How does this differ from standard ad-hoc debugging?

Instead of random trial-and-error, this skill enforces a methodical process of forming hypotheses and gathering evidence before attempting a fix.

Does it keep a record of the troubleshooting session?

Yes, it creates organized documentation in your repository containing the problem context, hypotheses tested, evidence gathered, and the final resolution.

What happens if the root cause isn't found immediately?

The skill includes an automated 'Ask for Help' mechanism that triggers after several inconclusive hypotheses or a time threshold to avoid wasting resources.

Can I use this with other Claude Code plugins?

Absolutely. It is designed to work alongside feature-audit for runtime evidence and code-archaeologist for understanding unfamiliar legacy code during investigation.

Systematic Feature Troubleshooting

Name: Systematic Feature Troubleshooting
Author: schuettc

byschuettc

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安全与测试

Automates a methodical, hypothesis-driven debugging workflow to identify root causes and implement verified fixes for software bugs.

关于

The feature-troubleshoot skill transforms the often chaotic debugging process into a structured, 5-phase workflow consisting of problem definition, hypothesis formation, evidence-based investigation, resolution, and verification. Designed to prevent 'spinning wheels' during complex issues, it forces a transition from trial-and-error to evidence-based engineering. The skill includes built-in safeguards such as a 'Ask for Help' mechanism for when progress stalls and session-based storage to document every step of the investigation, ensuring that fixes are not just applied, but thoroughly understood and verified against regressions.

主要功能

Structured 5-phase workflow: Definition, Hypothesis, Investigation, Resolution, and Verification.
Session-based storage for documenting logs, evidence, and final resolutions.
Seamless integration with feature-audit for real-time evidence gathering.
Hypothesis-driven investigation that ranks potential bug causes by likelihood.
4 GitHub stars
Intelligent 'Ask for Help' mechanism to prevent unproductive time sinks.

使用场景

Resolving regressions by establishing clear expected vs. actual behavior benchmarks.
Debugging intermittent or complex errors that require systematic evidence collection.
Investigating vague 'broken' reports where the root cause is not immediately obvious.

关于

主要功能

Structured 5-phase workflow: Definition, Hypothesis, Investigation, Resolution, and Verification.
Session-based storage for documenting logs, evidence, and final resolutions.
Seamless integration with feature-audit for real-time evidence gathering.
Hypothesis-driven investigation that ranks potential bug causes by likelihood.
4 GitHub stars
Intelligent 'Ask for Help' mechanism to prevent unproductive time sinks.

使用场景

Resolving regressions by establishing clear expected vs. actual behavior benchmarks.
Debugging intermittent or complex errors that require systematic evidence collection.
Investigating vague 'broken' reports where the root cause is not immediately obvious.