How does this skill handle cleanup?

Once an investigation is closed, the skill is designed to identify and remove temporary investigation logs and instrumentation files to ensure the codebase remains clean.

Can this skill help with production issues?

Yes, it provides a structured plan for gathering evidence and analyzing existing signals like traces and config diffs when direct reproduction in production is not possible.

What tool does this skill use for instrumentation?

It prioritizes using debugsk, a local HTTP ingest logger, to capture signals during the investigation without contaminating the application's permanent native logs.

What is a hypothesis-driven debugging workflow?

It is a scientific approach that involves forming multiple competing theories about a bug, predicting what evidence would prove or disprove them, and then testing those predictions through controlled instrumentation.

Hypothesis-Driven Debugging

Name: Hypothesis-Driven Debugging
Author: abekdwight

byabekdwight

Analytics & Monitoring

Systematizes the debugging process using hypothesis-driven workflows and isolated instrumentation to resolve regressions, incidents, and flaky tests.

About

This skill equips Claude with a rigorous, scientific approach to software debugging, moving beyond guesswork toward evidence-based root cause analysis. It facilitates a standardized loop of clarifying bugs, generating competing hypotheses, planning instrumentation via the debugsk local logger, and executing controlled reproductions. By separating observed facts from inferences and utilizing binary-search narrowing techniques, it helps developers quickly isolate issues in local, staging, or production environments without contaminating native logs or infrastructure.

Key Features

Isolated instrumentation using local HTTP ingest logging via debugsk
Binary-search style narrowing of candidate code paths
Automated cleanup of investigation logs and temporary probes
Standardized hypothesis-driven investigation workflow
Strict separation of observed facts from speculative inferences
0 GitHub stars

Use Cases

Investigating intermittent or flaky test failures in CI/CD or local environments
Narrowing down complex concurrency issues or state-dependent bugs
Performing root cause analysis for production incidents and regressions

About

Key Features

Isolated instrumentation using local HTTP ingest logging via debugsk
Binary-search style narrowing of candidate code paths
Automated cleanup of investigation logs and temporary probes
Standardized hypothesis-driven investigation workflow
Strict separation of observed facts from speculative inferences
0 GitHub stars

Use Cases

Investigating intermittent or flaky test failures in CI/CD or local environments
Narrowing down complex concurrency issues or state-dependent bugs
Performing root cause analysis for production incidents and regressions