debug

Guide structured debugging of kokoro-coreml issues with documentation and API investigation.

57|18|Updated Jul 9, 2025

Search Tags:#debugging #systematic approach #bug resolution #parallel investigation #kokoro-coreml

Ready to run

npx skills add https://github.com/mattmireles/kokoro-coreml --skill debug-mattmireles

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: debug
Source: https://github.com/mattmireles/kokoro-coreml/tree/main/.claude/skills/debug
Command: npx skills add https://github.com/mattmireles/kokoro-coreml --skill debug-mattmireles

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires coremltools, PyTorch, Core ML runtime, Swift Core ML, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill offers a systematic approach to debugging kokoro-coreml issues, guiding users through a structured process to identify and resolve problems efficiently.

Core Features & Use Cases

Structured Debugging Workflow: Provides a step-by-step guide to debugging, including reading documentation, using Context7 MCP, parallel investigation, and finalizing notes.
Bug Investigation: Assists in identifying and fixing bugs related to coremltools, PyTorch, Core ML runtime, and Swift integration.
Exceptional Cases: Offers CLI-based multi-agent audits for complex issues that require escalation.

Quick Start

Run the debug skill and follow the provided workflow to investigate and resolve kokoro-coreml issues.

Frequently Asked Questions about debug

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

FAQPage Schema

How do I debug kokoro-coreml issues systematically?▼

To debug kokoro-coreml issues systematically, follow a structured workflow that includes reviewing documentation, investigating library APIs, setting up parallel investigations, and consolidating final notes to identify and resolve problems efficiently.

What is the best way to investigate Core ML runtime bugs during PyTorch integration?▼

The best way to investigate Core ML runtime bugs during PyTorch integration is to use a structured debugging workflow that reviews coremltools and Swift Core ML documentation, enabling targeted bug resolution across the integrated libraries.

Do I need coremltools and PyTorch to resolve kokoro-coreml bugs?▼

Yes, you need access to coremltools and PyTorch. Systematic bug resolution for kokoro-coreml requires these dependencies, alongside the Core ML runtime and Swift Core ML documentation, to provide necessary debugging context.

Can I use a multi-agent CLI audit for complex Core ML debugging?▼

Yes, you can use a CLI-based multi-agent audit for complex Core ML debugging. This approach is offered specifically for exceptional cases where standard structured debugging steps require further escalation.

Why does my kokoro-coreml debugging workflow require parallel task setup?▼

Parallel task setup is required in kokoro-coreml debugging to investigate multiple library APIs or potential bug sources simultaneously, streamlining the identification process before final note consolidation.