How does Debug Mode differ from standard AI debugging?

Unlike standard debugging which often relies on guesswork, this skill follows a structured six-phase workflow including hypothesis ranking, strategic logging via a local server, and iterative verification.

How do I activate the debugging workflow?

The skill triggers automatically when you type 'debug mode', 'help me debug', or when you share error messages and stack traces that require deep analysis.

Will this skill leave console logs in my production code?

No. The workflow includes a mandatory Phase 6: Cleanup, where the skill uses grep to identify and remove all [DEBUG:] prefixed logs before the task is considered complete.

What programming languages are supported?

The skill includes specific patterns for JavaScript, TypeScript, and Python, but the systematic hypothesis and logging workflow can be applied to any programming language.

Autonomous Debugging Mode

Name: Autonomous Debugging Mode
Author: Remedy92

byRemedy92

분석 및 모니터링

Implements a systematic, hypothesis-driven debugging workflow for Claude Code that automates log injection and root-cause analysis.

소개

This skill brings advanced, systematic debugging capabilities to Claude Code by enforcing a rigorous six-phase diagnostic process inspired by modern IDE workflows. It guides the AI through generating multiple hypotheses, narrowing them down to the most likely causes, and injecting non-invasive strategic logs using a unique [DEBUG:] prefix. By utilizing an integrated local log server, it collects real-time data to bridge the gap between expected and actual behavior, ensuring that even complex race conditions or state-related bugs are identified and resolved before automatically cleaning up the codebase.

주요 기능

Strategic [DEBUG:] log injection for non-invasive tracing
Hypothesis-driven diagnostic framework with probability ranking
0 GitHub stars
Domain-specific patterns for async, state, and API troubleshooting
Automated identification and removal of debug artifacts
Integrated local log server for centralized data collection

사용 사례

Tracing complex data transformations across multiple service boundaries
Resolving intermittent race conditions in asynchronous application logic
Debugging persistent state mutations in large frontend or backend systems

소개

주요 기능

Strategic [DEBUG:] log injection for non-invasive tracing
Hypothesis-driven diagnostic framework with probability ranking
0 GitHub stars
Domain-specific patterns for async, state, and API troubleshooting
Automated identification and removal of debug artifacts
Integrated local log server for centralized data collection

사용 사례

Tracing complex data transformations across multiple service boundaries
Resolving intermittent race conditions in asynchronous application logic
Debugging persistent state mutations in large frontend or backend systems