Introduction
The systematic-debugging skill enforces a disciplined, evidence-based approach to fixing software issues. It solves the widespread problem of developers (and AI agents) guessing at solutions, throwing random patches at a problem, and ultimately masking the true underlying issue or creating new bugs.
Core Concept
“No fixes without root cause investigation first.” By mandating a four-phase diagnostic process, this skill ensures that every fix addresses the actual failure rather than just treating the symptom.
Installation and Usage Guide
https://github.com/obra/superpowers
Workflow Analysis (SOP)
- Read error messages carefully: Demands that the AI fully read stack traces, file paths, and error codes rather than skipping past them.
- Reproduce consistently: Instructs the AI to verify if the issue happens reliably and to gather more data instead of guessing if it doesn’t.
- Check recent changes: Forces a review of Git diffs, new dependencies, and configuration changes to identify what triggered the failure.
- Gather evidence in multi-component systems: Before proposing any fix, the AI must add diagnostic instrumentation (like logging data entering and exiting a component) to isolate exactly where the system breaks.
Skill Design Evaluation
- Strengths of SOP Integration: Enforces an “Iron Law” that strictly forbids proposing fixes without first investigating the root cause. Mandates a structured four-phase diagnostic approach, particularly emphasizing the addition of diagnostic instrumentation in multi-component systems.
- Potential Limitations: The absolute refusal to attempt quick patches might frustrate users who are facing trivial syntax errors or obvious typos where the root cause is already known.
- What Makes a Good Skill: It directly addresses the psychological traps of debugging (e.g., rushing under time pressure or guessing) by codifying a methodical, evidence-based approach that prioritizes long-term stability over quick fixes.