How does Root Cause Tracing differ from standard debugging?

Standard debugging often focuses on where an error occurs (the symptom), while Root Cause Tracing focuses on why the state became invalid in the first place, tracing the data backward to its origin point.

What are the stop conditions for a backward trace?

You have found the root cause when there is no earlier source to trace, the error was introduced at that specific point, and fixing it prevents the bug entirely without requiring workarounds.

Can this skill help with race conditions and concurrency issues?

Yes, it provides specific patterns for identifying race conditions by analyzing shared state design and state transition logic rather than just inspecting the site of the crash.

Does it integrate with other developer tools?

It is designed to work seamlessly with complementary skills like assumption-checkers and dependency-validators to provide a comprehensive diagnostic workflow.

Root Cause Tracing

Name: Root Cause Tracing
Author: a-ariff

bya-ariff

安全与测试

Traces software bugs backward from symptoms to their original source to prevent recurring issues and technical debt.

关于

Root Cause Tracing is a specialized Claude Code skill designed to systematically identify the origin of bugs by following data flows and state changes backward. Instead of applying superficial 'band-aid' fixes to symptoms, this skill enforces a rigorous methodology—the Backward Trace Method—to find logic errors, broken invariants, and incorrect assumptions at their point of creation. It helps developers eliminate technical debt by ensuring that fixes address the underlying 'disease' rather than just the visible symptoms, resulting in more stable and maintainable codebases.

主要功能

Root Identification Criteria: Clear markers for recognizing when the true source of an error is reached.
0 GitHub stars
Integrated Verification: A framework for explaining the causal chain and validating the final fix.
Backward Trace Methodology: A systematic workflow for navigating from symptoms to origin.
Anti-Pattern Detection: Identifies and prevents 'symptom patching' and 'guard stacking' behaviors.
Data Flow Analysis: Tools for mapping how incorrect values propagate through different functions and states.

使用场景

Investigating race conditions or invalid state transitions in asynchronous applications.
Debugging complex logic errors where the crash site is far from the actual data source.
Refactoring legacy codebases to remove accumulated technical debt and superficial patches.

关于

主要功能

Root Identification Criteria: Clear markers for recognizing when the true source of an error is reached.
0 GitHub stars
Integrated Verification: A framework for explaining the causal chain and validating the final fix.
Backward Trace Methodology: A systematic workflow for navigating from symptoms to origin.
Anti-Pattern Detection: Identifies and prevents 'symptom patching' and 'guard stacking' behaviors.
Data Flow Analysis: Tools for mapping how incorrect values propagate through different functions and states.

使用场景

Investigating race conditions or invalid state transitions in asynchronous applications.
Debugging complex logic errors where the crash site is far from the actual data source.
Refactoring legacy codebases to remove accumulated technical debt and superficial patches.