Implement retry logic for API calls with backoff schedules.
Error Type Differentiation: Classify errors into offline, timeout, blocked, server_error, etc., with user messages.
Offline Detection: Detect offline state via navigator.onLine and handle accordingly.
Loading States: Provide progressive loading indicators and user feedback during delays.

Quick Start

Implement a retryWithBackoff function and apply it to a sample API call in a frontend workflow.

Frequently Asked Questions about frontend-network-resilience

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I implement retry logic with exponential backoff for API calls?▼

Exponential backoff retry increases wait time between failed requests using a formula like delay = baseDelay * (multiplier ^ attempt). Implement a retryWithBackoff function that catches errors, increments attempt counts, and reschedules requests with growing intervals—commonly used for transient failures like timeouts or rate limits.

How do I detect when a user is offline and handle it in my frontend?▼

Offline detection uses navigator.onLine to check connectivity status and listens to online/offline events. When offline is detected, queue requests, show appropriate user messaging, and resume automatically when connectivity returns, enabling graceful degradation without losing user data.

What's the best way to differentiate network errors and show appropriate user messages?▼

Classify errors into categories—offline, timeout, blocked, server_error—by examining response status, error type, and navigator state. Map each category to specific user feedback so users understand why an operation failed and what happens next, reducing frustration during network issues.

Can I use retry logic safely with non-idempotent API operations?▼

Non-idempotent operations (POST, DELETE without unique keys) risk duplicate effects if retried blindly. Handle this by tracking request IDs, implementing server-side deduplication, or using error-type differentiation to skip retry on success-with-error responses, ensuring safe automatic recovery.

How do I provide loading feedback while retries happen in the background?▼

Use progressive loading indicators—spinners, progress bars, status messages—that update as retry attempts occur and backoff delays elapse. Inform users of retry count and estimated wait time so they understand the system is working, not frozen.