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debug-mode

Diagnose software errors by analyzing structured debug logs from Python and JavaScript projects.

Updated Feb 24, 2026
Search Tags:#debugging#python#javascript#diagnostics#instrumentation#root-cause#log-analysis
One-click install
npx skills add https://github.com/ms-moar/ccc-media --skill debug-mode-ms-moar
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: debug-mode
Source: https://github.com/ms-moar/ccc-media/tree/main/ailabspro/files/extracted/v32-antigravity-workflow/antigravity-workflow/.agent/skills/debug-mode
Command: npx skills add https://github.com/ms-moar/ccc-media --skill debug-mode-ms-moar

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill solves the problem of guessing at fixes for errors by providing a structured, evidence-driven debugging workflow that collects runtime data, analyzes execution traces, and pinpoints root causes so fixes can be applied with confidence.

Core Features & Use Cases

  • Hypothesis-driven diagnosis: Generate ranked, testable hypotheses about root causes instead of blind edits.
  • Automatic and manual instrumentation: Inject structured debug logging into Python and JavaScript code or follow language-specific logging patterns for targeted diagnostics.
  • Log analysis and anomaly detection: Parse debug logs, build execution traces, and surface unclosed functions, slow calls, error events, and null returns to accelerate root-cause identification.
  • Safe workflow and cleanup: Backup originals, restore after debugging, and recommend regression tests and cleanup steps.
  • Use Case: Troubleshoot an intermittent API failure in a Node.js service by instrumenting the suspected functions, reproducing the error, and running log analysis to confirm the failing code path.

Quick Start

Instrument the reported source file, reproduce the issue with the instrumented build, and paste the collected debug logs for analysis.

Frequently Asked Questions about debug-mode

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

FAQPage Schema
How do I debug intermittent Python or JavaScript failures using structured debug logs?▼

Debug intermittent failures by injecting structured debug logging into Python or JavaScript code, reproducing the issue, and analyzing the collected execution traces to pinpoint the root cause.

What is evidence-driven debugging and when do I need it for root-cause analysis?▼

Evidence-driven debugging replaces guessing with runtime data collection and structured log analysis to validate hypotheses and pinpoint root causes. Use it for unclear or hard-to-reproduce software errors during development and QA.

How do I find the root cause of a Node.js API failure without guessing at fixes?▼

Generate ranked, testable hypotheses about root causes, instrument the suspected functions in your Node.js service, reproduce the error, and run log analysis to confirm the failing code path.

Can I automatically inject diagnostic instrumentation into my source files?▼

Yes, you can automatically inject structured debug logging into Python and JavaScript code or follow language-specific logging patterns for targeted diagnostics to capture runtime evidence.

Does this debugging workflow support both Python and JavaScript projects?▼

Yes, the workflow applies to debugging scenarios across both Python and JavaScript projects, requiring consistent debug log formatting and automated log analysis to locate execution divergences.

How do I safely clean up instrumentation code after debugging is complete?▼

The safe workflow backs up original source files before instrumentation and restores them after debugging, concluding with recommendations for regression tests and cleanup steps.

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