tts-error-reporting

Name: tts-error-reporting
Availability: InStock
Author: StarrySerendipity

Route structured TTS worker errors through response_queue to frontend toasts.

Updated Mar 31, 2026

Search Tags:#frontend #error #reporting #queue #tts #multiprocessing #toast

Ready to run

npx skills add https://github.com/StarrySerendipity/N.E.K.O --skill tts-error-reporting

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: tts-error-reporting
Source: https://github.com/StarrySerendipity/N.E.K.O/tree/main/.agent/skills/tts-error-reporting
Command: npx skills add https://github.com/StarrySerendipity/N.E.K.O --skill tts-error-reporting

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

TTS workers running in separate processes can fail silently or crash, leaving users unaware of why a voice synthesis is not ready. This Skill defines a standard for propagating those errors from the worker to the main frontend so users receive a clear notification.

Core Features & Use Cases

Structured error propagation: workers send a well-formed error payload via a shared queue, enabling the frontend to translate failures into visible toasts.
Reliability across multiprocessing: ensures errors such as quota exhaustion, API issues, or connection failures are surfaced immediately instead of being swallowed.
Use Case: when a realtime TTS worker encounters a temporary quota limit, the UI displays an informative toast instead of stalling.

Quick Start

Update each TTS worker to push structured error messages to the main process via the response_queue using the error tuple.

Frequently Asked Questions about tts-error-reporting

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

FAQPage Schema

How do I show multiprocessing TTS worker errors as frontend toast notifications?▼

To show multiprocessing TTS worker errors as frontend toast notifications, push structured error payloads from the worker through a shared response_queue, then intercept them in a response handler to trigger the UI toast. This ensures failures like quota exhaustion are surfaced immediately instead of stalling.

Why does my multiprocessing TTS worker fail silently without notifying the frontend?▼

Multiprocessing TTS workers fail silently because separate processes cannot directly access the frontend UI. Without routing structured error payloads through a shared response_queue, failures like API issues or connection failures are swallowed and users remain unaware of why voice synthesis stalled.

What is the best way to propagate TTS quota exhaustion errors from a separate process?▼

The best way to propagate TTS quota exhaustion errors from a separate process is to define a standard error payload format and push it via a shared response_queue. A response handler intercepts these structured messages to trigger informative toast notifications on the frontend.

How do I format error messages from TTS workers for a response queue?▼

To format error messages from TTS workers for a response queue, push a well-formed error payload using a defined format like the __error__ tuple. This structured error propagation enables the frontend response handler to reliably translate multiprocessing worker failures into visible UI toasts.

Can I use this error reporting approach for realtime TTS API connection failures?▼

Yes, this error reporting approach works for realtime TTS API connection failures. It ensures connection issues encountered by multiprocessing TTS workers are immediately surfaced as informative frontend toast notifications rather than crashing the worker silently.

What limitations exist when routing TTS errors through a multiprocessing queue?▼

Routing TTS errors through a multiprocessing queue requires all separate TTS worker processes to strictly follow the defined error payload format. If workers do not push structured error messages using the standard tuple, the response handler cannot intercept them to trigger UI toasts.